More stories

  • in

    Fungus-growing insects host a distinctive microbiota apparently adapted to the fungiculture environment

    1.
    Cragg, S. M. et al. Lignocellulose degradation mechanisms across the tree of Life. Curr. Opin. Chem. Bio. 29, 108–119. https://doi.org/10.1016/j.cbpa.2015.10.018 (2015).
    Article  CAS  Google Scholar 
    2.
    Sticklen, M. B. Plant genetic engineering for biofuel production: towards affordable cellulosic ethanol. Nat. Rev. Genet. 9, 433–443. https://doi.org/10.1038/nrg2336 (2008).
    Article  PubMed  CAS  Google Scholar 

    3.
    Guerriero, G., Hausman, J., Strauss, J., Ertan, H. & Siddiqui, K. S. Lignocellulosic biomass: biosynthesis, degradation, and industrial utilization. Eng. Life Sci. 16, 1–16. https://doi.org/10.1002/elsc.201400196 (2016).
    Article  CAS  Google Scholar 

    4.
    Morrison, M., Pope, P. B., Denman, S. E. & McSweeney, C. S. Plant biomass degradation by gut microbiomes: more of the same or something new?. Curr. Opin. Biotechnol. 20, 358–363. https://doi.org/10.1016/j.copbio.2009.05.004 (2009).
    Article  PubMed  CAS  Google Scholar 

    5.
    Karasov, W. H., del Rio, C. M. & Caviedes-Vidal, E. Ecological physiology of diet and digestive systems. Annu. Rev. Physiol. 73, 69–93. https://doi.org/10.1146/annurev-physiol-012110-142152 (2011).
    Article  PubMed  CAS  Google Scholar 

    6.
    Engel, P. & Moran, N. A. The gut microbiota of insects — diversity in structure and function. FEMS Microbiol. Rev. 37, 699–735. https://doi.org/10.1111/1574-6976.12025 (2013).
    Article  PubMed  CAS  Google Scholar 

    7.
    Hansen, A. K. & Moran, N. A. The impact of microbial symbionts on host plant utilization by herbivorous insects. Mol. Ecol. 23, 1473–1496. https://doi.org/10.1111/mec.12421 (2013).
    Article  PubMed  Google Scholar 

    8.
    Kohl, K. D., Connelly, J. W., Dearing, M. D. & Forbey, J. S. Microbial detoxification in the gut of a specialist avian herbivore, the Greater Sage-Grouse. FEMS Microbiol.Lett. 363, fnw144. https://doi.org/10.1093/femsle/fnw144 (2016).
    Article  PubMed  CAS  Google Scholar 

    9.
    Mueller, U. G., Gerardo, N. M., Aanen, D. K., Six, D. L. & Schultz, T. R. The evolution of agriculture in insects. Annu. Rev. Ecol. Evol. Syst. 36, 563–595. https://doi.org/10.1146/annurev.ecolsys.36.102003.152626 (2005).
    Article  Google Scholar 

    10.
    Mayhé-Nunes, A. J. & Jaffé, K. On the biogeography of Attini (Hymenoptera: Formicidae). Ecotropicos 11, 45–54 (1998).
    Google Scholar 

    11.
    Ward, P. S., Brady, S. G., Fisher, B. L. & Schultz, T. R. The evolution of myrmicine ants: phylogeny and biogeography of a hyperdiverse ant clade (Hymenoptera: Formicidae). Syst. Entomol. 40, 61–81. https://doi.org/10.1111/syen.12090 (2015).
    Article  Google Scholar 

    12.
    Jordal, B. H. & Cognato, C. Molecular phylogeny of bark and ambrosia beetles reveals multiple origins of fungus farming during periods of global warming. BMC Evol. Biol. 12, 133. https://doi.org/10.1186/1471-2148-12-133 (2012).
    Article  PubMed  PubMed Central  CAS  Google Scholar 

    13.
    Nobre, T., Rouland-Lefevre, C. & Aanen, D. K. Comparative biology of fungus cultivation in termites and ants. In Biology of termites: a modern synthesis, Chapter 8, 193–210 (eds Bignell, D. E. et al.) (Springer, Berlin, 2011).
    Google Scholar 

    14.
    Aylward, F. O. et al. Leucoagaricus gongylophorus produces diverse enzymes for the degradation of recalcitrant plant polymers in leaf-cutter ant fungus gardens. Appl. Environ. Microbiol. 79, 3770–3778. https://doi.org/10.1128/AEM.03833-12 (2013).
    Article  PubMed  PubMed Central  CAS  Google Scholar 

    15.
    Khadempour, L. et al. The fungal cultivar of leaf-cutter ants produces specific enzymes in response to different plant substrates. Mol. Ecol. 25, 5795–5805. https://doi.org/10.1111/mec.13872 (2016).
    Article  PubMed  PubMed Central  CAS  Google Scholar 

    16.
    Vigueras, G. et al. Growth and enzymatic activity of Leucoagaricus gongylophorus, a mutualistic fungus isolated from the leaf-cutting ant Atta mexicana, on cellulose and lignocellulosic biomass. Lett. Appl. Microbiol. 65, 173–181. https://doi.org/10.1111/lam.12759 (2017).
    Article  PubMed  CAS  Google Scholar 

