More stories

  • in

    Bumble bees in landscapes with abundant floral resources have lower pathogen loads

    1.
    Potts, S. G. et al. Safeguarding pollinators and their values to human well-being. Nature 540, 220–229 (2016).
    ADS  CAS  PubMed  Article  Google Scholar 
    2.
    Potts, S. G. et al. Global pollinator declines: Trends, impacts and drivers. Trends Ecol. Evol. 25, 345–353 (2010).
    PubMed  Article  Google Scholar 

    3.
    Cameron, S. A. & Sadd, B. M. Global trends in bumble bee health. Annu. Rev. Entomol. 65, 209–232 (2020).
    CAS  PubMed  Article  Google Scholar 

    4.
    Goulson, D., Nicholls, E., Botías, C. & Rotheray, E. L. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 347, 1255957 (2015).
    PubMed  Article  CAS  Google Scholar 

    5.
    Steffan-Dewenter, I., Münzenberg, U., Bürger, C., Thies, C. & Tscharntke, T. Scale-dependent effects of landscape context on three pollinator guilds. Ecology 83, 1421–1432 (2002).
    Article  Google Scholar 

    6.
    Winfree, R., Aguilar, R., Vázquez, D. P., LeBuhn, G. & Aizen, M. A. A meta-analysis of bees’ responses to anthropogenic disturbance. Ecology 90, 2068–2076 (2009).
    PubMed  Article  Google Scholar 

    7.
    Grozinger, C. M. & Flenniken, M. L. Bee viruses: Ecology, pathogenicity, and impacts. Annu. Rev. Entomol. 64, 205–226 (2019).
    CAS  PubMed  Article  Google Scholar 

    8.
    Cameron, S. A. et al. Patterns of widespread decline in North American bumble bees. Proc. R. Soc. B Biol. Sci. 108, 662–667 (2011).
    CAS  Google Scholar 

    9.
    Tokarev, Y. S. et al. A formal redefinition of the genera Nosema and Vairimorpha (Microsporidia: Nosematidae) and reassignment of species based on molecular phylogenetics. J. Invertebr. Pathol. 169, 107279 (2020).
    CAS  PubMed  Article  Google Scholar 

    10.
    Levitt, A. L. et al. Cross-species transmission of honey bee viruses in associated arthropods. Virus Res. 176, 232–240 (2013).
    CAS  PubMed  Article  Google Scholar 

    11.
    Radzevičiūtė, R. et al. Replication of honey bee-associated RNA viruses across multiple bee species in apple orchards of Georgia, Germany and Kyrgyzstan. J. Invertebr. Pathol. 146, 14–23 (2017).
    PubMed  Article  CAS  Google Scholar 

    12.
    Fürst, M. A., McMahon, D. P., Osborne, J. L., Paxton, R. J. & Brown, M. J. F. Disease associations between honeybees and bumblebees as a threat to wild pollinators. Nature 506, 364–366 (2014).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    13.
    Dolezal, A. G. et al. Honey bee viruses in wild bees: Viral prevalence, loads, and experimental inoculation. PLoS ONE 11, 11 (2016).
    Google Scholar 

    14.
    Douglas, M. R., Sponsler, D. B., Lonsdorf, E. V. & Grozinger, C. M. County-level analysis reveals a rapidly shifting landscape of insecticide hazard to honey bees (Apis mellifera) on US farmland. Sci. Rep. 10, 1–11 (2020).
    Article  CAS  Google Scholar 

    15.
    Blacquiere, T., Smagghe, G., Van Gestel, C. A. & Mommaerts, V. Neonicotinoids in bees: A review on concentrations, side-effects and risk assessment. Ecotoxicology 21, 973–992 (2012).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    16.
    Aliouane, Y. et al. Subchronic exposure of honeybees to sublethal doses of pesticides: effects on behavior. Environ. Toxicol. Chem. 28, 113–122 (2009).
    CAS  PubMed  Article  Google Scholar 

    17.
    Whitehorn, P. R., O’connor, S., Wackers, F. L. & Goulson, D. Neonicotinoid pesticide reduces bumble bee colony growth and queen production. Science 336, 351–352 (2012).
    ADS  CAS  PubMed  Article  Google Scholar 

    18.
    Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines among bumble bees across continents. Science 367, 685–688 (2020).
    ADS  CAS  PubMed  Article  Google Scholar 

    19.
    Dolezal, A. G. & Toth, A. L. Feedbacks between nutrition and disease in honey bee health. Curr. Opin. Insect Sci. 26, 114–119 (2018).
    PubMed  Article  Google Scholar 

    20.
    DeGrandi-Hoffman, G. & Chen, Y. Nutrition, immunity and viral infections in honey bees. Curr. Opin. Insect Sci. 10, 170–176 (2015).
    PubMed  Article  Google Scholar 

    21.
    DeGrandi-Hoffman, G., Chen, Y., Huang, E. & Huang, M. H. The effect of diet on protein concentrcation, hypopharyngeal gland development and virus load in worker honey bees (Apis mellifera L.). J. Insect Physiol. 56, 1184–1191 (2010).
    CAS  PubMed  Article  Google Scholar 

    22.
    Di Pasquale, G. et al. Influence of pollen nutrition on honey bee health: Do pollen quality and diversity matter?. PLoS ONE 8, 8 (2013).
    Google Scholar 

    23.
    Manley, R., Boots, M. & Wilfert, L. Condition-dependent virulence of slow bee paralysis virus in Bombus terrestris: Are the impacts of honeybee viruses in wild pollinators underestimated?. Oecologia 184, 305–315 (2017).
    ADS  PubMed  PubMed Central  Article  Google Scholar 

    24.
    Ricigliano, V. A. et al. Honey bee colony performance and health are enhanced by apiary proximity to US Conservation Reserve Program (CRP) lands. Sci. Rep. 9, 1–11 (2019).
    CAS  Article  Google Scholar 

    25.
    O’Neal, S. T., Anderson, T. D. & Wu-Smart, J. Y. Interactions between pesticides and pathogen susceptibility in honey bees. Curr. Opin. Insect Sci. 26, 57–62 (2018).
    PubMed  Article  Google Scholar 

    26.
    Di Prisco, G. V. et al. Neonicotinoid clothianidin adversely affects insect immunity and promotes replication of a viral pathogen in honey bees. Proc. Natl. Acad. Sci. 110, 18466–18471 (2013).
    ADS  PubMed  Article  CAS  Google Scholar 

    27.
    O’Neal, S. T., Swale, D. R. & Anderson, T. D. ATP-sensitive inwardly rectifying potassium channel regulation of viral infections in honey bees. Sci. Rep. 7, 8668 (2017).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    28.
    Fine, J. D., Cox-Foster, D. L. & Mullin, C. A. An inert pesticide adjuvant synergizes viral pathogenicity and mortality in honey bee larvae. Sci. Rep. 7, 40499 (2017).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    29.
    Pettis, J. S., Johnson, J. & Dively, G. Pesticide exposure in honey bees results in increased levels of the gut pathogen Nosema. Naturwissenschaften 99, 153–158 (2012).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    30.
    Pettis, J. S., Lichtenberg, E. M., Andree, M., Stitzinger, J. & Rose, R. Crop pollination exposes honey bees to pesticides which alters their susceptibility to the gut pathogen Nosema ceranae. PLoS ONE 8, e70182 (2013).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    31.
    McArt, S. H., Fersch, A. A., Milano, N. J., Truitt, L. L. & Böröczky, K. High pesticide risk to honey bees despite low focal crop pollen collection during pollination of a mass blooming crop. Sci. Rep. 7, 46554 (2017).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    32.
    McArt, S. H., Koch, H., Irwin, R. E. & Adler, L. S. Arranging the bouquet of disease: Floral traits and the transmission of plant and animal pathogens. Ecol. Lett. 17, 624–636 (2014).
    PubMed  Article  Google Scholar 

    33.
    Piot, N. et al. Establishment of wildflower fields in poor quality landscapes enhances micro-parasite prevalence in wild bumble bees. Oecologia 189, 149–158 (2019).
    ADS  PubMed  Article  Google Scholar 

    34.
    Bailes, E. J. et al. Host density drives viral, but not trypanosome, transmission in a key pollinator. Proc. R. Soc. B Biol. Sci. 287, 20191969 (2020).
    Article  Google Scholar 

    35.
    Singh, R. et al. RNA viruses in hymenopteran pollinators: Evidence of inter-taxa virus transmission via pollen and potential impact on non-Apis hymenopteran species. PLoS ONE 5, e14357 (2010).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    36.
    Manley, R., Boots, M. & Wilfert, L. Emerging viral disease risk to pollinating insects: Ecological, evolutionary and anthropogenic factors. J. Appl. Ecol. 52, 331–340 (2015).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    37.
    Meeus, I., Pisman, M., Smagghe, G. & Piot, N. Interaction effects of different drivers of wild bee decline and their influence on host–pathogen dynamics. Curr. Opin. Insect Sci. 26, 136–141 (2018).
    PubMed  Article  Google Scholar 

    38.
    Huang, Z. Pollen nutrition affects honey bee stress resistance. Terr. Arthropod. Rev. 5, 175–189 (2012).
    Article  Google Scholar 

    39.
    Smart, M., Pettis, J., Rice, N., Browning, Z. & Spivak, M. Linking measures of colony and individual honey bee health to survival among apiaries exposed to varying agricultural land use. PLoS ONE 11, 3 (2016).
    Google Scholar 

    40.
    Danihlík, J., Aronstein, K. & Petřivalský, M. Antimicrobial peptides: a key component of honey bee innate immunity: Physiology, biochemistry, and chemical ecology. J. Apic. Res. 54, 123–136 (2015).
    Article  Google Scholar 

    41.
    Meeus, I., Brown, M. J., De Graaf, D. C. & Smagghe, G. U. Y. Effects of invasive parasites on bumble bee declines. Conserv. Biol. 25, 662–671 (2011).
    PubMed  Article  Google Scholar 

    42.
    Vaudo, A. D., Tooker, J. F., Grozinger, C. M. & Patch, H. M. Bee nutrition and floral resource restoration. Curr. Opin. Insect Sci. 10, 133–141 (2015).
    PubMed  Article  Google Scholar 

    43.
    Sánchez-Bayo, F. et al. Are bee diseases linked to pesticides?—A brief review. Environ. Int. 89, 7–11 (2016).
    PubMed  Article  CAS  Google Scholar 

    44.
    Beck, M. A. & Levander, O. A. Host nutritional status and its effect on a viral pathogen. J. Infect. Dis. 182, 93–96 (2000).
    Article  Google Scholar 

    45.
    Hing, S., Narayan, E. J., Thompson, R. A. & Godfrey, S. S. The relationship between physiological stress and wildlife disease: Consequences for health and conservation. Wildl. Res. 43, 51–60 (2016).
    Article  Google Scholar 

    46.
    Graystock, P., Goulson, D. & Hughes, W. O. Parasites in bloom: Flowers aid dispersal and transmission of pollinator parasites within and between bee species. Proc. R. Soc. B Biol. Sci. 282, 20151371 (2015).
    Article  Google Scholar 

    47.
    Sponsler, D. B., Shump, D., Richardson, R. T. & Grozinger, C. M. Characterizing the floral resources of a North American metropolis using a honey bee foraging assay. Ecosphere 11, e03102 (2020).
    Article  Google Scholar 

    48.
    Williams, N. M., Regetz, J. & Kremen, C. Landscape-scale resources promote colony growth but not reproductive performance of bumble bees. Ecology 93, 1049–1058 (2012).
    PubMed  Article  Google Scholar 

    49.
    Steffan-Dewenter, I. & Tscharntke, T. Resource overlap and possible competition between honey bees and wild bees in central Europe. Oecologia 122, 288–296 (2000).
    ADS  CAS  PubMed  Article  Google Scholar 

    50.
    Tehel, A., Brown, M. J. & Paxton, R. J. Impact of managed honey bee viruses on wild bees. Curr. Opin. Virol. 19, 16–22 (2016).
    PubMed  Article  Google Scholar 

    51.
    Sponsler, D. B. et al. Pesticides and pollinators: A socioecological synthesis. Sci. Total Environ. 662, 1012–1027 (2019).
    ADS  CAS  PubMed  Article  Google Scholar 

    52.
    McCaskill, G. L. et al. Pennsylvania’s Forests 2009 (U.S Forest Service, Washington, DC, 2009).
    Google Scholar 

    53.
    Park, M. G., Blitzer, E. J., Gibbs, J., Losey, J. E. & Danforth, B. N. Negative effects of pesticides on wild bee communities can be buffered by landscape context. Proc. R. Soc. B Biol. Sci. 282, 20150299 (2015).
    Article  CAS  Google Scholar 

    54.
    Koh, I. et al. Modeling the status, trends, and impacts of wild bee abundance in the United States. Proc. R. Soc. B Biol. Sci. 113, 140–145 (2016).
    CAS  Google Scholar 

    55.
    Williams, P. H., Thorp, R. W., Richardson, L. L. & Colla, S. R. Bumble Bees of North America: An Identification Guide (Princeton University Press, Princeton, 2014).
    Google Scholar 

    56.
    National Research Council. Under the Weather: Climate, Ecosystems, and Infectious Disease (National Academy Press, Washington, DC, 2001).
    Google Scholar 

    57.
    Polgreen, P. M. & Polgreen, E. L. Infectious diseases, weather, and climate. Clin. Infect. Dis. 66, 815–817 (2018).
    PubMed  Article  Google Scholar 

    58.
    Retschnig, G., Williams, G. R., Schneeberger, A. & Neumann, P. Cold ambient temperature promotes Nosema spp. intensity in honey bees (Apis mellifera). Insects 8, 20 (2017).
    PubMed Central  Article  PubMed  Google Scholar 

    59.
    Dalmon, A., Peruzzi, M. L., Conte, Y., Alaux, C. & Pioz, M. Temperature-driven changes in viral loads in the honey bee Apis mellifera. J. Invertebr. Pathol. 160, 87–94 (2019).
    PubMed  Article  Google Scholar 

    60.
    Gardner, W. A., Sutton, R. M. & Noblet, R. Persistence of Beauveria bassiana, Nomuraea rileyi, and Nosema necatrix on Soyhean Foliage. Environ. Entomol. 6, 616–618 (1977).
    Article  Google Scholar 

    61.
    Neidel, V., Steyer, C. S. & C., & Hoch, G. ,. Simulation of rain enhances horizontal transmission of the microsporidium Nosema lymantriae via infective feces. J. Invertebr. Pathol. 149, 56–58 (2017).
    PubMed  Article  Google Scholar 

    62.
    Rangel, J. et al. Prevalence of Nosema species in a feral honey bee population: A 20-year survey. Apidologie 47, 561–571 (2017).
    Article  Google Scholar 

    63.
    Leather, S. R. “Ecological Armageddon”-more evidence for the drastic decline in insect numbers. Ann. Appl. Biol. 172, 1–3 (2017).
    Article  Google Scholar 

    64.
    Scheper, J. et al. Local and landscape-level floral resources explain effects of wildflower strips on wild bees across four European countries. J. Appl. Ecol. 52, 1165–1175 (2015).
    Article  Google Scholar 

    65.
    Rodríguez, J. P., Brotons, L., Bustamante, J. & Seoane, J. The application of predictive modelling of species distribution to biodiversity conservation. Divers. Distrib. 13, 243–251 (2017).
    Article  Google Scholar 

    66.
    Young, B. E. et al. Using citizen science data to support conservation in environmental regulatory contexts. Biol. Conserv. 237, 57–62 (2019).
    Article  Google Scholar 

    67.
    Lesley, J. P. A Summary Description of the Geology of Pennsylvania (Board of Commissioners for the Geological Survey, Pennsylvania, 1892).
    Google Scholar 

    68.
    Dyer, J. Revisiting the Deciduous Forests of Eastern North America. Bioscience 56, 341–352 (2006).
    Article  Google Scholar 

    69.
    Wherry, E. T., Fogg, Jr., J. M., & Wahl. H. A. Atlas of the Flora of Pennsylvania. (University of Pennsylvania, Pennsylvania, 1979).