    17.
    Poulsen, M. et al. Complementary symbiont contributions to plant decomposition in a fungus-farming termite. Proc. Natl. Acad. Sci. USA 111, 14500–14505. https://doi.org/10.1073/pnas.1319718111 (2014).
    ADS  Article  PubMed  CAS  Google Scholar 

    18.
    Hyodo, F., Inoue, T., Azuma, J. I., Tayasu, I. & Abe, T. Role of the mutualistic fungus in lignin degradation in the fungus-growing termite Macrotermes gilvus (Isoptera; Macrotermitinae). Soil Biol. Biochem. 32, 653–658. https://doi.org/10.1016/S0038-0717(99)00192-3 (2000).
    Article  CAS  Google Scholar 

    19.
    Hyodo, F. et al. Differential role of symbiotic fungi in lignin degradation and food provision for fungus-growing termites (Macrotermitinae: Isoptera). Funct. Ecol. 17, 186–193. https://doi.org/10.1046/j.1365-2435.2003.00718.x (2003).
    Article  Google Scholar 

    20.
    De Fine Lich, H. H. & Biedermann, P. H. W. Patterns of functional enzyme activity in fungus farming ambrosia beetles. Front. Zool. 9, 13. https://doi.org/10.1186/1742-9994-9-13 (2012).
    Article  CAS  Google Scholar 

    21.
    Lange, L. & Grell, M. N. The prominent role of fungi and fungal enzymes in the ant–fungus biomass conversion symbiosis. Appl. Microbiol. Biotechnol. 98, 4839–4851. https://doi.org/10.1007/s00253-014-5708-5 (2014).
    Article  PubMed  CAS  Google Scholar 

    22.
    Collins, N. M. The role of termites in the decomposition of wood and leaf litter in the Southern Guinea savanna of Nigeria. Oecologia 51, 389–399. https://doi.org/10.1007/BF00540911 (1981).
    ADS  Article  PubMed  CAS  Google Scholar 

    23.
    Beaver, R. A. Insect-fungus relationships in the bark and ambrosia beetles. In Insect-fungus interactions (eds Wilding, N. et al.) 121–143 (Academic Press, Cambridge, 1989).
    Google Scholar 

    24.
    Kok, L. T., Norrisd, M. & Chu, H. M. Sterol metabolism as a basis for mutualistic symbiosis. Nature 225, 661–662. https://doi.org/10.1038/225661b0 (1970).
    ADS  Article  PubMed  CAS  Google Scholar 

    25.
    Six, D. L. Ecological and evolutionary determinants of bark beetle-fungus symbioses. Insects 3, 339–366. https://doi.org/10.3390/insects3010339 (2012).
    Article  PubMed  PubMed Central  Google Scholar 

    26.
    Pinto-Tomás, A. A. et al. Symbiotic nitrogen fixation in the fungus gardens of leaf-cutter ants. Science 326, 1120–1123. https://doi.org/10.1126/science.1173036 (2009).
    ADS  Article  PubMed  CAS  Google Scholar 

    27.
    Suen, G. et al. An insect herbivore microbiome with high plant biomass degrading capacity. PLoS Genet. 6, e1001129. https://doi.org/10.1371/journal.pgen.1001129 (2010).
    Article  PubMed  PubMed Central  CAS  Google Scholar 

    28.
    Aylward, F. O. et al. Metagenomic and metaproteomic insights into bacterial communities in leaf-cutter ant fungus gardens. ISME J. 6, 1688–1701. https://doi.org/10.1038/ismej.2012.10 (2012).
    Article  PubMed  PubMed Central  CAS  Google Scholar 

    29.
    Haanstad, J. O. & Norris, D. M. Microbial symbiotes of the ambrosia beetle Xyletorinus politus. Microb. Ecol. 11, 267–276. https://doi.org/10.1007/BF02010605 (1985).
    Article  PubMed  CAS  Google Scholar 

    30.
    Grubbs, K. J. et al. Genome sequence of Streptomyces griseus  strain XyelbKG-1, an ambrosia beetle associated actinomycete. J. Bacteriol. 193, 2890–2891. https://doi.org/10.1128/JB.00330-11 (2011).
    Article  PubMed  PubMed Central  CAS  Google Scholar 

    31.
    Scott, J. J. et al. Bacterial protection of beetle-fungus mutualism. Science 322, 63. https://doi.org/10.1126/science.1160423 (2008).
    ADS  Article  PubMed  PubMed Central  CAS  Google Scholar 

    32.
    Boone, C. K. Bacteria associated with a tree-killing insect reduce concentrations of plant defense compounds. J. Chem. Ecol. 39, 1003–1006. https://doi.org/10.1007/s10886-013-0313-0 (2013).
    Article  PubMed  CAS  Google Scholar 

    33.
    Xu, L.-T., Lu, M. & Sun, J.-H. Invasive bark beetle-associated microbes degrade a host defensive monoterpene. Insect Sci. 23, 183–190. https://doi.org/10.1111/1744-7917.12255 (2016).
    Article  PubMed  CAS  Google Scholar 

    34.
    Um, S., Fraimout, A., Sapountzis, P., Oh, D.-C. & Poulsen, M. The fungus-growing termite Macrotermes natalensis harbors bacillaene-producing Bacillus sp. that inhibit potentially antagonistic fungi. Sci. Rep. 3, 3250. https://doi.org/10.1038/srep03250 (2013).
    Article  PubMed  PubMed Central  Google Scholar 

    35.
    Li, H. et al. Lignocellulose pretreatment in a fungus-cultivating termite. Proc. Natl. Acad. Sci. USA 114, 4709–4714. https://doi.org/10.1073/pnas.1618360114 (2017).
    ADS  Article  PubMed  CAS  Google Scholar 

    36.
    Aylward, F. O. et al. Convergent bacterial microbiotas in the fungal agricultural systems of insects. mBio 5, e02077-14. https://doi.org/10.1128/mBio.02077-14 (2014).
    Article  PubMed  PubMed Central  Google Scholar 

    37.
    Stayton, C. T. The definition, recognition, and interpretation of convergent evolution, and two new measures for quantifying and assessing the significance of convergence. Evolution 69, 2140–2153. https://doi.org/10.1111/evo.12729 (2015).
    Article  PubMed  Google Scholar 

    38.
    Arbuckle, K. & Speed, M. P. Analysing convergent evolution: a practical guide to methods. In Evolutionary biology: convergent evolution, evolution of complex traits, concepts and methods, Chapter 2, (ed. Pontarotti, P.) 23–36 (Springer, Berlin , 2016).
    Google Scholar 

    39.
    Martiny, J. B. H., Jones, S. E., Lennon, J. T. & Martiny, A. C. Microbiomes in light of traits: a phylogenetic perspective. Science 350, 9323. https://doi.org/10.1126/science.aac9323 (2015).
    ADS  Article  CAS  Google Scholar 

    40.
    Rabeling, C., Verhaagh, M. & Engels, W. Comparative study of nest architecture and colony structure of the fungus-growing ants, Mycocepurus goeldii and M. smithii. J. Insect. Sci. 7, 40. https://doi.org/10.1673/031.007.4001 (2007).
    Article  PubMed  PubMed Central  CAS  Google Scholar 

    41.
    Zanetti, R. et al. An overview of integrated management of leaf-cutting ants (Hymenoptera: Formicidae) in Brazilian forest plantations. Forests 5, 439–454. https://doi.org/10.3390/f5030439 (2014).
    Article  Google Scholar 

    42.
    Markowitz, V. M. et al. IMG/M-HMP: a metagenome comparative analysis system for the human microbiome project. PLoS ONE 7, e40151. https://doi.org/10.1371/journal.pone.0040151 (2012).
    ADS  Article  PubMed  PubMed Central  CAS  Google Scholar 

    43.
    Adams, A. S. et al. Mountain pine beetles colonizing historical and naïve host trees are associated with a bacterial community highly enriched in genes contributing to terpene metabolism. Appl. Environ. Microbiol. 79, 3468–3475. https://doi.org/10.1128/AEM.00068-13 (2013).
    Article  PubMed  PubMed Central  CAS  Google Scholar 

    44.
    Solheim, H. Oxygen deficiency and spruce resin inhibition of growth of blue stain fungi associated with Ips typographus. Mycol. Res. 95, 1387–1392. https://doi.org/10.1016/S0953-7562(09)80390-0 (1991).
    Article  Google Scholar 

    45.
    Schuurman, G. H. Ecosystem influences of fungus-growing termites in the dry Paleotropics. In Soil ecology and ecosystem services, Chapter 34 (eds Wall, D. H. et al.) 173–188 (Oxford University Press, Oxford, 2012).
    Google Scholar 

    46.
    Somera, A. F., Lima, A. M., Santos-Neto, A. J., Lanças, F. M. & Bacci, M. Jr. Leaf-cutter ant fungus gardens are biphasic mixed microbial bioreactors that convert plant biomass to polyols with biotechnological applications. Appl. Environ. Microbiol. 81, 4525–4535. https://doi.org/10.1128/AEM.00046-15 (2015).
    Article  PubMed  PubMed Central  CAS  Google Scholar 

    47.
    Ballard, R. W., Palleroni, N. J., Doudoroff, M., Stanier, R. Y. & Mandel, M. Taxonomy of the aerobic pseudomonads: Pseudomonas cepacia, P. marginata, P. alliicola and P. caryophylli. J. Gen. Microbiol. 60, 199–214. https://doi.org/10.1099/00221287-60-2-199 (1970).
    Article  PubMed  CAS  Google Scholar 

    48.
    O’Hara, C. M. Manual and automated instrumentation for identification of Enterobacteriaceae and other aerobic gram-negative Bacilli. Clin. Microbiol. Rev. 18, 147–162. https://doi.org/10.1128/CMR.18.1.147-162.2005 (2005).
    Article  PubMed  PubMed Central  CAS  Google Scholar 

    49.
    Brune, A., Miambi, E. & Breznak, J. A. Roles of oxygen and the intestinal microflora in the metabolism of lignin-derived phenylpropanoids and other monoaromatic compounds by termites. Appl. Environ. Microbiol. 61, 2688–2695 (1995).
    Article  CAS  Google Scholar 

    50.
    White, B. A., Lamed, R., Bayer, E. A. & Flint, H. J. Biomass utilization by gut microbiomes. Annu. Rev. Microbiol. 68, 279–296. https://doi.org/10.1146/annurev-micro-092412-155618 (2014).
    Article  PubMed  CAS  Google Scholar 