    70.
    Albright, T. A. Forests of Pennsylvania, 2017. Resource Update FS-175. (U.S. Department of Agriculture, Forest Service, 2017).

    71.
    Wickham, J. et al. The multi-resolution land characteristics (MRLC) consortium—20 years of development and integration. Remote Sens. 6, 7424–7441 (2014).
    ADS  Article  Google Scholar 

    72.
    Shannon, C. E. A mathematical theory of communication. Bell Labs Tech. J. 27, 379–423 (1948).
    MathSciNet  MATH  Article  Google Scholar 

    73.
    Plischuk, S. et al. South American native bumblebees (Hymenoptera: Apidae) infected by Nosema ceranae (Microsporidia), an emerging pathogen of honeybees (Apis mellifera). Environ. Microbiol. Rep. 1, 131–135 (2009).
    PubMed  Article  Google Scholar 

    74.
    Chu, C. C. & Cameron, S. A. A scientific note on Nosema bombi infection intensity among different castes within a Bombus auricomus nest. Apidologie 48, 141–143 (2017).
    Article  Google Scholar 

    75.
    vanEngelsdorp, D. et al. Colony collapse disorder: A descriptive study. PLoS ONE 4, e6481–e6481 (2009).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    76.
    Simmons, W. R. & Angelini, D. R. Chronic exposure to a neonicotinoid increases expression of antimicrobial peptide genes in the bumblebee Bombus impatiens. Sci. Rep. 7, 44773 (2017).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    77.
    Muller, C. B. & Schmid-Hempel, P. Variation in life-history pattern in relation to worker mortality in the bumble-bee, Bombus lucorum. Funct. Ecol. 6, 48–56 (1992).
    Article  Google Scholar 

    78.
    Hijmans, R. J. & van Etten, J. Raster: Geographic analysis and modeling with raster data. R package version 2.0-12. http://CRAN.R-project.org/package=raster (2012).

    79.
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.r-project.org/index.html (2019).

    80.
    Knight, M. E. et al. Bumblebee nest density and the scale of available forage in arable landscapes. Insect Conserv. Diver. 2, 116–124 (2009).
    Article  Google Scholar 

    81.
    Darvill, B., Knight, M. E. & Goulson, D. Use of genetic markers to quantify bumblebee foraging range and nest density. Oikos 107, 471–478 (2004).
    Article  Google Scholar 

    82.
    Desjardins, È. C. & De Oliveira, D. Commercial bumble bee Bombus impatiens (Hymenoptera: Apidae) as a pollinator in lowbush blueberry (Ericale: Ericaceae) fields. J. Econ. Entomol. 99, 443–449 (2006).
    PubMed  Article  Google Scholar 

    83.
    Natural Capital Project. InVEST: Crop Pollination Model. Version 3.1.0. http://naturalcapitalproject.org/models/crop_pollination.html (2014).

    84.
    Kammerer, M. A., Biddinger, D. J., Joshi, N. K., Rajotte, E. G. & Mortensen, D. A. Modeling local spatial patterns of wild bee diversity in Pennsylvania apple orchards. Landsc. Ecol. 31, 2459–2469 (2016).
    Article  Google Scholar 

    85.
    Johnson, D. M. & Mueller, R. The 2009 cropland data layer. Photogramm. Eng. Remote. Sens. 76, 1201–1205 (2010).
    Google Scholar 

    86.
    PRISM Climate Group. PRISM Gridded Climate Data. Oregon State University, Corvallis Oregon, USA. http://prism.oregonstate.edu (2019).

    87.
    Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference—A Practical Information-Theoretic Approach (Springer, New York, 2002).
    Google Scholar 

    88.
    Bates, D., Mächler, M., Bolker, B. M. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
    Article  Google Scholar 

    89.
    Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, New York, 2009).
    Google Scholar 

    90.
    Sokal, R. R. & Rohlf, F. J. The Principles and Practice of Statistics in Biological Research (W.H Freeman and Company, New York, 1969).
    Google Scholar  More

  • in

    Interacting effects of insect and ungulate herbivory on Scots pine growth

    1.
    Moreira, X. et al. Specificity of induced defenses, growth, and reproduction in lima bean (Phaseolus lunatus) in response to multispecies herbivory. Am. J. Bot. 102, 1300–1308 (2015).
    CAS  PubMed  Article  Google Scholar 
    2.
    Danell, K., Bergström, R. & Edenius, L. Effects of large mammalian browsers on architecture, biomass, and nutrients of woody plants. J. Mammal. 75, 833–844 (1994).
    Article  Google Scholar 

    3.
    Kaitaniemi, P., Neuvonen, S. & Nyyssönen, T. Effects of cumulative defoliations on growth, reproduction, and insect resistance in mountain birch. Ecology 80, 524–532 (1999).
    Article  Google Scholar 

    4.
    den Herder, M., Bergström, R., Niemelä, P., Danell, K. & Lindgren, M. Effects of natural winter browsing and simulated summer browsing by moose on growth and shoot biomass of birch and its associated invertebrate fauna. Ann. Zool. Fennici 46, 63–74 (2009).
    Article  Google Scholar 

    5.
    Wallgren, M., Bergquist, J., Bergström, R. & Eriksson, S. Effects of timing, duration, and intensity of simulated browsing on Scots pine growth and stem quality. Scand. J. For. Res. 29, 734–746 (2014).
    Article  Google Scholar 

    6.
    Schwenk, W. S. & Strong, A. M. Contrasting patterns and combined effects of moose and insect herbivory on striped maple (Acer pensylvanicum). Basic Appl. Ecol. 12, 64–71 (2011).
    Article  Google Scholar 

    7.
    Muiruri, E. W., Milligan, H. T., Morath, S. & Koricheva, J. Moose browsing alters tree diversity effects on birch growth and insect herbivory. Funct. Ecol. 29, 724–735 (2015).
    Article  Google Scholar 

    8.
    van Zandt, P. A. & Agrawal, A. A. Community-Wide impacts of herbivore-induced plant responses in milkweed (Asclepias syriaca). Ecology 85, 2616–2629 (2004).
    Article  Google Scholar 

    9.
    Erb, M., Robert, C. A. M., Hibbard, B. E. & Turlings, T. C. J. Sequence of arrival determines plant-mediated interactions between herbivores. J. Ecol. 99, 7–15 (2011).
    Article  Google Scholar 

    10.
    Kafle, D., Hänel, A., Lortzing, T., Steppuhn, A. & Wurst, S. Sequential above- and belowground herbivory modifies plant responses depending on herbivore identity. BMC Ecol. 17, 1–10 (2017).
    Article  Google Scholar 

    11.
    Stephens, A. E. A., Srivastava, D. S. & Myers, J. H. Strength in numbers? Effects of multiple natural enemy species on plant performance. Proc. R. Soc. B Biol. Sci. 280, 20122756 (2013).
    Article  Google Scholar 

    12.
    Gagic, V. et al. Interactive effects of pests increase seed yield. Ecol. Evol. 6, 2149–2157 (2016).
    PubMed  PubMed Central  Article  Google Scholar 

    13.
    Strauss, S. Y. Direct, indirect, and cumulative effects of three native herbivores on a shared host plant. Ecology 72, 543–558 (1991).
    Article  Google Scholar 

    14.
    Gómez, J. M. & González-Megías, A. Asymmetrical interactions between ungulates and phytophagous insects: being different matters. Ecology 83, 203–211 (2002).
    Article  Google Scholar 

    15.
    Ohgushi, T. Indirect interaction webs: herbivore-induced effects through trait change in plants. Annu. Rev. Ecol. Evol. Syst. 36, 81–105 (2005).
    Article  Google Scholar 

    16.
    Mauch-Mani, B., Baccelli, I., Luna, E. & Flors, V. Defense priming: an adaptive part of induced resistance. Annu. Rev. Plant Biol. 68, 485–512 (2017).
    CAS  PubMed  Article  Google Scholar 

    17.
    Hilker, M. et al. Priming and memory of stress responses in organisms lacking a nervous system. Biol. Rev. 91, 1118–1133 (2016).
    PubMed  Article  Google Scholar 

    18.
    Lyytikäinen-Saarenmaa, P. The responses of scots pine, Pinus silvestris, to natural and artificial defoliation stress. Ecol. Appl. 9, 469–474 (1999).
    Article  Google Scholar 

    19.
    Ericsson, A., Larsson, S. & Tenow, O. Effects of early and late season defoliation on growth and carbohydrate dynamics in scots pine. J. Appl. Ecol. 17, 747–769 (1980).
    Article  Google Scholar 

    20.
    Edenius, L. Browsing by moose on Scots pine in relation to plant resource availability. Ecology 74, 2261–2269 (1993).
    Article  Google Scholar 

    21.
    Nordkvist, M. et al. Trait-mediated indirect interactions: Moose browsing increases sawfly fecundity through plant-induced responses. Ecol. Evol. 9, 10615–10629 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    22.
    Edenius, L., Danell, K. & Nyquist, H. Effects of simulated moose browsing on growth, mortality, and fecundity in Scots pine: relations to plant productivity. Can. J. For. Res. 25, 529–535 (1995).
    Article  Google Scholar 

    23.
    Honkanen, T., Haukioja, E. & Kitunen, V. Responses of Pinus sylvestris branches to simulated herbivory are modified by tree sink/source dynamics and by external resources. Funct. Ecol. 13, 126–140 (1999).
    Article  Google Scholar 

    24.
    Persson, I. L., Bergström, R. & Danell, K. Browse biomass production and regrowth capacity after biomass loss in deciduous and coniferous trees: Responses to moose browsing along a productivity gradient. Oikos 116, 1639–1650 (2007).
    Article  Google Scholar 

    25.
    Belsky, A. J. Does herbivory benefit plants? A review of the evidence. Am. Nat. 127, 870–892 (1986).
    Article  Google Scholar 

    26.
    Bergman, M. Can saliva from moose, Alces alces, affect growth responses in the salow, Salix caprea?. Oikos 96, 164–168 (2002).
    Article  Google Scholar 

    27.
    Ohse, B. et al. Salivary cues: simulated roe deer browsing induces systemic changes in phytohormones and defence chemistry in wild-grown maple and beech saplings. Funct. Ecol. 31, 340–349 (2017).
    Article  Google Scholar 

    28.
    Kollberg, I. et al. Temperature affects insect outbreak risk through tritrophic interactions mediated by plant secondary compounds. Ecosphere 6, 1–17 (2015).
    Article  Google Scholar 

    29.
    Lyytikäinen-Saarenmaa, P. & Tomppo, E. Impact of sawfly defoliation on growth of Scots pine Pinus sylvestris (Pinaceae) and associated economic losses. Bull. Entomol. Res. 92, 137–140 (2002).
    PubMed  Article  Google Scholar 

    30.
    Augustine, D. J. & McNaughton, S. J. Ungulate effects on the functional species composition of plant communities: herbivore selectivity and plant tolerance. J. Wildl. Manag. 62, 1165–1183 (1998).
    Article  Google Scholar 

    31.
    Edenius, L., Bergman, M., Ericsson, G. & Danell, K. The role of moose as a disturbance factor in managed boreal forests. Silva Fennica 36, 57–67 (2002).
    Article  Google Scholar 

    32.
    Hódar, J. A., Zamora, R., Castro, J., Gómez, J. M. & García, D. Biomass allocation and growth responses of Scots pine saplings to simulated herbivory depend on plant age and light availability. Plant Ecol. 197, 229–238 (2008).
    Article  Google Scholar 

    33.
    Bergström, R. & Hjeljord, O. Moose and vegetation interactions in northwestern Europe and Poland. Swedish Wildl. Res. Suppl. 1, 213–228 (1987).
    Google Scholar 

    34.
    Nilsson, U., Berglund, M., Bergquist, J., Holmström, H. & Wallgren, M. Simulated effects of browsing on the production and economic values of Scots pine (Pinus sylvestris) stands. Scand. J. For. Res. 31, 279–285 (2016).
    Article  Google Scholar 

    35.
    Långsström, B. & Hellqvist, C. Effects of different pruning regimes on growth and sapwood area of Scots pine. For. Ecol. Manag. 44, 239–254 (1991).
    Article  Google Scholar 

    36.
    Mathisen, K. M., Milner, J. M. & Skarpe, C. Moose-tree interactions: rebrowsing is common across tree species. BMC Ecol. 17, 1–15 (2017).
    Article  Google Scholar 

    37.
    Bergqvist, G., Bergström, R. & Edenius, L. Effects of moose (Alces alces) rebrowsing on damage development in young stands of Scots pine (Pinus sylvestris). For. Ecol. Manag. 176, 397–403 (2003).
    Article  Google Scholar 

    38.
    Bergqvist, G., Bergström, R. & Edenius, L. Patterns of stem damage by moose (Alces alces) in young Pinus sylvestris stands in Sweden. Scand. J. For. Res. 16, 363–370 (2001).
    Article  Google Scholar 

    39.
    Riipi, M., Lempa, K., Haukioja, E., Ossipov, V. & Pihlaja, K. Effects of simulated winter browsing on mountain birch foliar chemistry and on the performance of insect herbivores. Oikos 111, 221–234 (2005).
    Article  Google Scholar 

    40.
    Kupferschmid, A. D. & Bugmann, H. Timing, light availability and vigour determine the response of Abies alba saplings to leader shoot browsing. Eur. J. For. Res. 132, 47–60 (2013).
    Article  Google Scholar 

    41.
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2019).

    42.
    Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team. _nlme: Linear and Nonlinear Mixed Effects Models_. R package version 3.1–142. https://CRAN.R-project.org/package=nlme (2019).

    43.
    Fox, J. & Weisberg, F. An {R} Companion to Applied Regression, Third Edition. Thousand Oaks CA: Sage. https://socialsciences.mcmaster.ca/jfox/Books/Companion/. (2019).