    51.
    de Vos, W. Microbial biofilms and the human intestinal microbiome. npj Biofilms Microbio. 1, 15005. https://doi.org/10.1038/npjbiofilms.2015.5 (2015).
    Article  CAS  Google Scholar 

    52.
    Koh, A., De Vadder, F., Kovatcheva-Datchary, P. & Bäckhed, F. From dietary fiber to host physiology: short-chain fatty acids as key bacterial metabolites. Cell 165, 1332–1345. https://doi.org/10.1016/j.cell.2016.05.041 (2016).
    Article  PubMed  CAS  Google Scholar 

    53.
    Leng, R. A. Biofilm compartmentalisation of the rumen microbiome: modification of fermentation and degradation of dietary toxins. Anim. Prod. Sci. 57, 2188–2203. https://doi.org/10.1071/AN17382 (2017).
    Article  CAS  Google Scholar 

    54.
    Kohl, K. D. et al. Metagenomic sequencing provides insights into microbial detoxification in the guts of small mammalian herbivores (Neotoma spp.). FEMS Microbiol. Ecol. 94, fiy184. https://doi.org/10.1093/femsec/fiy184 (2018).
    Article  CAS  Google Scholar 

    55.
    Burke, C., Steinberg, P., Rusch, D., Kjelleberg, S. & Thomas, T. Bacterial community assembly based on functional genes rather than species. Proc. Natl. Acad. Sci USA 108, 14288–14293. https://doi.org/10.1073/pnas.1101591108 (2011).
    ADS  Article  Google Scholar 

    56.
    Louca, S., Parfrey, L. W. & Doebeli, M. Decoupling function and taxonomy in the global ocean microbiome. Science 353, 1272–1277. https://doi.org/10.1126/science.aaf4507 (2016).
    ADS  Article  PubMed  CAS  Google Scholar 

    57.
    Louca, S. Function and functional redundancy in microbial systems. Nat. Ecol. Evol. 2, 936–943. https://doi.org/10.1038/s41559-018-0519-1 (2018).
    Article  PubMed  Google Scholar 

    58.
    Jurburg, S. D. & Salles, J. F. Functional redundancy and ecosystem function—the soil microbiota as a case study. In Biodiversity in ecosystems—linking structure and function (eds Lo, Y.-H. et al.) 29–49 (INTECH, New York, 2015).
    Google Scholar 

    59.
    Grell, M. N. et al. The fungal symbiont of Acromyrmex leaf-cutting ants expresses the full spectrum of genes to degrade cellulose and other plant cell wall polysaccharides. BMC Genomics 14, 928. https://doi.org/10.1186/1471-2164-14-928 (2013).
    Article  PubMed  PubMed Central  CAS  Google Scholar 

    60.
    Žifčáková, L. et al. Feed in summer, rest in winter: microbial carbon utilization in forest topsoil. Microbiome 5, 122. https://doi.org/10.1186/s40168-017-0340-0 (2017).
    Article  PubMed  PubMed Central  Google Scholar 

    61.
    Jing, T., Qi, F. & Wang, Z. Most dominant roles of insect gut bacteria: digestion, detoxification, or essential nutrient provision? Microbiome 8, 38. https://doi.org/10.1186/s40168-020-00823-y (2020).
    Article  PubMed  PubMed Central  Google Scholar 

    62.
    Howard, J. J., Cazin, J. & Wiemer, D. F. Toxicity of terpenoid deterrents to the leafcutting ant Atta cephalotes and its mutualistic fungus. J. Chem. Ecol. 14, 59–69. https://doi.org/10.1007/BF01022531 (1988).
    Article  PubMed  CAS  Google Scholar 

    63.
    Keeling, C. I. & Bohlmann, J. Diterpene resin acids in conifers. Phytochemistry 67, 2415–2423. https://doi.org/10.1016/j.phytochem.2006.08.019 (2006).
    Article  PubMed  CAS  Google Scholar 

    64.
    Zhu, L. et al. Potential mechanism of detoxification of cyanide compounds by gut microbiomes of bamboo-eating pandas. MSphere 3, e00229-18. https://doi.org/10.1128/mSphere.00229-18 (2018).
    Article  PubMed  PubMed Central  Google Scholar 

    65.
    Cheng, X. et al. Metagenomic analysis of the pinewood nematode microbiome reveals a symbiotic relationship critical for xenobiotics degradation. Sci. Rep. 3, 1869. https://doi.org/10.1038/srep01869 (2013).
    Article  PubMed  PubMed Central  CAS  Google Scholar 

    66.
    Flemming, H. et al. Biofilms: an emergent form of bacterial life. Nat. Rev. Microbiol. 14, 563–575. https://doi.org/10.1038/nrmicro.2016.94 (2016).
    Article  PubMed  CAS  Google Scholar 

    67.
    Sivadon, P., Barnier, C., Urios, L. & Grimaud, R. Biofilm formation as a microbial strategy to assimilate particulate substrates. Environ. Microbiol. Rep. 11, 749–764. https://doi.org/10.1111/1758-2229.12785 (2019).
    Article  PubMed  CAS  Google Scholar 

    68.
    Brethauer, S., Shahab, R. L. & Studer, M. H. Impacts of biofilms on the conversion of cellulose. Appl. Microbiol. Biotechnol. 104, 5201–5212. https://doi.org/10.1007/s00253-020-10595-y (2020).
    Article  PubMed  PubMed Central  CAS  Google Scholar 