    44.
    Darling, E. S., Mcclanahan, T. R. & Côté, I. M. Combined effects of two stressors on Kenyan coral reefs are additive or antagonistic, not synergistic. Conserv. Lett. 3, 122–130 (2010).
    Article  Google Scholar 

    45.
    Bansal, S., Hallsby, G., Löfvenius, M. O. & Nilsson, M. C. Synergistic, additive and antagonistic impacts of drought and herbivory on Pinus sylvestris: leaf, tissue and whole-plant responses and recovery. Tree Physiol. 33, 451–463 (2013).
    CAS  PubMed  Article  Google Scholar  More

  • in

    Comparative analysis of bacterioplankton assemblages from two subtropical karst reservoirs of southwestern China with contrasting trophic status

    1.
    Neuenschwander, S. M., Pernthaler, J., Posch, T. & Salcher, M. M. Seasonal growth potential of rare lake water bacteria suggest their disproportional contribution to carbon fluxes. Environ. Microbiol. 17(3), 781–795 (2015).
    CAS  PubMed  Article  Google Scholar 
    2.
    Oki, T. & Kanae, S. Global hydrological cycles and world water resources. Science 313(5790), 1068–1072 (2006).
    ADS  CAS  PubMed  Article  Google Scholar 

    3.
    United Nations Environment Programme. GEO Year Book 2004/5: An Overview of Our Changing Environment (2004). https://www.unep.org/resources/report/geo-year-book-20045-overview-our-changing-environment.

    4.
    Lindström, E. S. Bacterioplankton community composition in five lakes differing in trophic status and humic content. Microb. Ecol. 40(2), 104–113 (2000).
    PubMed  Article  Google Scholar 

    5.
    Ávila, M. P., Staehr, P. A., Barbosa, F. A., Chartone-Souza, E. & Nascimento, A. Seasonality of freshwater bacterioplankton diversity in two tropical shallow lakes from the Brazilian Atlantic Forest. FEMS Microbiol. Ecol. 93, fw218 (2017).
    Article  CAS  Google Scholar 

    6.
    Zhang, H. et al. Biogeographic distribution patterns of algal community in different urban lakes in China: insights into the dynamics and co-existence. J. Environ. Sci. 100, 216–227 (2021).
    Article  Google Scholar 

    7.
    Ji, B. et al. Bacterial communities of four adjacent fresh lakes at different trophic status. Ecotoxicol. Environ. Safe 157, 388–394 (2018).
    CAS  Article  Google Scholar 

    8.
    Iliev, I. et al. Metagenomic profiling of the microbial freshwater communities in two Bulgarian reservoirs. J. Basic Microb. 57(8), 669–679 (2017).
    CAS  Article  Google Scholar 

    9.
    Linz, A. M. et al. Bacterial community composition and dynamics spanning five years in freshwater bog lakes. mSphere 2(3), e00169 (2017).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    10.
    Hartmann, A., Goldscheider, N., Wagener, T., Lange, J. & Weiler, M. Karst water resources in a changing world: review of hydrological modeling approaches. Rev. Geophys. 52(3), 218–242 (2014).
    ADS  Article  Google Scholar 

    11.
    Yu, S. et al. Spatial and temporal dynamics of bacterioplankton community composition in a subtropical dammed karst river of southwestern China. Microbiol. Open 8(9), e00849 (2019).
    Article  CAS  Google Scholar 

    12.
    Li, Q., Sun, H., Han, J., Liu, Z. & Yu, L. High-resolution study on the hydrochemical variations caused by the dilution of precipitation in the epikarst spring: an example spring of Landiantang at Nongla, Mashan, China. Environ. Geol. 54(2), 347–354 (2008).
    ADS  CAS  Article  Google Scholar 

    13.
    Song, A., Yue, M. L. & Li, Q. Influence of precipitation on bacterial structure in a typical karst spring, SW China. J. Groundw. Sci. Eng. 6(3), 193–204 (2018).
    Google Scholar 

    14.
    Gray, C. J. & Engel, A. S. Microbial diversity and impact on carbonate geochemistry across a changing geochemical gradient in a karst aquifer. ISME J. 7(2), 325–337 (2013).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    15.
    Shabarova, T. et al. Bacterial community structure and dissolved organic matter in repeatedly flooded subsurface karst water pools. FEMS Microbiol. Ecol. 89(1), 111–126 (2014).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    16.
    Li, Q. et al. Contribution of aerobic anoxygenic phototrophic bacteria to total organic carbon pool in aquatic system of subtropical karst catchments, Southwest China: evidence from hydrochemical and microbiological study. FEMS Microbiol. Ecol. 93, fix065 (2017).
    Google Scholar 

    17.
    Stevanović, Z. & Milanović, P. Engineering challenges in karst. Acta Carsol. 44(3), 381–399 (2015).
    Article  Google Scholar 

    18.
    Lu, X. X. et al. Water chemistry and characteristics of dissolved organic carbon during the wet season in Wulixia Reservoir, SW China. Huanjing Kexue 39(5), 2075–2085 (2018) (in Chinese with English abstract).
    PubMed  PubMed Central  Google Scholar 

    19.
    Xin, S. L. et al. Relationship between the bacterial abundance and production with environmental factors in a subtropical karst reservoir. Huanjing Kexue 39(12), 5647–5656 (2018) (in Chinese with English abstract).
    PubMed  PubMed Central  Google Scholar 

    20.
    National Research Council. Assessing the TMDL Approach to Water Quality Management (National Academy Press, Washington, DC, 2001).
    Google Scholar 

    21.
    Cunha, D. G. F., do Carmo Calijuri, M. & Lamparelli, M. C. A trophic state index for tropical/subtropical reservoirs (TSItsr). Ecol. Eng. 60, 126–134 (2013).
    Article  Google Scholar 

    22.
    Lorenzen, C. J. Determination of chlirophyll and pheo-pigments: spectrophotometric equations. Limnol. Oceanogr. 12(2), 343–346 (1967).
    ADS  CAS  Article  Google Scholar 

    23.
    Tamaki, H. et al. Analysis of 16S rRNA amplicon sequencing options on the Roche/454 next-generation titanium sequencing platform. PLoS ONE 6(9), e25263 (2011).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    24.
    Kuczynski, J. et al. Using QIIME to analyze 16S rRNA gene sequences from microbial communities. Curr. Protoc. Microbiol. 27(1), 1–20 (2012).
    Google Scholar 

    25.
    Vázquez-Baeza, Y., Pirrung, M., Gonzalez, A. & Knight, R. EMPeror: a tool for visualizing high-throughput microbial community data. Gigascience 2, 16 (2013).
    PubMed  PubMed Central  Article  Google Scholar 

    26.
    Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Austral. J. Ecol. 18(1), 117–143 (1993).
    Article  Google Scholar 

    27.
    Palmer, M. W., McGlinn, D. J., Westerberg, L. & Milberg, P. Indices for detecting differences in species composition: some simplifications of RDA and CCA. Ecology 89(6), 1769–1771 (2008).
    PubMed  Article  Google Scholar 

    28.
    Barberán, A., Bates, S. T., Casamayor, E. O. & Fierer, N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 6(2), 343–351 (2012).
    PubMed  Article  CAS  Google Scholar 

    29.
    Sanchez, G. PLS Path Modeling with R (Trowchez Editions, Berkeley, 2013).
    Google Scholar 

    30.
    Lopez-Chicano, M., Bouamama, M., Vallejos, A. & Pulido-Bosch, A. Factors which determine the hydrogeochemical behaviour of karstic springs. A case study from the Betic Cordilleras, Spain. Appl. Geochem. 16(9–10), 1179–1192 (2001).
    CAS  Article  Google Scholar 

    31.
    Stumm, W. & Morgan, J. J. Aquatic chemistry: chemical equilibria and rates in natural waters. In Environmental Science and Technology (eds Stumm, W. & Morgan, J. J.) (Wiley, New York, 2012).
    Google Scholar 

    32.
    Newton, R. J., Jones, S. E., Eiler, A., McMahon, K. D. & Bertilsson, S. A guide to the natural history of freshwater lake bacteria. Microbiol. Mol. Biol. R. 75, 14–49 (2011).
    CAS  Article  Google Scholar 

    33.
    Freilich, S. et al. Competitive and cooperative metabolic interactions in bacterial communities. Nat. Commun. 2(1), 589 (2011).
    ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

    34.
    Li, D. et al. Microbial community evolution during simulated managed aquifer recharge in response to different biodegradable dissolved organic carbon (BDOC) concentrations. Water Res. 47(7), 2421–2430 (2013).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    35.
    Miranda, C. D. & Zemelman, R. Bacterial resistance to oxytetracycline in Chilean salmon farming. Aquaculture 212(1–4), 31–47 (2002).
    CAS  Article  Google Scholar 

    36.
    Dul’tseva, N. M., Chernitsina, S. M. & Zemskaya, T. I. Isolation of bacteria of the genus Variovorax from the Thioploca mats of Lake Baikal. Microbiology 81(1), 67–78 (2012).
    Article  CAS  Google Scholar 

    37.
    Mohiuddin, M. M., Salama, Y., Schellhorn, H. E. & Golding, G. B. Shotgun metagenomic sequencing reveals freshwater beach sands as reservoir of bacterial pathogens. Water Res. 115, 360–369 (2017).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    38.
    Fuentes, S., Méndez, V., Aguila, P. & Seeger, M. Bioremediation of petroleum hydrocarbons: catabolic genes, microbial communities, and applications. Appl. Microbiol. Biotechnol. 98(11), 4781–4794 (2014).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    39.
    Gomes, B. C. et al. Analysis of a microbial community associated with polychlorinated biphenyl degradation in anaerobic batch reactors. Biodegradation 25(6), 797–810 (2014).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    40.
    Cai, J. et al. Characterization of bacterial and microbial eukaryotic communities associated with an ephemeral hypoxia event in Taihu Lake, a shallow eutrophic Chinese lake. Environ. Sci. Pollut. R. 25(31), 31543–31557 (2018).
    CAS  Article  Google Scholar 

    41.
    Zhang, S. et al. Characterization of a novel bacteriophage specific to Exiguobacterium indicum isolated from a plateau eutrophic lake. J. Basic Microb. 59(2), 206–214 (2019).
    CAS  Article  Google Scholar 

    42.
    Li, S., Luo, Z. & Ji, G. Seasonal function succession and biogeographic zonation of assimilatory and dissimilatory nitrate-reducing bacterioplankton. Sci. Total Environ. 637, 1518–1525 (2018).
    ADS  PubMed  Article  CAS  Google Scholar 

    43.
    Savio, D. et al. Spring water of an alpine karst aquifer is dominated by a taxonomically stable but discharge-responsive bacterial community. Front. Microbiol. 10, 28 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    44.
    Freedman, Z. & Zak, D. R. Soil bacterial communities are shaped by temporal and environmental filtering: evidence from a long-term chronosequence. Environ. Microbiol. 17(9), 3208–3218 (2015).
    PubMed  Article  Google Scholar 

    45.
    Subramani, T., Elango, L. & Damodarasamy, S. R. Groundwater quality and its suitability for drinking and agricultural use in Chithar River Basin, Tamil Nadu, India. Environ. Geol. 47(8), 1099–1110 (2005).
    CAS  Article  Google Scholar 

    46.
    Jost, L. Partitioning diversity into independent alpha and beta components. Ecology 88(10), 2427–2439 (2007).
    PubMed  Article  Google Scholar 

    47.
    Niño-García, J. P., Ruiz-González, C. & del Giorgio, P. A. Interactions between hydrology and water chemistry shape bacterioplankton biogeography across borssseal freshwater networks. ISME J. 10(7), 1755 (2016).
    PubMed  PubMed Central  Article  CAS  Google Scholar  More

  • in

    Microbial community composition in the rhizosphere of Larix decidua under different light regimes with additional focus on methane cycling microorganisms

    1.
    Paul, E. A. (ed.) Soil Microbiology, Ecology and Biochemistry (Academic Press, Amsterdam, 2015).
    Google Scholar 
    2.
    Nannipieri, P. et al. Microbial diversity and soil functions. Eur. J. Soil. Sci. 54, 655–670 (2003).
    Article  Google Scholar 

    3.
    Kuzyakov, Y. & Blagodatskaya, E. Microbial hotspots and hot moments in soil: concept & review. Soil. Biol. Biochem. 83, 184–199 (2015).
    CAS  Article  Google Scholar 

    4.
    Berg, G. & Smalla, K. Plant species and soil type cooperatively shape the structure and function of microbial communities in the rhizosphere. FEMS Microbiol. Ecol. 68, 1–13 (2009).
    CAS  Article  Google Scholar 

    5.
    Bais, H. P., Park, S.-W., Weir, T. L., Callaway, R. M. & Vivanco, J. M. How plants communicate using the underground information superhighway. Trends Plant. Sci. 9, 26–32 (2004).
    CAS  Article  Google Scholar 

    6.
    Mendes, R., Garbeva, P. & Raaijmakers, J. M. The rhizosphere microbiome: significance of plant beneficial, plant pathogenic, and human pathogenic microorganisms. FEMS Microbiol. Rev. 37, 634–663. https://doi.org/10.1111/1574-6976.12028 (2013).
    CAS  Article  PubMed  Google Scholar 

    7.
    Praeg, N., Pauli, H. & Illmer, P. Microbial diversity in bulk and rhizosphere soil of Ranunculus glacialis along a high-alpine altitudinal gradient. Front. Microbiol. 10, 1429. https://doi.org/10.3389/fmicb.2019.01429 (2019).
    Article  PubMed  PubMed Central  Google Scholar 

    8.
    Nacke, H. et al. Pyrosequencing-based assessment of bacterial community structure along different management types in German forest and grassland soils. PLoS ONE 6, e17000. https://doi.org/10.1371/journal.pone.0017000 (2011).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    9.
    Jackson, R. B., Solomon, E. I., Canadell, J. G., Cargnello, M. & Field, C. B. Methane removal and atmospheric restoration. Nat. Sustain. 2, 436–438. https://doi.org/10.1038/s41893-019-0299-x (2019).
    Article  Google Scholar 

    10.
    Ciais, P. et al. Climate Change 2013: The Physical Science Basis. In Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) (Cambridge University Press, Cambridge, 2013).
    Google Scholar 

    11.
    Adam, P. S., Borrel, G., Brochier-Armanet, C. & Gribaldo, S. The growing tree of Archaea: new perspectives on their diversity, evolution and ecology. ISME J. 11, 2407. https://doi.org/10.1038/ismej.2017.122 (2017).
    Article  PubMed  PubMed Central  Google Scholar 

    12.
    Knief, C. Diversity and habitat preferences of cultivated and uncultivated aerobic methanotrophic bacteria evaluated based on pmoA as molecular marker. Front. Microbiol. 6, 1346 (2015).
    Article  Google Scholar 

    13.
    Hanson, R. S. & Hanson, T. E. Methanotrophic bacteria. Microbiol. Rev. 60, 439–471 (1996).
    CAS  Article  Google Scholar 

    14.
    Op den Camp, H. J. M. et al. Environmental, genomic and taxonomic perspectives on methanotrophic Verrucomicrobia. Environ. Microbiol. Rep. 1, 293–306. https://doi.org/10.1111/j.1758-2229.2009.00022.x (2009).
    CAS  Article  PubMed  Google Scholar 

    15.
    Knief, C., Lipski, A. & Dunfield, P. F. Diversity and activity of methanotrophic bacteria in different upland soils. Appl. Environ. Microbiol. 69, 6703–6714. https://doi.org/10.1128/AEM.69.11.6703-6714.2003 (2003).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    16.
    Kolb, S. The quest for atmospheric methane oxidizers in forest soils. Environ. Microbiol. Rep. 1, 336–346 (2009).
    CAS  Article  Google Scholar 

    17.
    Plesa, I. et al. Effects of drought and salinity on European Larch (Larix decidua Mill.) seedlings. Forests 9, 320. https://doi.org/10.3390/f9060320 (2018).
    Article  Google Scholar 

    18.
    Falk, W., Bachmann-Gigl, U. & Kölling, C. Die Europäische Lärche im Klimawandel. In Beiträge zur Europäischen Lärche (ed. Schmidt, O.) 19–27 (Bayrische Landesanstalt für Wald und Forstwirtschaft, Freising, 2012).
    Google Scholar 

    19.
    Obojes, N. et al. Water stress limits transpiration and growth of European larch up to the lower subalpine belt in an inner-alpine dry valley. New Phytol. 220, 460–475 (2018).
    Article  Google Scholar 

    20.
    Wieser, G. (ed.) Trees at Their Upper Limit. Treelife Limitation at the Alpine Timberline (Springer, Dordrecht, 2007).
    Google Scholar 

    21.
    Dedysh, S. N. et al. Methylocapsa palsarum sp. nov., a methanotroph isolated from a subArctic discontinuous permafrost ecosystem. Int. J. Syst. Evol. Microbiol. 65, 3618–3624. https://doi.org/10.1099/ijsem.0.000465 (2015).
    CAS  Article  PubMed  Google Scholar 

    22.
    Praeg, N., Wagner, A. O. & Illmer, P. Plant species, temperature, and bedrock affect net methane flux out of grassland and forest soils. Plant Soil 410, 193–206 (2017).
    CAS  Article  Google Scholar 

    23.
    Lladó, S., López-Mondéjar, R. & Baldrian, P. Forest soil bacteria: diversity, involvement in ecosystem processes, and response to global change. Microbiol. Mol. Biol. Rev. https://doi.org/10.1128/MMBR.00063-16 (2017).
    Article  PubMed  PubMed Central  Google Scholar 

    24.
    Urbanová, M., Šnajdr, J. & Baldrian, P. Composition of fungal and bacterial communities in forest litter and soil is largely determined by dominant trees. Soil. Biol. Biochem. 84, 53–64. https://doi.org/10.1016/j.soilbio.2015.02.011 (2015).
    CAS  Article  Google Scholar 

    25.
    Liu, J. et al. Characteristics of bulk and rhizosphere soil microbial community in an ancient Platycladus orientalis forest. Appl. Soil Ecol. 132, 91–98. https://doi.org/10.1016/j.apsoil.2018.08.014 (2018).
    ADS  Article  Google Scholar 

    26.
    Uroz, S. et al. Specific impacts of beech and Norway spruce on the structure and diversity of the rhizosphere and soil microbial communities. Sci. Rep. 6, 27756. https://doi.org/10.1038/srep27756 (2016).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    27.
    Štursová, M., Bárta, J., Šantrůčková, H. & Baldrian, P. Small-scale spatial heterogeneity of ecosystem properties, microbial community composition and microbial activities in a temperate mountain forest soil. FEMS Microbiol. Ecol. https://doi.org/10.1093/femsec/fiw185 (2016).
    Article  PubMed  Google Scholar 

    28.
    Ferrari, B., Winsley, T., Ji, M. & Neilan, B. Insights into the distribution and abundance of the ubiquitous candidatus Saccharibacteria phylum following tag pyrosequencing. Sci. Rep. 4, 3957. https://doi.org/10.1038/srep03957 (2014).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    29.
    Starr, E. P. et al. Stable isotope informed genome-resolved metagenomics reveals that Saccharibacteria utilize microbially-processed plant-derived carbon. Microbiome 6, 122. https://doi.org/10.1186/s40168-018-0499-z (2018).
    Article  PubMed  PubMed Central  Google Scholar 

    30.
    Brewer, T. E., Handley, K. M., Carini, P., Gilbert, J. A. & Fierer, N. Genome reduction in an abundant and ubiquitous soil bacterium ‘Candidatus Udaeobacter copiosus’. Nat. Microbiol. 2, 16198. https://doi.org/10.1038/nmicrobiol.2016.198 (2016).
    CAS  Article  PubMed  Google Scholar 

    31.
    Kielak, A. M., Barreto, C. C., Kowalchuk, G. A., van Veen, J. A. & Kuramae, E. E. The ecology of acidobacteria: moving beyond genes and genomes. Front. Microbiol. 7, 744. https://doi.org/10.3389/fmicb.2016.00744 (2016).
    Article  PubMed  PubMed Central  Google Scholar 

    32.
    Fierer, N., Bradford, M. A. & Jackson, R. B. Toward an ecological classification of soil bacteria. Ecology 88, 1354–1364 (2007).
    Article  Google Scholar 

    33.
    Johnston-Monje, D., Lundberg, D. S., Lazarovits, G., Reis, V. M. & Raizada, M. N. Bacterial populations in juvenile maize rhizospheres originate from both seed and soil. Plant Soil 405, 337–355. https://doi.org/10.1007/s11104-016-2826-0 (2016).
    CAS  Article  Google Scholar 

    34.
    Fierer, N. et al. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. Proc. Nat. Acad. Sci. USA 109, 21390–21395. https://doi.org/10.1073/pnas.1215210110 (2012).
    ADS  Article  PubMed  Google Scholar 

    35.
    Kottke, I. & Oberwinkler, F. Comparative studies on the mycorrhization of Larix decidua and Picea abies by Suillus grevillei. Trees https://doi.org/10.1007/BF00196758 (1988).
    Article  Google Scholar 

    36.
    Uroz, S., Buée, M., Murat, C., Frey-Klett, P. & Martin, F. Pyrosequencing reveals a contrasted bacterial diversity between oak rhizosphere and surrounding soil. Environ. Microbiol. Rep. 2, 281–288. https://doi.org/10.1111/j.1758-2229.2009.00117.x (2010).
    CAS  Article  PubMed  Google Scholar 

    37.
    Mapelli, F. et al. The stage of soil development modulates rhizosphere effect along a High Arctic desert chronosequence. ISME J. 12, 1188. https://doi.org/10.1038/s41396-017-0026-4 (2018).
    Article  PubMed  PubMed Central  Google Scholar 

    38.
    Mello, B. L., Alessi, A. M., McQueen-Mason, S., Bruce, N. C. & Polikarpov, I. Nutrient availability shapes the microbial community structure in sugarcane bagasse compost-derived consortia. Sci. Rep. 6, 38781. https://doi.org/10.1038/srep38781 (2016).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    39.
    Turnbull, G. A., Morgan, J. A. W., Whipps, J. M. & Saunders, J. R. The role of bacterial motility in the survival and spread of Pseudomonas fluorescens in soil and in the attachment and colonisation of wheat roots. FEMS Microbiol. Ecol. 36, 21–31. https://doi.org/10.1111/j.1574-6941.2001.tb00822.x (2001).
    CAS  Article  PubMed  Google Scholar 

    40.
    Rees, D. C., Johnson, E. & Lewinson, O. ABC transporters: the power to change. Nat. Rev. Mol. Cell. Biol. 10, 218–227. https://doi.org/10.1038/nrm2646 (2009).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    41.
    Aronson, E. L., Allison, S. D. & Helliker, B. R. Environmental impacts on the diversity of methane-cycling microbes and their resultant function. Front. Microbiol. 4, 225. https://doi.org/10.3389/fmicb.2013.00225 (2013).
    Article  PubMed  PubMed Central  Google Scholar 

    42.
    Dalal, R. C., Allen, D. E., Livesley, S. J. & Richards, G. Magnitude and biophysical regulators of methane emission and consumption in the Australian agricultural, forest, and submerged landscapes. A review. Plant Soil 309, 43–76 (2008).
    CAS  Article  Google Scholar 

    43.
    Martins, C. S. C., Nazaries, L., Macdonald, C. A., Anderson, I. C. & Singh, B. K. Water availability and abundance of microbial groups are key determinants of greenhouse gas fluxes in a dryland forest ecosystem. Soil Biol. Biochem. 86, 5–16. https://doi.org/10.1016/j.soilbio.2015.03.012 (2015).
    CAS  Article  Google Scholar 

    44.
    Praeg, N., Schwinghammer, L. & Illmer, P. Larix decidua and additional light affect the methane balance of forest soil and the abundance of methanogenic and methanotrophic microorganisms. FEMS Microbiol Lett. https://doi.org/10.1093/femsle/fnz259 (2020).
    Article  Google Scholar 

    45.
    Ström, L., Mastepanov, M. & Christensen, T. R. Species-specific effects of vascular plants on carbon turnover and methane emissions from wetlands. Biogeochemistry 75, 65–82 (2005).
    Article  Google Scholar 

    46.
    Borrel, G. et al. Genome sequence of “Candidatus Methanomassiliicoccus intestinalis” Issoire-Mx1, a third thermoplasmatales-related methanogenic archaeon from human feces. Genome Announc. 1, e004523. https://doi.org/10.1128/genomeA.00453-13 (2013).
    Article  Google Scholar 

    47.
    Deng, Y., Liu, P. & Conrad, R. Effect of temperature on the microbial community responsible for methane production in alkaline NamCo wetland soil. Soil Biol. Biochem. 132, 69–79. https://doi.org/10.1016/j.soilbio.2019.01.024 (2019).
    CAS  Article  Google Scholar 

    48.
    Söllinger, A. et al. Phylogenetic and genomic analysis of Methanomassiliicoccales in wetlands and animal intestinal tracts reveals clade-specific habitat preferences. FEMS Microbiol. Ecol. 92, 149. https://doi.org/10.1093/femsec/fiv149 (2016).
    CAS  Article  Google Scholar 

    49.
    Berghuis, B. A. et al. Hydrogenotrophic methanogenesis in archaeal phylum Verstraetearchaeota reveals the shared ancestry of all methanogens. Proc. Natl. Acad. Sci. U.S.A. 116, 5037. https://doi.org/10.1073/pnas.1815631116 (2019).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    50.
    Cai, Y., Zheng, Y., Bodelier, P. L. E., Conrad, R. & Jia, Z. Conventional methanotrophs are responsible for atmospheric methane oxidation in paddy soils. Nat. Commun. 7, 11728 (2016).
    ADS  CAS  Article  Google Scholar 

    51.
    Henckel, T., Jäckel, U., Schnell, S. & Conrad, R. Molecular analyses of novel methanotrophic communities in forest soil that oxidize atmospheric methane. Appl. Environ. Microbiol. 60, 1801–1808 (2000).
    Article  Google Scholar 

    52.
    Ricke, P., Kolb, S. & Braker, G. Application of a newly developed ARB software-integrated tool for in silico terminal restriction fragment length polymorphism analysis reveals the dominance of a novel pmoA cluster in a forest soil. Appl. Environ. Microbiol. 71, 1671–1673. https://doi.org/10.1128/AEM.71.3.1671-1673.2005 (2005).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    53.
    Pratscher, J., Dumont, M. G. & Conrad, R. Assimilation of acetate by the putative atmospheric methane oxidizers belonging to the USCα clade. Environ. Microbiol. 13, 2692–2701. https://doi.org/10.1111/j.1462-2920.2011.02537.x (2011).
    CAS  Article  PubMed  Google Scholar 

    54.
    Cai, Y., Zhou, X., Shi, L. & Jia, Z. Atmospheric methane oxidizers are dominated by upland soil cluster alpha in 20 forest soils of China. Microb. Ecol. 80, 859–871. https://doi.org/10.1007/s00248-020-01570-1 (2020).
    CAS  Article  PubMed  Google Scholar 

    55.
    Täumer, J. et al. Divergent drivers of the microbial methane sink in temperate forest and grassland soils. Glob. Change Biol. https://doi.org/10.1111/gcb.15430 (2020).
    Article  Google Scholar 

    56.
    Andreote, F. D. et al. Culture-independent assessment of Rhizobiales-related alphaproteobacteria and the diversity of Methylobacterium in the rhizosphere and rhizoplane of transgenic eucalyptus. Microb. Ecol. 57, 82–93. https://doi.org/10.1007/s00248-008-9405-8 (2009).
    Article  PubMed  Google Scholar 

    57.
    Iguchi, H., Yurimoto, H. & Sakai, Y. Interactions of methylotrophs with plants and other heterotrophic bacteria. Microorganisms 3, 137–151. https://doi.org/10.3390/microorganisms3020137 (2015).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    58.
    Ho, A. et al. Biotic interactions in microbial communities as modulators of biogeochemical processes: methanotrophy as a model system. Front. Microbiol. 7, 1285. https://doi.org/10.3389/fmicb.2016.01285 (2016).
    Article  PubMed  PubMed Central  Google Scholar 

    59.
    Iguchi, H., Yurimoto, H. & Sakai, Y. Stimulation of methanotrophic growth in cocultures by cobalamin excreted by rhizobia. Appl. Environ. Microbiol. 77, 8509–8515. https://doi.org/10.1128/AEM.05834-11 (2011).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    60.
    Veraart, A. J. et al. Living apart together—bacterial volatiles influence methanotrophic growth and activity. ISME J. 12, 1163–1166 (2018).
    CAS  Article  Google Scholar 

    61.
    Karlsson, A. E., Johansson, T. & Bengtson, P. Archaeal abundance in relation to root and fungal exudation rates. FEMS Microbiol. Ecol. 80, 305–311 (2012).
    CAS  Article  Google Scholar 

    62.
    Haichar, F. E. Z. et al. Plant host habitat and root exudates shape soil bacterial community structure. ISME J. 2, 1221–1230. https://doi.org/10.1038/ismej.2008.80 (2008).
    CAS  Article  PubMed  Google Scholar 

    63.
    Tkacz, A., Cheema, J., Chandra, G., Grant, A. & Poole, P. S. Stability and succession of the rhizosphere microbiota depends upon plant type and soil composition. ISME J. 9, 2349–2359. https://doi.org/10.1038/ismej.2015.41 (2015).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    64.
    Schinner, F. et al. (eds) Methods in Soil Biology (Springer, Berlin, 1996).
    Google Scholar 

    65.
    Barillot, C. D. C., Sarde, C.-O., Bert, V., Tarnaud, E. & Cochet, N. A standardized method for the sampling of rhizosphere and rhizoplan soil bacteria associated to a herbaceous root system. Ann. Microbiol. 63, 471–476 (2013).
    CAS  Article  Google Scholar 

    66.
    Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Nat. Acad. Sci. U.S.A. 108(Suppl 1), 4516–4522. https://doi.org/10.1073/pnas.1000080107 (2011).
    ADS  Article  Google Scholar 

    67.
    Ihrmark, K. et al. New primers to amplify the fungal ITS2 region–evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol. Ecol. 82, 666–677. https://doi.org/10.1111/j.1574-6941.2012.01437.x (2012).
    CAS  Article  PubMed  Google Scholar 

    68.
    White, T. J., Bruns, T., Lee, S. & Taylor, J. W. Amplification and direct sequencing of fungal ribosomal RNA Genes for phylogenetics. In PCR Protocols: A Guide to Methods and Applications (eds Innis, M. A. et al.) 315–322 (Academic Press, Cambridge, 1990).
    Google Scholar 

    69.
    Schloss, P. D. et al. Introducing mothur. Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).
    CAS  Article  Google Scholar 

    70.
    Bengtsson-Palme, J. et al. Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods Ecol. Evol. 25, 914–919. https://doi.org/10.1111/2041-210X.12073 (2013).
    Article  Google Scholar 

    71.
    Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mah, F. VSEARCH. A versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).
    Article  Google Scholar 

    72.
    Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596. https://doi.org/10.1093/nar/gks1219 (2013).
    CAS  Article  PubMed  Google Scholar 

    73.
    Kõljalg, U. et al. Towards a unified paradigm for sequence-based identification of fungi. Mol. Ecol. 22, 5271–5277. https://doi.org/10.1111/mec.12481 (2013).
    CAS  Article  PubMed  Google Scholar 

    74.
    Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267. https://doi.org/10.1128/AEM.00062-07 (2007).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    75.
    Mantel, N. The detection of disease clustering and a generalized regression approach. Can. Res. 27, 209–220 (1967).
    CAS  Google Scholar 

    76.
    Martin, A. P. Phylogenetic approaches for describing and comparing the diversity of microbial communities. Appl. Environ. Microbiol. 68, 3673–3682. https://doi.org/10.1128/AEM.68.8.3673-3682.2002 (2002).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    77.
    Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117–143 (1993).
    Article  Google Scholar 

    78.
    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2017). http://www.R-project.org. Accessed 24 Sept 2018.