    69.
    Macfarlane, S. & Macfarlane, G. T. Composition and metabolic activities of bacterial biofilms colonizing food residues in the human gut. Appl. Environ. Microbiol. 72, 6204–6211. https://doi.org/10.1128/AEM.00754-06 (2006).
    Article  PubMed  PubMed Central  CAS  Google Scholar 

    70.
    Deveau, A. et al. Bacterial–fungal interactions: ecology, mechanisms and challenges. FEMS Microbiol. Rev. 42, 335–352. https://doi.org/10.1093/femsre/fuy008 (2018).
    Article  PubMed  CAS  Google Scholar 

    71.
    Purahong, W. et al. Life in leaf litter: novel insights into community dynamics of bacteria and fungi during litter decomposition. Mol. Ecol. 25, 4059–4074. https://doi.org/10.1111/mec.13739 (2016).
    Article  PubMed  CAS  Google Scholar 

    72.
    Frey-Klett, P. et al. Bacterial-fungal interactions: hyphens between agricultural, clinical, environmental, and food microbiologists. Microbiol. Mol. Biol. Rev. 75, 583–609. https://doi.org/10.1128/MMBR.00020-11 (2011).
    Article  PubMed  PubMed Central  CAS  Google Scholar 

    73.
    Martin, M. M. Biochemical implications of insect mycophagy. Biol. Rev. 54, 1–21. https://doi.org/10.1111/j.1469-185X.1979.tb00865.x (1979).
    Article  CAS  Google Scholar 

    74.
    Brabcová, V., Nováková, M., Davidová, A. & Baldrian, P. Dead fungal mycelium in forest soil represents a decomposition hotspot and a habitat for a specific microbial community. New Phytol. 210, 1369–1381. https://doi.org/10.1111/nph.13849 (2016).
    Article  PubMed  CAS  Google Scholar 

    75.
    Brabcová, V., Štursová, M. & Baldrian, P. Nutrient content affects the turnover of fungal biomass in forest topsoil and the composition of associated microbial communities. Soil Biol. Biochem. 118, 187–198. https://doi.org/10.1016/j.soilbio.2017.12.012 (2018).
    Article  CAS  Google Scholar 

    76.
    de Boer, W. D., Folman, L. B., Summerbell, R. C. & Boddy, L. Living in a fungal world: impact of fungi on soil bacterial niche development. FEMS Microbiol. Rev. 29, 795–811. https://doi.org/10.1016/j.femsre.2004.11.005 (2005).
    Article  PubMed  CAS  Google Scholar 

    77.
    Leveau, J. H. & Preston, G. M. Bacterial mycophagy: definition and diagnosis of a unique bacterial–fungal interaction. New Phytol. 177, 859–876. https://doi.org/10.1111/j.1469-8137.2007.02325.x (2008).
    Article  PubMed  Google Scholar 

    78.
    Carrasco, J. & Preston, G. M. Growing edible mushrooms: a conversation between bacteria and fungi. Environ. Microbiol. 22, 858–872. https://doi.org/10.1111/1462-2920.14765 (2020).
    Article  PubMed  Google Scholar 

    79.
    Warmink, J. A., Nazir, R. & Van Elsas, J. D. Universal and species-specific bacterial ‘fungiphiles’ in the mycospheres of different basidiomycetous fungi. Environ. Microbiol. 11, 300–312. https://doi.org/10.1111/j.1462-2920.2008.01767.x (2009).
    Article  PubMed  CAS  Google Scholar 

    80.
    Guennoc, C., Rose, C., Labbé, J. & Deveau, A. Bacterial biofilm formation on the hyphae of ectomycorrhizal fungi: a widespread ability under controls?. FEMS Microbiol. Ecol. 94, 093. https://doi.org/10.1093/femsec/fiy093 (2018).
    Article  CAS  Google Scholar 

    81.
    Figueiredo, A. R. T. D. & Kramer, J. Cooperation and conflict within the microbiota and their effects on animal hosts. Front. Ecol. Evol. 8, 132. https://doi.org/10.3389/fevo.2020.00132 (2020).
    Article  Google Scholar 

    82.
    Coyte, K. Z., Schluter, J. & Foster, K. R. The ecology of the microbiome: networks, competition, and stability. Science 350, 663–666. https://doi.org/10.1126/science.aad2602 (2015).
    ADS  Article  PubMed  CAS  Google Scholar 

    83.
    Donaldson, G., Lee, S. & Mazmanian, S. Gut biogeography of the bacterial microbiota. Nat. Rev. Microbiol. 14, 20–32. https://doi.org/10.1038/nrmicro3552 (2016).
    Article  PubMed  CAS  Google Scholar 

    84.
    Tropini, C., Earle, K. A., Huang, K. C. & Sonnenburg, J. L. The gut microbiome: connecting spatial organization to function. Cell Host Microbe 21, 433–442. https://doi.org/10.1016/j.chom.2017.03.010 (2017).
    Article  PubMed  PubMed Central  CAS  Google Scholar 

    85.
    Adair, K. L. & Douglas, A. E. Making a microbiome: the many determinants of host-associated microbial community composition. Curr. Opin. Microbiol. 35, 23–29. https://doi.org/10.1016/j.mib.2016.11.002 (2017).
    Article  PubMed  Google Scholar 

    86.
    Shafquat, A., Joice, R., Simmons, S. & Huttenhower, C. Functional and phylogenetic assembly of microbial communities in the human microbiome. Trends Microbiol. 22, 261–266. https://doi.org/10.1016/j.tim.2014.01.011 (2014).
    Article  PubMed  PubMed Central  CAS  Google Scholar 

    87.
    Hernandez-Agreda, A., Gates, R. D. & Ainsworth, T. D. Defining the core microbiome in corals’ microbial soup. Trends Microbiol. 25, 125–140. https://doi.org/10.1016/j.tim.2016.11.003 (2017).
    Article  PubMed  CAS  Google Scholar 

    88.
    Ramadhar, T. et al. Bacterial symbionts in agricultural systems provide a strategic source for antibiotic discovery. J. Antibiot. 67, 53–58. https://doi.org/10.1038/ja.2013.77 (2014).
    Article  PubMed  CAS  Google Scholar 

    89.
    Van Arnam, E. B., Currie, C. R. & Clardy, J. Defense contracts: molecular protection in insect-microbe symbioses. Chem. Soc. Rev. 47, 1638–1651. https://doi.org/10.1039/C7CS00340D (2018).
    Article  PubMed  Google Scholar 

    90.
    Berasategui, A. et al. Potential applications of insect symbionts in biotechnology. Appl. Microbiol. Biotechnol. 100, 1567–1577. https://doi.org/10.1007/s00253-015-7186-9 (2016).
    Article  PubMed  CAS  Google Scholar 

    91.
    Cox, M. P., Peterson, D. A. & Biggs, P. J. SolexaQA: At-a-glance quality assessment of Illumina second-generation sequencing data. BMC Bioinformatics 11, 485. https://doi.org/10.1186/1471-2105-11-485 (2010).
    Article  PubMed  PubMed Central  Google Scholar 

    92.
    Li, D., Liu, C., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676. https://doi.org/10.1093/bioinformatics/btv033 (2015).
    Article  PubMed  CAS  Google Scholar 

    93.
    Schmieder, R. & Edwards, R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 27, 863–864. https://doi.org/10.1093/bioinformatics/btr026 (2011).
    Article  PubMed  PubMed Central  CAS  Google Scholar 

    94.
    Markowitz, V. M. et al. IMG/M 4 version of the integrated metagenome comparative analysis system. Nucleic Acids Res. 42, D568–D573. https://doi.org/10.1093/nar/gkt919 (2014).
    Article  PubMed  CAS  Google Scholar 

    95.
    Patil, K. R., Roune, L. & MChardy, A. C. The PhyloPythiaS web server for taxonomic assignment of metagenome sequences. PLoS One 7, e38581. https://doi.org/10.1371/journal.pone.0038581 (2012).
    ADS  Article  PubMed  PubMed Central  CAS  Google Scholar 

    96.
    Engel, P., Martinson, V. G. & Moran, N. A. Functional diversity within the simple gut microbiota of the honey bee. Proc. Natl Acad. Sci. USA 109, 11002–11007. https://doi.org/10.1073/pnas.1202970109 (2012).
    ADS  Article  PubMed  Google Scholar 

    97.
    Kešnerová, L., Moritz, R. & Engel, P. Bartonella apis sp. Nov., a honey bee gut symbiont of the class Alphaproteobacteria. Int. J. Syst. Evol. Microbiol. 66, 414–421. https://doi.org/10.1099/ijsem.0.000736 (2016).
    Article  CAS  Google Scholar 

    98.
    Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797. https://doi.org/10.1093/nar/gkh340 (2004).
    Article  PubMed  PubMed Central  CAS  Google Scholar 

    99.
    Guindon, S. & Gascuel, O. A Simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52, 696–704. https://doi.org/10.1080/10635150390235520https://doi.org/10.1080/10635150390235520 (2003).
    Article  PubMed  Google Scholar 

    100.
    Hammer, Ř., Harper, D. A. T. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electronica 4, 1. https://palaeo-electronica.org/2001_1/past/issue1_01.htm (2001).

    101.
    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/btu494https://doi.org/10.1093/bioinformatics/btu494 (2014).
    Article  PubMed  PubMed Central  CAS  Google Scholar 

    102.
    Fan, H., Ives, A. R., Surget-Groba, Y. & Cannon, C. H. An assembly and alignment-free method of phylogeny reconstruction from next-generation sequencing data. BMC Genomics 16, 522. https://doi.org/10.1186/s12864-015-1647-5 (2015).
    Article  PubMed  PubMed Central  CAS  Google Scholar 

    103.
    Letunic, I. & Bork, P. Interactive tree of life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics 23, 127–128. https://doi.org/10.1093/bioinformatics/btl529 (2007).
    Article  PubMed  CAS  Google Scholar 

    104.
    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).
    Article  PubMed  CAS  Google Scholar 

    105.
    White, J. R. et al. Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput. Biol. 5, 1000352. https://doi.org/10.1371/journal.pcbi.1000352 (2009).
    Article  CAS  Google Scholar 