    79.
    Oksanen, J. et al. vegan. Community Ecology Package. R package version 2.4–4 (2017). https://CRAN.R-project.org/package=vegan. Accessed 24 Sept 2018.

    80.
    Afgan, E. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucleic Acids Res. 44, W3–W10. https://doi.org/10.1093/nar/gkw343 (2016).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    81.
    Langille, M. G. I. et al. Predictive functional profiling of microbial communities using 16SrRNA marker gene sequences. Nat. Biotechnol. 31, 814–821 (2013).
    CAS  Article  Google Scholar 

    82.
    White, J. R., Nagarajan, N. & Pop, M. Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput. Biol. 5, e1000352. https://doi.org/10.1371/journal.pcbi.1000352 (2009).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    83.
    Parks, D. H., Tyson, G. W., Hugenholtz, P. & Beiko, R. G. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30, 3123–3124. https://doi.org/10.1093/bioinformatics/btu494 (2014).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    84.
    Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. & Tanabe, M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109–D114. https://doi.org/10.1093/nar/gkr988 (2012).
    CAS  Article  PubMed  Google Scholar 

    85.
    Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).
    Article  Google Scholar 

    86.
    Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504. https://doi.org/10.1101/gr.1239303 (2003).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    87.
    Pratscher, J., Vollmers, J., Wiegand, S., Dumont, M. G. & Kaster, A.-K. Unravelling the identity, metabolic potential and global biogeography of the atmospheric methane-oxidizing upland soil cluster α. Environ. Microbiol. 20, 1016–1029. https://doi.org/10.1111/1462-2920.14036 (2018).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    88.
    Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780. https://doi.org/10.1093/molbev/mst010 (2013).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    89.
    Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321. https://doi.org/10.1093/sysbio/syq010 (2010).
    CAS  Article  PubMed  Google Scholar  More

  • in

    Leaf proteome modulation and cytological features of seagrass Cymodocea nodosa in response to long-term high CO2 exposure in volcanic vents

    1.
    Tassi, F. et al. Low-pH waters discharging from submarine vents at Panarea Island (Aeolian Islands, southern Italy) after the 2002 gas blast: Origin of hydrothermal fluids and implications for volcanic surveillance. Appl. Geochem. 24, 246–254 (2009).
    CAS  Article  Google Scholar 
    2.
    Boatta, F. et al. Geochemical survey of Levante Bay, Vulcano Island (Italy), a natural laboratory for the study of ocean acidification. Mar. Pollut. Bull. 73, 485–494. https://doi.org/10.1016/j.marpolbul.2013.01.029 (2013).
    CAS  Article  PubMed  Google Scholar 

    3.
    Hall-Spencer, J. M. et al. Volcanic carbon dioxide vents show ecosystem effects of ocean acidification. Nature 454, 96–99 (2008).
    ADS  CAS  Article  Google Scholar 

    4.
    Ricevuto, E., Kroeker, K. J., Ferrigno, F. & Gambi, M. C. Spatio-temporal variability of polychaete colonization at volcanic CO2 vents indicates high tolerance to ocean acidification. Mar. Biol. 161, 2909–2919. https://doi.org/10.1007/s00227-014-2555-y (2014).
    CAS  Article  Google Scholar 

    5.
    Ricevuto, E., Vizzini, S. & Gambi, M. C. Ocean acidification effects on stable isotope signatures and trophic interactions of polychaete consumers and organic matter sources at a CO2 shallow vent system. J. Exp. Mar. Biol. Ecol. 468, 105–117. https://doi.org/10.1016/j.jembe.2015.03.016 (2015).
    CAS  Article  Google Scholar 

    6.
    Foo, S.A., Byrne, M., Ricevuto, E., Gambi, M.C. The Carbon Dioxide Vents of Ischia, Italy, A Natural System to Assess Impacts of Ocean Acidification on Marine Ecosystems: An Overview of Research and Comparisons with Other Vent Systems. In Oceanography and Marine Biology An Annual Review. S. J. Hawkins, A. J. Evans, A.C. Dale, L. B. Firth, I. P. Smith eds. Taylor & Francis Group, 56 (2018).

    7.
    Mutalipassi, M. et al. Ocean acidification alters the responses of invertebrates to wound-activated infochemicals produced by epiphytes of the seagrassPosidonia oceanica. J. Exp. Mar. Biol. Ecol. 530–531, 151435 (2020).
    Article  Google Scholar 

    8.
    Apostolaki, E. T., Vizzini, S., Hendriks, I. E. & Olsen, Y. S. Seagrass ecosystem response to long-term high CO2 in a Mediterranean volcanic vent. Mar. Environ. Res. 99, 9–15 (2014).
    CAS  Article  Google Scholar 

    9.
    Vizzini, S., Apostolaki, E. T., Ricevuto, E., Polymenakou, P. & Mazzola, A. Plant and sediment properties in seagrass meadows from two Mediterranean CO2 vents: Implications for carbon storage capacity of acidified oceans. Mar. Environ. Res. 146, 101–108 (2019).
    CAS  Article  Google Scholar 

    10.
    Beer, S., Björk, M., Beardall, J. Acquisition of carbon in marine plants. In: John Wiley & Sons eds. Photoshynthesis in the Marine Environment. Wiley Blackwell, Iowa, USA. pp: 95–124 (2014).

    11.
    Beer, S., Björk, M., Hellblom, F. & Axelsson, L. Inorganic carbon utilization in marine angiosperms (seagrasses). Funct. Plant Biol. 29, 349–354 (2002).
    CAS  Article  Google Scholar 

    12.
    Koch, M., Bowes, G., Ross, C. & Zhang, X. H. Climate change and ocean acidification effects on seagrasses and marine macroalgae. Glob. Change Biol. 19, 103–132. https://doi.org/10.1111/j.1365-2486.2012.02791.x (2013).
    ADS  Article  Google Scholar 

    13.
    Zimmerman, R. C., Kohrs, D. G., Steller, D. L. & Alberte, R. S. Impacts of CO2 enrichment on productivity and light requirements of eelgrass. Plant Physiol. 115, 599–607. https://doi.org/10.1104/pp.115.2.599 (1997).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    14.
    Garrard, S. L. & Beaumont, N. J. The effect of ocean acidification on carbon storage and sequestration in seagrass beds; a global and UK context. Mar. Pollut. Bull. 86, 138–146 (2014).
    CAS  Article  Google Scholar 

    15.
    Hendriks, I. E., Duarte, C. M. & Alvarez, M. A. Vulnerability of marine biodiversity to ocean acidification: a meta-analysis. Estuar. Coast. Shelf Sci. 86, 157–164 (2010).
    ADS  CAS  Article  Google Scholar 

    16.
    Zimmerman, R. C., Hill, V. J. & Gallegos, C. L. Predicting effects of ocean warming, acidification, and water quality on Chesapeake region eelgrass. Limnol. Oceanogr. 60(2015), 1781–1804 (2015).
    ADS  CAS  Article  Google Scholar 

    17.
    Pacella, S. R., Cheryl, A. B., George, G. W., Rochelle, G. L. & Burke, H. Seagrass habitat metabolism increases short-term extremes and long-term offset of CO2 under future ocean acidification. PNAS 115(15), 3870–3875 (2018).
    ADS  CAS  Article  Google Scholar 

    18.
    Russell, B. D., Connell, S. D., Uthicke, S. & Hall-Spencer, J. M. Future seagrass beds: can increased productivity lead to increased carbon storage?. Mar. Pollut. Bull. 73, 463–469 (2013).
    CAS  Article  Google Scholar 

    19.
    de los Santos, C. B., Godbold, J. A. & Solan, M. Short-term growth and biomechanical responses of the temperate seagrassCymodocea nodosato CO2 enrichment. Mar. Ecol. Prog. Ser. 572, 91–102 (2017).
    ADS  CAS  Article  Google Scholar 

    20.
    Schneider, G. et al. Structural and physiological responses of Halodule wrightii to ocean acidification. Protoplasma 255, 629–641 (2018).
    CAS  Article  Google Scholar 

    21.
    Radoglou, K. M. & Jarvis, P. G. The effects of CO2 enrichment and nutrient supply on growth morphology and anatomy of Phaseolus vulgaris L seedlings. Ann. Bot. 70, 245–256 (1992).
    CAS  Article  Google Scholar 

    22.
    Epron, D., Liozon, R. & Mousseau, M. Effects of elevated CO2 concentration on leaf characteristics and photosynthetic capacity of beech (Fagus sylvatica) during the growing season. Tree Physiol. 16, 425–432 (1995).
    Article  Google Scholar 

    23.
    Lin, J., Jach, M. E. & Ceulemans, R. Stomatal density and needle anatomy of Scots pine (Pinus sylvestris) are affected by elevated CO2. New Phytol. 150, 665–674 (2001).
    Article  Google Scholar 

    24.
    Ruocco, M. et al. Genome-wide transcriptional reprogramming in the seagrassCymodocea nodosa under experimental ocean acidification. MolEcol 26, 4241–4259. https://doi.org/10.1111/mec.14204 (2017).
    CAS  Article  Google Scholar 

    25.
    Olivé, I. et al. Linking gene expression to productivity to unravel long- and short-term responses of seagrasses exposed to CO2 in volcanic vents. Sci. Rep. 7, 42278 (2017).
    ADS  Article  Google Scholar 

    26.
    Procaccini, G. et al. Depth-specific fluctuations of gene expression and protein abundance modulate the photophysiology in the seagrassPosidonia oceanica. Sci. Rep. 7, 42890. https://doi.org/10.1038/srep42890 (2017).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    27.
    Kumar, M. et al. Proteome analysis reveals extensive light stress response reprogramming in the seagrassZostera muelleri (Alismatales, Zosteraceae) metabolism. Frontiers Plant Sci. 7, 2023 (2017).
    Article  Google Scholar 

    28.
    Piro, A. et al. The modulation of leaf metabolism plays a role in salt tolerance of Cymodocea nodosa exposed to hypersaline stress in mesocosms. Front Plant Sci. 6, 464 (2015).
    Article  Google Scholar 

    29.
    Dattolo, E. et al. Acclimation to different depths by the marine angiosperm Posidonia oceanica: transcriptomic and proteomic profiles. Front. Plant Sci. 4, 195. https://doi.org/10.3389/fpls.2013.00195 (2013).
    Article  PubMed  PubMed Central  Google Scholar 

    30.
    Mazzuca, S. et al. Seagrass light acclimation: 2-DE protein analysis in Posidonia leaves grown inchronic low light conditions. J. Exp. Mar. Biol. Ecol. 374, 113–122 (2009).
    CAS  Article  Google Scholar 

    31.
    Schwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).
    ADS  Article  Google Scholar 

    32.
    Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).
    CAS  Article  Google Scholar 

    33.
    Watanabe, C. K. et al. Effects of elevated CO2 on levels of primary metabolites and transcripts of genes encoding respiratory enzymes and their diurnal patterns in Arabidopsis thaliana: possible relationships with respiratory rates. Plant Cell Physiol. 55(2), 341–357. https://doi.org/10.1093/pcp/pct185 (2014).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    34.
    Lauritano, C. et al. Response of key stress-related genes of the seagrassPosidonia oceanica in the vicinity of submarine volcanic vents. Biogeosciences 12, 4947–4971 (2015).
    Article  Google Scholar 

    35
    Neha, S., Gokhale, S. P. & Kumar, B. A. Effect of elevated [CO2] on cell structure and function in seed plants. Clim. Change Environ. Sustain. 2, 69–104. https://doi.org/10.5958/2320-642X.2014.00001.5 (2014).
    Article  Google Scholar 

    36.
    Iuchi, S. et al. Regulation of drought tolerance by gene manipulation of 9-cis-epoxycarotenoid dioxygenase, a key enzyme in abscisic acid biosynthesis in Arabidopsis. Plant J. 27, 325–333. https://doi.org/10.1046/j.1365-313x.2001.01096.x (2001).
    CAS  Article  PubMed  Google Scholar 

    37.
    Endo, A. et al. Drought induction of Arabidopsis 9-cis-epoxycarotenoid dioxygenase occurs in vascular parenchyma cells. Plant Physiol. 147, 1984–1993 (2008).
    CAS  Article  Google Scholar 

    38
    Toh, S. et al. High temperature-induced abscisic acid biosynthesis and its role in the inhibition of gibberellins action in Arabidopsis seeds. Plant Physiol. 146, 1368–1385 (2008).
    CAS  Article  Google Scholar 

    39.
    Dong, C. H. et al. ADF proteins are involved in the control of flowering and regulate F-actin organization, cell expansion, and organ growth in Arabidopsis. Plant Cell 13, 1333–1346 (2001).
    CAS  Article  Google Scholar 

    40.
    Vantard, M. & Blanchoin, L. Actin polymerization processes in plant cells. Curr. Opin. Plant Biol. 5(6), 502–506 (2002).
    CAS  Article  Google Scholar 

    41.
    Smertenko, A. P. et al. Ser6 in the maize actin-depolymerizing factor, ZmADF3, is phosphorylated by a calcium-stimulated protein kinase and is essential for the control of functional activity. Plant J. 14(2), 187–193 (1988).
    Article  Google Scholar 

    42.
    Webster, J. & Stone, B. A. Isolation, structure and monosaccharide composition of the wall of vegetative parts of Heterozostera tasmanica (Martens ex Aschers) den Hartog. Aquat. Bot. 47, 39–52 (1994).
    CAS  Article  Google Scholar 

    43.
    Olsen J.L., Rouzé, P., Verhelst, B., Lin, Y.-C., Bayer, T., Collen, J., Dattolo, E., De Paoli, E., Dittami, S., Maumus, F., et al. The genome of the seagrass Zostera marina reveals angiosperm adaptation to the sea. Nature 530, 331–335 (2016) https://doi.org/10.1038/nature16548.
    ADS  CAS  Article  PubMed  Google Scholar 

    44.
    Brummel, D. A. Cell wall acidification and its role in Auxin-stimulated growth. J. Exp. Bot. 37(2), 270–276 (1986).
    Article  Google Scholar 

    45.
    Laemmli, U. K. Cleavage of structural proteins during the assembly of the head of bacteriophage T4. Nature 227, 680–685 (1970).
    ADS  CAS  Article  Google Scholar 

    46.
    Shevchenko, A., Tomas, H., Havlis, J., Olsen, J. V. & Mann, M. In-gel digestion for mass spectrometric characterization of proteins and proteomes. Nat. Protoc. 1, 2856–2860 (2007).
    Article  Google Scholar 

    47.
    Lucini, L. & Bernardo, L. Comparison of proteome response to saline and zinc stress in lettuce. Front. Plant Sci. https://doi.org/10.3389/fpls.2015.00240 (2015).
    Article  PubMed  PubMed Central  Google Scholar  More

  • in

    Near-complete loss of fire-resistant primary tropical forest cover in Sumatra and Kalimantan

    1.
    Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).
    CAS  Article  Google Scholar 
    2.
    Austin, K. G., Schwantes, A., Gu, Y. & Kasibhatla, P. S. What causes deforestation in Indonesia? Environ. Res. Lett. 14, 024007 (2019).
    Article  Google Scholar 

    3.
    Tsujino, R., Yumoto, T., Kitamura, S., Djamaluddin, I. & Darnaedi, D. History of forest loss and degradation in Indonesia. Land use policy 57, 335–347 (2016).
    Article  Google Scholar 

    4.
    Turubanova, S., Potapov, P. V., Tyukavina, A. & Hansen, M. C. Ongoing primary forest loss in Brazil, Democratic Republic of the Congo, and Indonesia. Environ. Res. Lett. 13, 074028 (2018).
    Article  Google Scholar 

    5.
    Miettinen, J., Hooijer, A., Wang, J., Shi, C. & Liew, S. C. Peatland degradation and conversion sequences and interrelations in Sumatra. Reg. Environ. Change 12, 729–737 (2012).
    Article  Google Scholar 

    6.
    Margono, B. A., Potapov, P. V., Turubanova, S., Stolle, F. & Hansen, M. C. Primary forest cover loss in Indonesia over 2000–2012. Nat. Clim. Change 4, 730 (2014).
    Article  Google Scholar 

    7.
    Stibig, H. J., Achard, F., Carboni, S., Rasi, R. & Miettinen, J. Change in tropical forest cover of Southeast Asia from 1990 to 2010. Biogeosciences 11, 247–258 (2014).
    Article  Google Scholar 

    8.
    Page, S. E. et al. The amount of carbon released from peat and forest fires in Indonesia during 1997. Nature 420, 61–65 (2002).
    CAS  Article  Google Scholar 

    9.
    Huijnen, V. et al. Fire carbon emissions over maritime southeast Asia in 2015 largest since 1997. Sci. Rep. 6, 26886 (2016).
    CAS  Article  Google Scholar 

    10.
    Koplitz, S. N. et al. Public health impacts of the severe haze in Equatorial Asia in September–October 2015: demonstration of a new framework for informing fire management strategies to reduce downwind smoke exposure. Environ. Res. Lett. 11, 094023 (2016).
    Article  Google Scholar 

    11.
    Crippa, P. et al. Population exposure to hazardous air quality due to the 2015 fires in Equatorial Asia. Sci. Rep. 6, 37074 (2016).
    CAS  Article  Google Scholar 

    12.
    Wijaya, A. R. et al. How can Indonesia achieve its climate change mitigation goal? An analysis of potential emissions reductions from energy and land-use policies. World Resour. Inst. (Washington D.C, 2017).

    13.
    Cochrane, M. A. Fire science for rainforests. Nature 421, 913 (2003).
    CAS  Article  Google Scholar 

    14.
    Page, S. E. & Hooijer, A. In the line of fire: the peatlands of Southeast Asia. Philos. Trans. Royal Soc. B 371, 20150176 (2016).
    Article  CAS  Google Scholar 

    15.
    Miettinen, J., Shi, C. & Liew, S. C. Fire distribution in Peninsular Malaysia, Sumatra and Borneo in 2015 with special emphasis on peatland fires. Environ. Manag. 60, 747–757 (2017).
    Article  Google Scholar 

    16.
    Goldammer, J. G. History of equatorial vegetation fires and fire research in Southeast Asia before the 1997–98 episode: a reconstruction of creeping environmental changes. Mitig. Adapt. Strat. Glob. Chang. 12, 13–32 (2007).
    Article  Google Scholar 

    17.
    Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).
    CAS  Article  Google Scholar 

    18.
    Baker, J. & Spracklen, D. Climate benefits of intact Amazon forests and the biophysical consequences of disturbance. Front. For. Glob. Chang. 2, 47 (2019).
    Article  Google Scholar 

    19.
    Uhl, C., Kauffman, J. B. and Cummings, D. L. Fire in the Venezuelan Amazon 2: environmental conditions necessary for forest fires in the evergreen rainforest of Venezuela. Oikos 53, 176–184 (1988).

    20.
    Dommain, R., Couwenberg, J., Glaser, P. H., Joosten, H. & Suryadiputra, I. N. N. Carbon storage and release in Indonesian peatlands since the last deglaciation. Quat. Sci. Rev. 97, 1–32 (2014).
    Article  Google Scholar 

    21.
    Cole, L. E. S., Bhagwat, S. A. & Willis, K. J. Fire in the swamp forest: palaeoecological insights into natural and human-induced burning in intact tropical peatlands. Front. For. Glob. Chang. 2, 48 (2019).
    Article  Google Scholar 

    22.
    Warren, M., Hergoualc’h, K., Kauffman, J. B., Murdiyarso, D. & Kolka, R. An appraisal of Indonesia’s immense peat carbon stock using national peatland maps: uncertainties and potential losses from conversion. Carbon balanc. management 12, 12 (2017).
    Article  CAS  Google Scholar 

    23.
    Page, S. E., Rieley, J. O. & Banks, C. J. Global and regional importance of the tropical peatland carbon pool. Glob. chang. biol. 17, 798–818 (2011).
    Article  Google Scholar 

    24.
    Page, S. E. et al. A record of late pleistocene and holocene carbon accumulation and climate change from an equatorial peat bog (Kalimantan, Indonesia): implications for past, present and future carbon dynamics. J. Quat. Sci. 19, 625–635 (2004).
    Article  Google Scholar 

    25.
    Schultz, N. M., Lawrence, P. J. & Lee, X. Global satellite data highlights the diurnal asymmetry of the surface temperature response to deforestation. J. Geophys. Res. 122, 903–917 (2017).
    Article  Google Scholar 

    26.
    Sabajo, C. R. et al. Expansion of oil palm and other cash crops causes an increase of the land surface temperature in the Jambi province in Indonesia. Biogeosciences 14, 4619–4635 (2017).
    CAS  Article  Google Scholar 

    27.
    McAlpine, C. A. et al. Forest loss and Borneo’s climate. Environ. Res. Lett. 13, 044009 (2018).
    Article  Google Scholar 

    28.
    Hardwick, S. R. et al. The relationship between leaf area index and microclimate in tropical forest and oil palm plantation: forest disturbance drives changes in microclimate. Agric. For. Meteorol. 201, 187–195 (2015).
    Article  Google Scholar 

    29.
    Jauhiainen, J., Kerojoki, O., Silvennoinen, H., Limin, S. & Vasander, H. Heterotrophic respiration in drained tropical peat is greatly affected by temperature—a passive ecosystem cooling experiment. Environ. Res. Lett. 9, 105013 (2014).
    Article  CAS  Google Scholar 

    30.
    Miettinen, J., Shi, C. & Liew, S. C. Land cover distribution in the peatlands of Peninsular Malaysia, Sumatra and Borneo in 2015 with changes since 1990. Glob. Ecol. Conserv. 6, 67–78 (2016).
    Article  Google Scholar 

    31.
    Hoscilo, A., Page, S. E., Tansey, K. J. & Rieley, J. O. Effect of repeated fires on land-cover change on peatland in southern Central Kalimantan, Indonesia, from 1973 to 2005. Int. J. Wildland Fire 20, 578–588 (2011).
    Article  Google Scholar 

    32.
    Laurance, W. F. Do edge effects occur over large spatial scales? Trends Ecol. Evol. 15, 134–135 (2000).
    CAS  Article  Google Scholar 

    33.
    Cochrane, M. A. & Laurance, W. F. Fire as a large-scale edge effect in Amazonian forests. J. Tropi. Ecol. 18, 311–325 (2002).
    Article  Google Scholar 

    34.
    Laurance, W. F., Laurance, S. G. & Delamonica, P. Tropical forest fragmentation and greenhouse gas emissions. For. Ecol. Manag. 110, 173–180 (1998).
    Article  Google Scholar 

    35.
    Curran, L. M. et al. Impact of El Nino and logging on canopy tree recruitment in Borneo. Science 286, 2184–2188 (1999).
    CAS  Article  Google Scholar 

    36.
    Chaplin-Kramer, R. et al. Degradation in carbon stocks near tropical forest edges. Nat. Commun. 6, 10158 (2015).
    CAS  Article  Google Scholar 

    37.
    Brinck, K. et al. High resolution analysis of tropical forest fragmentation and its impact on the global carbon cycle. Nat. Commun. 8, 1–6 (2017).
    Article  CAS  Google Scholar 

    38.
    Briant, G., Gond, V. & Laurance, S. G. Habitat fragmentation and the desiccation of forest canopies: a case study from eastern Amazonia. Biol. conserv. 143, 2763–2769 (2010).
    Article  Google Scholar 

    39.
    Didham, R. K. & Lawton, J. H. Edge structure determines the magnitude of changes in microclimate and vegetation structure in tropical forest fragments. Biotropica 31, 17–30 (1999).
    Google Scholar 

    40.
    Hooijer, A. et al. Subsidence and carbon loss in drained tropical peatlands. Biogeosciences 9, 1053 (2012).
    CAS  Article  Google Scholar 

    41.
    Evans, C. D. et al. Rates and spatial variability of peat subsidence in Acacia plantation and forest landscapes in Sumatra, Indonesia. Geoderma 338, 410–421 (2019).
    Article  Google Scholar 

    42.
    Cattau, M. E. et al. Sources of anthropogenic fire ignitions on the peat-swamp landscape in Kalimantan, Indonesia. Glob. Environ. Chang. 39, 205–219 (2016).
    Article  Google Scholar 

    43.
    Wooster, M. J., Perry, G. L. W. and Zoumas, A. Fire, drought and El Niño relationships on Borneo (Southeast Asia) in the pre-MODIS era (1980–2000). Biogeosciences 9, (2012)

    44.
    Spessa, A. C. et al. Seasonal forecasting of fire over Kalimantan, Indonesia. Nat. Hazards Earth Syst. Sci. 15, 429–442 (2015).
    Article  Google Scholar 

    45.
    Field, R. D. et al. Indonesian fire activity and smoke pollution in 2015 show persistent nonlinear sensitivity to El Niño-induced drought. Proc. Natl Acad. Sci. 113, 9204–9209 (2016).
    CAS  Article  Google Scholar 

    46.
    Langner, A. & Siegert, F. Spatiotemporal fire occurrence in Borneo over a period of 10 years. Glob. Chang. Biol. 15, 48–62 (2009).
    Article  Google Scholar 

    47.
    Pan, X., Chin, M., Ichoku, C. M. & Field, R. D. Connecting Indonesian fires and drought with the type of El Niño and phase of the Indian Ocean Dipole during 1979–2016. J. Geophys. Res. 123, 7974–7988 (2018).
    Google Scholar 

    48.
    Konecny, K. et al. Variable carbon losses from recurrent fires in drained tropical peatlands. Glob. Chang. Biol. 22, 1469–1480 (2016).
    Article  Google Scholar 

    49.
    Miettinen, J., Hooijer, A., Vernimmen, R., Liew, S. C. & Page, S. E. From carbon sink to carbon source: extensive peat oxidation in insular Southeast Asia since 1990. Environ. Res. Lett. 12, 024014 (2017).
    Article  CAS  Google Scholar 

    50.
    Langner, A., Miettinen, J. & Siegert, F. Land cover change 2002–2005 in Borneo and the role of fire derived from MODIS imagery. Glob. Chang. Biol. 13, 2329–2340 (2007).
    Article  Google Scholar 

    51.
    van der Werf, G. R. et al. Climate regulation of fire emissions and deforestation in equatorial Asia. Proc. Natl Acad. Sci. 105, 20350–20355 (2008).
    Article  Google Scholar 

    52.
    Tacconi, L. Preventing fires and haze in Southeast Asia. Nat. Clim. Chang. 6, 640 (2016).
    Article  Google Scholar 

    53.
    Wahyunto, R. S. & Suparto, S. H. Maps of area of peatland distribution and carbon content in Kalimantan, 2000–2002. Wetl. Int.-Indones. Program. Wildl. Habitat Can. (WHC) Bogor. (2004).

    54.
    Purnomo A. Protecting Indonesia’s Forests, Pros-Cons Policy of Moratorium on Forests and Peatlands (Kepustakaan Populer Gramedia, Jakarta, Indonesia, 2012).

    55.
    Normile, D. Indonesia’s fires are bad, but new measures prevented them from becoming worse. Sci. Mag. https://www.sciencemag.org/news/2019/10/indonesias-fires-are-bad-new-measures-prevented-them-becoming-worse (2019).

    56.
    Purnomo, H. et al. Fire economy and actor network of forest and land fires in Indonesia. For. Policy Econ. 78, 21–31 (2017).
    Article  Google Scholar 

    57.
    Seymour, F. Indonesia Reduces Deforestation, Norway to Pay Up. World Resources Institute. https://www.wri.org/blog/2019/02/indonesia-reduces-deforestation-norway-pay (2019).