    106.
    Cantarel, B. L. et al. The carbohydrate-active enZymes database (CAZy): an expert resource for glycogenomics. Nucleic Acids Res. 37, D233–D238. https://doi.org/10.1093/nar/gkn663 (2009).
    Article  PubMed  CAS  Google Scholar 

    107.
    Zhang, H. et al. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 46, W95–W101. https://doi.org/10.1093/nar/gky418 (2018).
    ADS  Article  PubMed  PubMed Central  CAS  Google Scholar 

    108.
    Kanehisa, M., Sato, Y. & Morishima, K. BlastKOALA & GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J. Mol. Biol. 428, 726–731. https://doi.org/10.1016/j.jmb.2015.11.006 (2016).
    Article  PubMed  CAS  Google Scholar 

    109.
    Barcoto, M. O. Fungus-growing insects host a convergent microbiome with functional similarities to other lignocellulose-feeding insects. Masters dissertation, São Paulo State University (2017). More

  • in

    A marine virus as foe and friend

    1.
    Zhao, Y. et al. Nature 494, 357–360 (2013).
    CAS  Article  Google Scholar 
    2.
    Giovannoni, S. J. Annu. Rev. Mar. Sci. 9, 231–255 (2017).
    Article  Google Scholar 

    3.
    Morris, R. M., Cain, K. R., Hvorecny, K. L. & Kollman, J. M. Nat. Microbiol. https://doi.org/10.1038/s41564-020-0725-x (2020).
    Article  PubMed  Google Scholar 

    4.
    Zhao, Y. et al. Environ. Microbiol. 21, 1989–2001 (2019).
    CAS  Article  Google Scholar 

    5.
    Howard-Varona, C., Hargreaves, K. R., Abedon, S. T. & Sullivan, M. B. ISME J. 11, 1511–1520 (2017).
    Article  Google Scholar 

    6.
    Nanda, A. M., Thormann, K. & Frunzke, J. J. Bacteriol. 197, 410–419 (2015).
    Article  Google Scholar 

    7.
    Liu, X. et al. Environ. Microbiol. 21, 4212–4232 (2019).
    CAS  Article  Google Scholar 

    8.
    Feiner, R. et al. Nat. Rev. Microbiol. 13, 641–650 (2015).
    CAS  Article  Google Scholar 

    9.
    Våge, S., Storesund, J. E. & Thingstad, T. F. Nature 499, E3–E4 (2013).
    Article  Google Scholar  More

  • in

    Selectivity of deltamethrin doses on Palmistichus elaeisis (Hymenoptera: Eulophidae) parasitizing Tenebrio molitor (Coleoptera: Tenebrionidae)

    1.
    Zanuncio, J. C. et al. Population dynamics of Lepidoptera pests in Eucalyptus urophylla plantations in the Brazilian Amazonia. Forests 5, 72–87 (2014).
    Google Scholar 
    2.
    Macedo-Reis, L., Soares, L., Faria, M., Espírito-Santo, M. & Zanuncio, J. Survival of a lepidopteran defoliator of Eucalyptus is influenced by local hillside and forest remnants in Brazil. Fla. Entomol. 96, 941–947 (2013).
    Google Scholar 

    3.
    Loetti, V. & Bellocq, I. Effects of the insecticides methoxyfenozide and cypermethrin on non-target arthropods: a field experiment. Austral Entomol. 56, 255–260 (2017).
    Google Scholar 

    4.
    Lundström, N. L. P., Zhang, H. & Brännström, Å. Pareto-efficient biological pest control enable high efficacy at small costs. Ecol. Model. 364, 89–97 (2017).
    Google Scholar 

    5.
    Costa, L. G., Giordano, G., Guizzetti, M. & Vitalone, A. Neurotoxicity of pesticides: a brief review. Front. Biosci. 13, 1240–1249 (2008).
    CAS  PubMed  Google Scholar 

    6.
    de S Pereira, K., Guedes, N. M. P., Serrão, J. E., Zanuncio, J. C. & Guedes, R. N. C. Superparasitism, immune response and optimum progeny yield in the gregarious parasitoid Palmistichus elaeisis. Pest Manag. Sci. 73, 1101–1109 (2017).
    PubMed  Google Scholar 

    7.
    Pereira, F. F., Zanuncio, T. V., Zanuncio, J. C., Pratissoli, D. & Tavares, M. T. Species of lepidoptera defoliators of eucalyptus as new host for the parasitoid Palmistichus elaeisis (Hymenoptera: Eulophidae). Braz. Arch. Biol. Technol. 51, 259–262 (2008).
    Google Scholar 

    8.
    Barbosa, R. H., Zanuncio, J. C., Pereira, F. F., Kassab, S. O. & Rossoni, C. Foraging activity of Palmistichus elaeisis (Hymenoptera: Eulophidae) at various densities on pupae of the Eucalyptus defoliator Thyrinteina arnobia (Lepidoptera: Geometridae). Fla. Entomol. 99, 686–690 (2016).
    Google Scholar 

    9.
    Zanuncio, J., Pereira, F., Jacques, G., Tavares, M. & Serrão, J. Tenebrio molitor Linnaeus (Coleoptera: Tenebrionidae), a new alternative host to rear the pupae parasitoid Palmistichus elaeisis Delvare & Lasalle (Hymenoptera: Eulophidae). Coleopt. Bull. 62, 64–66 (2008).
    Google Scholar 