    58.
    Haddad, N. M. et al. Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci. Adv. 1, 1500052 (2015).
    Article  Google Scholar 

    59.
    Watson, J. E. et al. The exceptional value of intact forest ecosystems. Nat. Ecol. Evol. 2, 599–610 (2018).
    Article  Google Scholar 

    60.
    Gaveau, D. L. et al. Major atmospheric emissions from peat fires in Southeast Asia during non-drought years: evidence from the 2013 Sumatran fires. Sci. Rep. 4, 6112 (2014).
    CAS  Article  Google Scholar 

    61.
    Wooster, M. et al. New tropical peatland gas and particulate emissions factors indicate 2015 Indonesian fires released far more particulate matter (but less methane) than current inventories imply. Remote Sens. 10, 495 (2018).
    Article  Google Scholar 

    62.
    Taufik, M. et al. Amplification of wildfire area burnt by hydrological drought in the humid tropics. Nat. Clim. Chang. 7, 428–431 (2017).
    Article  Google Scholar 

    63.
    Margono, B. A. et al. Mapping and monitoring deforestation and forest degradation in Sumatra (Indonesia) using Landsat time series data sets from 1990 to 2010. Environ. Res. Lett. 7, 034010 (2012).
    Article  Google Scholar 

    64.
    Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).
    CAS  Article  Google Scholar 

    65.
    Giglio, L., Schroeder, W. & Justice, C. O. The collection 6 MODIS active fire detection algorithm and fire products. Remote Sens. Environ. 178, 31–41 (2016).
    Article  Google Scholar 

    66.
    Lohberger, S., Stängel, M., Atwood, E. C. & Siegert, F. Spatial evaluation of Indonesia’s 2015 fire‐affected area and estimated carbon emissions using Sentinel‐1. Glob. Chang. Biol. 24, 644–654 (2015).
    Article  Google Scholar  More

  • in

    An altered microbiome in urban coyotes mediates relationships between anthropogenic diet and poor health

    1.
    Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005).
    ADS  CAS  PubMed  Article  Google Scholar 
    2.
    Ellis, E. C., Goldewijk, K. K., Siebert, S., Lightman, D. & Ramankutty, N. Anthropogenic transformation of the biomes, 1700 to 2000. Glob. Ecol. Biogeogr. 19, 589–606 (2010).
    Google Scholar 

    3.
    Concepción, E. D., Moretti, M., Altermatt, F., Nobis, M. P. & Obrist, M. K. Impacts of urbanisation on biodiversity: the role of species mobility, degree of specialisation and spatial scale. Oikos 124, 1571–1582 (2015).
    Article  Google Scholar 

    4.
    Lowry, H., Lill, A. & Wong, B. B. M. Behavioural responses of wildlife to urban environments. Biol. Rev. 88, 537–549 (2013).
    PubMed  Article  Google Scholar 

    5.
    Callaghan, C. T. et al. Generalists are the most urban-tolerant of birds: a phylogenetically controlled analysis of ecological and life history traits using a novel continuous measure of bird responses to urbanization. Oikos 128, 845–858 (2019).
    Article  Google Scholar 

    6.
    Ducatez, S., Sayol, F., Sol, D. & Lefebvre, L. Are urban vertebrates city specialists, artificial habitat exploiters, or environmental generalists? Integr. Comp. Biol. 58, 929–938 (2018).
    PubMed  Article  Google Scholar 

    7.
    Murray, M. H. et al. City sicker? A meta-analysis of wildlife health and urbanization. Front. Ecol. Environ. 17, 575–583 (2019).
    Article  Google Scholar 

    8.
    Lyons, J., Mastromonaco, G., Edwards, D. B. & Schulte-Hostedde, A. I. Fat and happy in the city: eastern chipmunks in urban environments. Behav. Ecol. 28, 1464–1471 (2017).
    Article  Google Scholar 

    9.
    Meillère, A. et al. Corticosterone levels in relation to trace element contamination along an urbanization gradient in the common blackbird (Turdus merula). Sci. Total Environ. 566–567, 93–101 (2016).
    ADS  PubMed  Article  CAS  Google Scholar 

    10.
    Soto-Calderón, I., Acevedo-Garcés, Y., Álvarez-Cardona, J., Hernandez, C. & García, G. Physiological and parasitological implications of living in a city: the case of the white-footed tamarin (Saguinus leucopus). Am. J. Primatol. 78, (2016).

    11.
    Sillero-Zubiri, C., Sukumar, R. & Treves, A. Living with wildlife: the roots of conflict and the solutions. In Key Topics in Conservation Biology (eds. MacDonald, D. & Service, K.) 255–272 (2006).

    12.
    Muegge, B. D. et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science 332, 970–974 (2011).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    13.
    Hanning, I. & Diaz-Sanchez, S. The functionality of the gastrointestinal microbiome in non-human animals. Microbiome 3, 51 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    14.
    Tremaroli, V. & Bäckhed, F. Functional interactions between the gut microbiota and host metabolism. Nature 489, 242–249 (2012).
    ADS  CAS  PubMed  Article  Google Scholar 

    15.
    Pickard, J. M., Zeng, M. Y., Caruso, R. & Núñez, G. Gut microbiota: role in pathogen colonization, immune responses, and inflammatory disease. Immunol. Rev. 279, 70–89 (2017).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    16.
    Mockler, B. K., Kwong, W. K., Moran, N. A. & Koch, H. Microbiome structure influences infection by the parasite Crithidia bombi in bumble bees. Appl. Environ. Microbiol. 84, e02335-e2417 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    17.
    Suzuki, T. A. Links between natural variation in the microbiome and host fitness in wild mammals. Integr. Comp. Biol. 57, 756–769 (2017).
    CAS  PubMed  Article  Google Scholar 

    18.
    Kirchoff, N. S., Udell, M. A. & Sharpton, T. J. The gut microbiome correlates with conspecific aggression in a small population of rescued dogs (Canis familiaris). PeerJ 7, e6103 (2019).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    19.
    Walter, J. Ecological role of lactobacilli in the gastrointestinal tract: implications for fundamental and biomedical research. Appl. Environ. Microbiol. 74, 4985–4996 (2008).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    20.
    Teyssier, A. et al. Inside the guts of the city: urban-induced alterations of the gut microbiota in a wild passerine. Sci. Total Environ. 612, 1276–1286 (2018).
    ADS  CAS  PubMed  Article  Google Scholar 

    21.
    Murray, M. H. et al. Gut microbiome shifts with urbanization and potentially facilitates a zoonotic pathogen in a wading bird. PLoS ONE 15, 1–16 (2020).
    Google Scholar 

    22.
    Phillips, J. N., Berlow, M. & Derryberry, E. P. The effects of landscape urbanization on the gut microbiome: an exploration into the gut of urban and rural white-crowned sparrows. Front. Ecol. Evol. 6, 148 (2018).
    Article  Google Scholar 

    23.
    Teyssier, A. et al. Diet contributes to urban-induced alterations in gut microbiota: experimental evidence from a wild passerine. Proc. R. Soc. B Biol. Sci. 287, (2020).

    24.
    Stothart, M. R., Palme, R. & Newman, A. E. M. It’s what’s on the inside that counts: stress physiology and the bacterial microbiome of a wild urban mammal. Proc. R. Soc. B Biol. Sci. 286, (2019).

    25.
    Becker, C. G., Longo, A. V., Haddad, C. F. B. & Zamudio, K. R. Land cover and forest connectivity alter the interactions among host, pathogen and skin microbiome. Proc. R. Soc. B Biol. Sci. 284, 20170582 (2017).
    Article  Google Scholar 

    26.
    Bestion, E. et al. Climate warming reduces gut microbiota diversity in a vertebrate ectotherm. Nat. Ecol. Evol. 1, 0161 (2017).
    Article  Google Scholar 

    27.
    Barelli, C. et al. Habitat fragmentation is associated to gut microbiota diversity of an endangered primate: implications for conservation. Sci. Rep. 5, 14862 (2015).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    28.
    Trevelline, B. K., Fontaine, S. S., Hartup, B. K. & Kohl, K. D. Conservation biology needs a microbial renaissance: a call for the consideration of host-associated microbiota in wildlife management practices. Proc. R. Soc. B Biol. Sci. 286, (2019).

    29.
    Nelson, T. M., Rogers, T. L., Carlini, A. R. & Brown, M. V. Diet and phylogeny shape the gut microbiota of Antarctic seals: a comparison of wild and captive animals. Environ. Microbiol. 15, 1132–1145 (2013).
    CAS  PubMed  Article  Google Scholar 

    30.
    Wasimuddin, et al. Gut microbiomes of free-ranging and captive Namibian cheetahs: diversity, putative functions and occurrence of potential pathogens. Mol. Ecol. 26, 5515–5527 (2017).
    CAS  PubMed  Article  Google Scholar 

    31.
    Amato, K. R. et al. Evolutionary trends in host physiology outweigh dietary niche in structuring primate gut microbiomes. ISME J. 13, 576–587 (2019).
    CAS  PubMed  Article  Google Scholar 

    32.
    Gehrt, S. D. & Riley, S. P. D. Coyotes (Canis latrans). in Urban Carnivores: Ecology, Conflict, and Conservation (eds. Gehrt, S. D., Riley, S. P. D. & Cypher, B. L.) 79–95 (2010).

    33.
    Breck, S. W., Poessel, S. A., Mahoney, P. & Young, J. K. The intrepid urban coyote: a comparison of bold and exploratory behavior in coyotes from urban and rural environments. Sci. Rep. 9, 2104 (2019).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    34.
    Gier, H. T. Coyotes in Kansas. (1968).

    35.
    Murray, M. H. et al. Greater consumption of protein-poor anthropogenic food by urban relative to rural coyotes increases diet breadth and potential for human-wildlife conflict. Ecography 38, 001–008 (2015).
    Article  Google Scholar 

    36.
    Massolo, A., Liccioli, S., Budke, C. & Klein, C. Echinococcus multilocularis in North America: the great unknown. Parasite 21, 73 (2014).
    PubMed  PubMed Central  Article  Google Scholar 

    37.
    Murray, M. H., Edwards, M. A., Abercrombie, B. & St. Clair, C. C. Poor health is associated with use of anthropogenic resources in an urban carnivore. Proc. R. Soc. B Biol. Sci. 282, 20150009 (2015).

    38.
    Murray, M. H., Hill, J., Whyte, P. & St. Clair, C. C. Urban compost attracts coyotes, contains toxins, and may promote disease in urban-adapted wildlife. Ecohealth 13, 285–292 (2016).

    39.
    Luong, L. T., Chambers, J. L., Moizis, A., Stock, T. & St. Clair, C. Helminth parasites and zoonotic risk associated with urban coyotes (Canis latrans) in Alberta, Canada. J. Helminthol. 94, e25 (2020).

    40.
    Corbin, E. et al. Spleen mass as a measure of immune strength in mammals. Mamm. Rev. 38, 108–115 (2008).
    Article  Google Scholar 

    41.
    Newsome, S. D., Ralls, K., Van Horn Job, C., Fogel, M. L. & Cypher, B. L. Stable isotopes evaluate exploitation of anthropogenic foods by the endangered San Joaquin kit fox (Vulpes macrotis mutica). J. Mammol. 91, 1313–1321 (2010).

    42.
    Huot, J., Poulle, M. & Crate, M. Evaluation of several indices for assessment of coyote (Canis latrans) body composition. Can. J. Zool. 73, 1620–1624 (1995).
    Article  Google Scholar 

    43.
    Tucker, C. M. et al. A guide to phylogenetic metrics for conservation, community ecology and macroecology. Biol. Rev. 92, 698–715 (2016).
    PubMed  Article  Google Scholar 

    44.
    Reese, A. T. & Dunn, R. R. Drivers of microbiome biodiversity: a review of general rules, feces, and ignorance. MBio 9, e01294-e1318 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    45.
    Pilla, R. & Suchodolski, J. S. The role of the canine gut microbiome and metabolome in health and gastrointestinal disease. Front. Vet. Sci. 6, 498 (2020).
    PubMed  PubMed Central  Article  Google Scholar 

    46.
    Conlon, M. A. & Bird, A. R. The impact of diet and lifestyle on gut microbiota and human health. Nutrition 7, 17–44 (2015).
    Google Scholar 

    47.
    Makki, K., Deehan, E. C., Walter, J. & Bäckhed, F. The impact of dietary fiber on gut microbiota in host health and disease. Cell Host Microbe 23, 705–715 (2018).
    CAS  PubMed  Article  Google Scholar 

    48.
    Schnorr, S. L. et al. Gut microbiome of the Hadza hunter-gatherers. Nat. Commun. 5, 3654 (2014).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    49.
    Vieco-Saiz, N. et al. Benefits and inputs from lactic acid bacteria and their bacteriocins as alternatives to antibiotic growth promoters during food-animal production. Front. Microbiol. 10, 1–17 (2019).
    Article  Google Scholar 

    50.
    Karasov, W. H. & Douglas, A. E. Comparative digestive physiology. Comp. Physiol. 3, 741–783 (2013).
    Google Scholar 

    51.
    Wang, T. et al. Structural segregation of gut microbiota between colorectal cancer patients and healthy volunteers. ISME J. 6, 320–329 (2012).
    CAS  PubMed  Article  Google Scholar 

    52.
    AlShawaqfeh, M. K. et al. A dysbiosis index to assess microbial changes in fecal samples of dogs with chronic inflammatory enteropathy. FEMS Microbiol. Ecol. 93, 1–8 (2017).
    Article  CAS  Google Scholar 

    53.
    Beldomenico, P. M. & Begon, M. Disease spread, susceptibility and infection intensity: vicious circles? Trends Ecol. Evol. 25, 21–27 (2010).
    PubMed  Article  Google Scholar 

    54.
    Newsome, S. D., Garbe, H. M., Wilson, E. C. & Gehrt, S. D. Individual variation in anthropogenic resource use in an urban carnivore. Oecologia 178, 115–128 (2015).
    ADS  PubMed  Article  Google Scholar 

    55.
    Henderson, G. et al. Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range. Sci. Rep. 5, 14567 (2015).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    56.
    Brennan, C. A. & Garrett, W. S. Fusobacterium nucleatum – symbiont, opportunist and oncobacterium. Nat. Rev. Microbiol. 17, 156–166 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    57.
    Bermingham, E. N., Maclean, P., Thomas, D. G., Cave, N. J. & Young, W. Key bacterial families (Clostridiaceae, Erysipelotrichaceae and Bacteroidaceae) are related to the digestion of protein and energy in dogs. PeerJ 5, e3019 (2017).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    58.
    Alessandri, G. et al. Metagenomic dissection of the canine gut microbiota: insights into taxonomic, metabolic and nutritional features. Environ. Microbiol. 21, 1331–1343 (2019).
    CAS  PubMed  Article  Google Scholar 

    59.
    Schmidt, M. et al. The fecal microbiome and metabolome differs between dogs fed Bones and Raw Food (BARF) diets and dogs fed commercial diets. PLoS ONE 13, e0201279 (2018).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    60.
    Sandri, M., Dal Monego, S., Conte, G., Sgorlon, S. & Stefanon, B. Raw meat based diet influences faecal microbiome and end products of fermentation in healthy dogs. BMC Vet. Res. 13, 1–11 (2017).
    Google Scholar 

    61.
    Moon, C. D., Cookson, A. L., Young, W., Maclean, P. H. & Bermingham, E. N. Metagenomic insights into the roles of Proteobacteria in the gastrointestinal microbiomes of healthy dogs and cats. Microbiologyopen 7, e677 (2018).
    Article  Google Scholar 

    62.
    Wu, X. et al. Analysis and comparison of the wolf microbiome under different environmental factors using three different data of next generation sequencing. Sci. Rep. 7, 11332 (2017).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    63.
    Wang, B. & Wang, X.-L. Species diversity of fecal microbial flora in Canis lupus familiaris infected with canine parvovirus. Vet. Microbiol. 237, 108390 (2019).
    PubMed  Article  Google Scholar 

    64.
    Chen, L. et al. NLRP12 attenuates colon inflammation by maintaining colonic microbial diversity and promoting protective commensal bacterial growth. Nat. Immunol. 18, 541–551 (2017).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    65.
    Martínez, I. et al. Gut microbiome composition is linked to whole grain-induced immunological improvements. ISME J. 7, 269–280 (2013).
    PubMed  Article  CAS  Google Scholar 

    66.
    Liu, Y. et al. Splenectomy leads to amelioration of altered gut microbiota and metabolome in liver cirrhosis patients. Front. Microbiol. 9, 1–13 (2018).
    Article  Google Scholar 

    67.
    Demas, G. E., Zysling, D. A., Beechler, B. R., Muehlenbein, M. P. & French, S. S. Beyond phytohaemagglutinin: assessing vertebrate immune function across ecological contexts. J. Anim. Ecol. 80, 710–730 (2011).
    PubMed  Article  Google Scholar 

    68.
    Sugden, S. A., St. Clair, C. C. & Stein, L. Y. Individual and site-specific variation in a biogeographical profile of the coyote intestinal microbiota. Microb. Ecol. (2020).

    69.
    David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).
    ADS  CAS  PubMed  Article  Google Scholar 

    70.
    Leung, J. M., Graham, A. L. & Knowles, S. C. L. Parasite-microbiota interactions with the vertebrate gut: synthesis through an ecological lens. Front. Microbiol. 9, 843 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    71.
    Ezenwa, V. O., Gerardo, N. M., Inouye, D. W., Medina, M. & Xavier, J. B. Animal behavior and the microbiome. Science 338, 198–199 (2012).
    ADS  CAS  PubMed  Article  Google Scholar 

    72.
    Stewart, R. E. A., Stewart, B. E., Stirling, I. & Street, E. Counts of growth layer groups in cementum and dentine in ringed seals. Mar. Mammal Sci. 12, 383–401 (1996).
    Article  Google Scholar 

    73.
    Linhart, S. B. & Knowlton, F. F. Determining age of coyotes by tooth cementum layers. J. Wildl. Manage. 31, 362–365 (1967).
    Article  Google Scholar 

    74.
    Jahren, A. H. & Kraft, R. A. Carbon and nitrogen stable isotopes in fast food: signatures of corn and confinement. Proc. Natl. Acad. Sci. 105, 17855–17860 (2008).
    ADS  CAS  PubMed  Article  Google Scholar 

    75.
    Parnell, A. C. simmr: a stable isotope mixing model. (2019).

    76.
    Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. 108, 4516–4522 (2011).
    ADS  CAS  PubMed  Article  Google Scholar 

    77.
    Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).
    Article  Google Scholar 

    78.
    Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 226 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    79.
    Trachsel, D., Deplazes, P. & Mathis, A. Identification of taeniid eggs in the faeces from carnivores based on multiplex PCR using targets in mitochondrial DNA. Parasitology 134, 911–920 (2007).
    CAS  PubMed  Article  Google Scholar 

    80.
    R Core Team. R: A language and environment for statistical computing. (2019).

    81.
    Chao, A. et al. Rarefaction and extrapolation of phylogenetic diversity. Methods Ecol. Evol. 6, 380–388 (2015).
    Article  Google Scholar 

    82.
    Kembel, S. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).
    CAS  Article  Google Scholar 

    83.
    Giam, X. & Olden, J. D. Quantifying variable importance in a multimodel inference framework. Methods Ecol. Evol. 7, 388–397 (2016).
    Article  Google Scholar 

    84.
    Cade, B. S. Model averaging and muddled multimodel inferences. Ecology 96, 2370–2382 (2015).
    PubMed  Article  Google Scholar 

    85.
    Fernandes, A., Macklaim, J. M., Linn, T., Reid, G. & Gloor, G. B. ANOVA-like differential expression (ALDEx) analysis for mixed population RNA-Seq. PLoS ONE 8, e67019 (2013).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar  More

  • in

    2000 Year-old Bogong moth (Agrotis infusa) Aboriginal food remains, Australia

    Ethnographic accounts from around the world have reported the widespread use of insects as food by people1,2,3. In some cases, such as among the Shoshone and other Great Basin tribes of the U.S., swarms of grasshoppers and crickets were driven into pits and blankets4, while among the Paiute the larvae of Pandora moths (Coloradia pandora lindseyi) were smoked out of trees to fall into prepared trenches, where they would be cooked5. Across the world, insects could be mass-harvested, often seasonally, offering high nutritional value especially in fat, protein and vitamins6. The harvesting of insects in the past has ranged from opportunities to feed large communal gatherings during times of plenty, to more individualistic economic pursuits such as in the search for delicacies or the exploitation of low-ranked resources when other foods were scarce or depleted7,8,9. Irrespective of the catch, insects often represented an important component of the diet, and of the reliability and thus dependability of locales as resource zones, with implications for social scheduling and cultural practice. However, a paucity of archaeological studies of insect food remains has resulted in a downplay or omission of the use of insects from archaeological narratives and deep-time community histories10.
    In Australia, a wide range of insects is known to have been eaten by Aboriginal groups, in particular the larvae (‘witchetty grubs’) of cossid moths (especially Endoxyla leucomochla) in arid and semi-arid areas11,12,13. Of particular interest to archaeologists and behavioural ecologists has been the seasonal consumption of Bogong moths by mass gatherings of Aboriginal groups in the southern portions of the Eastern Uplands14 (Fig. 1). However, no conclusive archaeological evidence has ever been reported for the processing or use of Bogong moths.
    Figure 1

    (A) Bogong moth, Agrotis infusa (photo: Ajay Narendra). (B) Thousands of moths per square metre aestivating on a rock surface (photo: Eric Warrant).

    Full size image

    The Cloggs Cave grindstone
    Cloggs Cave is located 72 m above sea level in the southern foothills of the Australian Alps, in the lands of the Krauatungalung clan of the GunaiKurnai Aboriginal peoples of southeastern Australia (Fig. 2). The cave is a small, 12 m long × 5 m wide × 6.8 m high limestone karst formation that is today entered through a walk-through opening on the side of a cliff (Fig. 3). Indirect sunlight dimly illuminates the cave for much of the day (Supplementary Fig. S1).
    Figure 2

    Location of Cloggs Cave and the area of the GunaiKurnai Land and Waters Aboriginal Corporation, at the southern foothills of the Australian Alps. Esri ArcMap 10.5 (https://desktop.arcgis.com/en/arcmap/) and Adobe Illustrator CC 2017 (21.0) (https://helpx.adobe.com/au/illustrator/release-note/illustrator-cc-2017-21-0-release-notes.html) were used by CartoGIS Services, College of Asia and the Pacific at the Australian National University, to create the map.

    Full size image

    Figure 3

    Cloggs Cave cliffline above the Buchan River flood plain, showing location of cave entrance (white rectangle) (photo: Bruno David).

    Full size image

    Archaeological excavations were first undertaken in 1971–197214, followed by a new program of excavations in 2019–2020, initiated by the GunaiKurnai Land and Waters Aboriginal Corporation and directed by Bruno David. The new excavations were aimed at better determining the site’s stratigraphy and the antiquity of Aboriginal occupation (Supplementary Fig. S2). An intensive dating programme showed that the oldest excavated evidence for human activity dates to between 19,330–19,730 cal BP (median age of 19,530 cal BP; cal BP = before AD1950) and 20,590–23,530 cal BP (median age of 21,690 cal BP) (all calibrated radiocarbon ages in the text are presented at 95.4% probability range. See “Methods”; Supplementary Fig. S3)15,16,17.
    During the 2019 excavations, a small, flat grindstone was found. The finely stratified hearth layers of stratigraphic unit (SU) 2 in which it was found were radiocarbon-dated to 1567–1696 cal BP at their top (uncalibrated: 1724 ± 16 BP; median age of 1632 cal BP) and 2002–2117 cal BP at their base (uncalibrated: 2091 ± 16 BP; median age of 2062 cal BP). The grindstone therefore dates to between 1600 and 2100 years ago (see “Methods”; Supplementary Figs. S3 and S4)17. No other grindstone has been found at Cloggs Cave.
    The grindstone is a tabular fragment of sandstone with two flat and parallel ground surfaces (Surfaces A and B), in the form of a flat dish (Fig. 4). It measures 10.5 cm long × 8.3 cm wide × 2.2 cm thick and weighs 304 g. The outer, intact margin is elliptical in plan view; the other three margins indicate old breaks that have been subsequently worn from use. Therefore, prior to its deposition at Cloggs Cave, the grindstone had been used in its current form.
    Figure 4

    The Cloggs Cave grindstone. (A) Surface A, with the accretion that formed across parts of the surface after its use. (B) Surface B. (C) Margin A. (D) Margin B. (E) Narrow end. The numbers in circles are the residue sample numbers; the ‘control’ samples are in areas where grinding did not take place (photos: Richard Fullagar).

    Full size image

    To understand how the grindstone was used, we undertook use-wear and residue analyses (see “Methods”). The central area of both its surfaces contain fine unidirectional striations (Supplementary Figs. S5A and S5B), a lowered but not levelled topography, and areas of missing or ripped quartz grains (Supplementary Figs. S5C and S5D). Its use to shape ground stone axes is an unlikely function because the Cloggs Cave grindstone surfaces are relatively flat with only very slight concavities, and the lowered surface topography (Fig. 4) lacks broad grooves typical of axe grinding.
    When viewed at lower (up to 5 ×) magnification under a stereozoom microscope with a point source of light, each surface appears relatively rough compared with grindstones used for processing seeds, which, in Australia, tend to be highly smoothed and polished18,19. There are numerous ‘pits’ where sand grains have been plucked from the surface during use (Supplementary Fig. S5D). The presence of a lowered surface topography (Supplementary Fig. S5C) with a lack of smooth, developed polish suggests that the stone was not used to process siliceous plants.
    The repeated mechanical action of grinding has been shown to force residues into the voids and interstitial spaces of ground surfaces, where they become trapped20,21,22. Residue analyses conducted on grindstones worldwide have relied on microscopic observations of individual residue morphologies. However, visually diagnostic features can be altered by the mechanical forces of grinding, heat, and contact with water and various environmental factors, which can cause residues to swell or become amorphous21,22,23,24. The distinctiveness of residue identifications can be enhanced significantly with the introduction of biochemical staining that can be observed under high-power microscopy and is best used in conjunction with microscopic use-wear analysis and identification of residue morphologies22.
    We extracted nine samples, or ‘lifts’, for residue analysis from across Surface A and Surface B of the Cloggs Cave grindstone, including a control sample from an unworked part of each surface (Fig. 4; see “Methods”). These samples were analysed using a recently developed biochemical staining technique that enables residues to be identified from colorimetric changes occurring at a cellular level, rather than relying solely on structural features (see “Methods”)22. We used the collagen stain Picrosirius Red (PSR) to differentiate between plant and animal residues (see “Methods”). When PSR comes into contact with collagen (a protein unique to animals), it reacts to produce clear and distinctive staining and enhanced birefringence in cross-polarised light22,25.
    Residues extracted from the grindstone
    A range of residues were identified in the lifts, including amorphous collagen, collagen fibres, collagen structures, partially woven collagen, possible bone-like fragments, moth wing segments, a possible moth hind leg, amorphous cellulose, wood-like structures with pits, carbonised material, bordered pits and minerals (see below).
    We found collagenous residues in mid-range densities across Samples 1 and 4 from Surface B and across Sample 5 from Surface A (Supplementary Fig. S6). These extractions were taken from central areas across each modified surface. In all cases, the frequency of the collagenous residues was approximately three times greater than the collagenous residues associated with the control samples. Residues include damaged collagen fibres of varying thicknesses, including some reticular fibres.
    Woven collagen structures clearly show birefringence in cross-polarised light across Sample 1. Woven collagen, which forms quickly, is mechanically weak and usually associated with immature bone. Although woven collagen may persist as tendon and ligament attachments to bone, it is generally replaced by organised parallel collagen fibre bundles at skeleton maturity26. Collagen fibrils are found in the connective tissues of vertebrates as well as in invertebrates such as insects27, and may be present as individual strands, woven structures or parallel bundles, including among the Lepidoptera (moths and butterflies)28.
    The density and combination of collagenous residues on the Cloggs Cave grindstone indicates that it was used to process fauna. A variety of collagenous materials (including woven collagen) were found in association with carbonised residues across Sample 2, which was extracted from a crystalline layer. The residues present on Samples 1 and 2 suggest that an insect or immature vertebrate was prepared and cooked using the grindstone.
    We identified a moderate density of carbonised plant residues across Sample 2, in particular, wood-like structures with pits. These ranged from being partially to completely carbonised. Partially carbonised residues were also seen across Sample 4. In addition, bordered pits in small clusters were identified, along with pits within the carbonised structures. Bordered pits are cavities that are essential components in the water-transport system of higher-order plants and are found in the lignified cell walls of xylem conduits (vessels and tracheids). The pit membrane allows water to pass between xylem conduits, but limits the spread of embolism and vascular pathogens in the xylem29. Small quantities of lignin were also present (see “Methods”). Lignin is found in the cell walls of vascular plants (especially in wood and bark) and is responsible for the rigidity of plant structures.
    The residues identified via biochemical staining are consistent with the use of twigs and bark as fuel for fires such as those of the microstratified ashy layers in which the grindstone was found (see Supplementary Fig. S3)17. Partially carbonised wood-like material was also noted across Sample 5. The density and distribution of carbonised residues varies across extractions. Our observations suggest either that: (a) the stone has been placed in or near fires; or (b) ash, embers or fires of varying heat were placed or lit across the stone, for varied durations of time.
    We identified especially high densities (frequency of residue particles per unit volume of sample) of amorphous cellulose across Samples 1, 2, 4 and 5 (Supplementary Fig. S7). The presence of partially carbonised amorphous cellulose indicates that the plant residues were associated with fire. While the high density is indicative of a plant-processing event, there is no evidence of combinations of plant residues normally expected from plant processing. In particular, no starch grain or phytolith was seen in any of the extractions. While low heat can damage starch and cause its structure to be disrupted and its characteristic extinction-cross to be lost, low heat does not completely destroy starch visibility30. Similarly, phytoliths can be reshaped but not destroyed by fire31. The presence of animal and mineral residues but absence of starches and phytoliths is thus interpreted as a true absence of plant processing activities rather than a taphonomic effect of environmental factors negatively impacting their preservation.
    We found a high density of variably carbonised insect wings in Sample 6 (Surface A), and lower densities in Samples 2 and 4. These wing fragments contain regular patterning or structure and exhibit distinct birefringence in cross-polarised light. A portion of proteinaceous material was associated with a ‘tangle’ of these structures (Fig. 5). To assess whether the insect remains were those of the Bogong moth, we compared the residues on Samples 2, 4 and 6 with a comparative reference sample (see “Methods”). All 26 cases of wing segments from the grindstone matched the metrical and morphological characteristics of those from Bogong moths in the reference material. The recorded damage on the archaeological wing segments, such as ripped wing structures, small rectangular wing fragments and tearing in various states of carbonisation, is what would be expected from ethnohistoric accounts of Bogong moth processing. Aboriginal people from across the region are known to have cooked Bogong moths on heated earth during the early and mid-nineteenth century. The moths were stirred during cooking, causing the wings and legs to be broken off by friction and heat. Some of the moths were pounded and ground into a paste which could then be smoked to preserve the food for weeks1,2.
    Figure 5

    Examples of Bogong moth segments from lifted samples (all at × 400 magnification). (A) Partially carbonised wing structures from Sample 2 (pp). (B) Partially carbonised wing structure and carbonised material from Sample 2 (pp). (C) Partially carbonised moth wing segment from Sample 4 (pp). (D–E) Damaged moth wing segment from Sample 6 (D pp; E xp). (F–G) Damaged moth wing segment from Sample 6 (F pp; G xp). (H) Damaged moth wing segment with proteinaceous material, from Sample 6 (pp). (I) Unburnt moth wing segment from Sample 4 (pp). (J) Damaged moth wing segment with attachment, from Sample 6 (pp). (K) Damaged moth wing segments from Sample 6 (pp). (L–M) Probable moth hind leg from Sample 6 (L pp; M xp). (N) Damaged moth wing segment from Sample 6 (pp). (O) Damaged moth wing segment with attachment, from Sample 6 (pp). Light source = plane (pp), part polarised (part pol) and cross-polarised (xp) (photos: Birgitta Stephenson).

    Full size image More