    10.
    Roubos, C. R., Rodriguez-Saona, C. & Isaacs, R. Mitigating the effects of insecticides on arthropod biological control at field and landscape scales. Biol. Control 75, 28–38 (2014).
    CAS  Google Scholar 

    11.
    Addison, P. J. & Barker, G. M. Effect of various pesticides on the non-target species Microctonus hyperodae, a biological control agent of Listronotus bonariensis. Entomol. Exp. Appl. 119, 71–79 (2006).
    CAS  Google Scholar 

    12.
    Banks, J. E., Stark, J. D., Vargas, R. I. & Ackleh, A. S. Parasitoids and ecological risk assessment: can toxicity data developed for one species be used to protect an entire guild?. Biol. Control 59, 336–339 (2011).
    Google Scholar 

    13.
    Bayram, A., Salerno, G., Onofri, A. & Conti, E. Lethal and sublethal effects of preimaginal treatments with two pyrethroids on the life history of the egg parasitoid Telenomus busseolae. Biocontrol 55, 697–710 (2010).
    CAS  Google Scholar 

    14.
    Desneux, N., Decourtye, A. & Delpuech, J.-M. The sublethal effects of pesticides on beneficial arthropods. Annu. Rev. Entomol. 52, 81–106 (2007).
    CAS  PubMed  Google Scholar 

    15.
    Delpuech, J. M. & Delahaye, M. The sublethal effects of deltamethrin on Trichogramma behaviors during the exploitation of host patches. Sci. Total Environ. 447, 274–279 (2013).
    ADS  CAS  PubMed  Google Scholar 

    16.
    De La Cruz, R. A. et al. Side-effects of pesticides on the generalist endoparasitoid Palmistichus elaeisis (Hymenoptera: Eulophidae). Sci. Rep. 7, 1–8 (2017).
    CAS  Google Scholar 

    17.
    Wang, Y. et al. Susceptibility to selected insecticides and risk assessment in the insect egg parasitoid Trichogramma confusum (Hymenoptera: Trichogrammatidae). J. Econ. Entomol. 106, 142–149 (2013).
    CAS  PubMed  Google Scholar 

    18.
    Zanuncio, T. V. et al. Fertility and life expectancy of the predator Supputius cincticeps (Heteroptera: Pentatomidae) exposed to sublethal doses of permethrin. Biol. Res. 38, 31–39 (2005).
    CAS  PubMed  Google Scholar 

    19.
    Wang, Y. et al. Toxicity risk of insecticides to the insect egg parasitoid Trichogramma evanescens Westwood (Hymenoptera: Trichogrammatidae). Pest Manag. Sci. 70, 398–404 (2014).
    CAS  PubMed  Google Scholar 

    20.
    Biondi, A., Zappalà, L., Stark, J. D. & Desneux, N. Do biopesticides affect the demographic traits of a parasitoid wasp and its biocontrol services through sublethal effects?. PLoS ONE 8, 1–11 (2013).
    Google Scholar 

    21.
    Cônsoli, F. L., Botelho, P. S. M. & Parra, J. R. P. Selectivity of insecticides to the egg parasitoid Trichogramma galloi Zucchi, 1988, (Hym., Trichogrammatidae). J. Appl. Entomol. 125, 37–43 (2001).
    Google Scholar 

    22.
    Soderlund, D. M. Toxicology and mode of action of pyrethroid insecticides. In Hayes’ Handbook of Pesticide Toxicology 1665–1686 (Elsevier Inc., 2010). doi:10.1016/B978-0-12-374367-1.00077-X.

    23.
    Youssef, A. I. et al. The side-effects of plant protection products used in olive cultivation on the hymenopterous egg parasitoid Trichogramma cacoeciae Marchal. J. Appl. Entomol. 128, 593–599 (2004).
    CAS  Google Scholar 

    24.
    Alix, A., Cortesero, A. M., Nénon, J. P. & Anger, J. P. Selectivity assessment of chlorfenvinphos reevaluated by including physiological and behavioral effects on an important beneficial insect. Environ. Toxicol. Chem. 20, 2530–2536 (2001).
    CAS  PubMed  Google Scholar 

    25.
    Fontes, J., Roja, I. S., Tavares, J. & Oliveira, L. Lethal and sublethal effects of various pesticides on Trichogramma achaeae (Hymenoptera: Trichogrammatidae). J. Econ. Entomol. 111, 1219–1226 (2018).
    CAS  PubMed  Google Scholar 

    26.
    Bayram, A., Salerno, G., Onofri, A. & Conti, E. Sub-lethal effects of two pyrethroids on biological parameters and behavioral responses to host cues in the egg parasitoid Telenomus busseolae. Biol. Control 53, 153–160 (2010).
    CAS  Google Scholar 

    27.
    Zantedeschi, R. et al. Toxicity of soybean-registered agrochemicals to Telenomus podisi and Trissolcus basalis immature stages. Phytoparasitica 46, 203–212 (2018).
    CAS  Google Scholar 

    28.
    Thubru, D. P., Firake, D. M. & Behere, G. T. Assessing risks of pesticides targeting lepidopteran pests in cruciferous ecosystems to eggs parasitoid, Trichogramma brassicae (Bezdenko). Saudi J. Biol. Sci. 25, 680–688 (2018).
    CAS  PubMed  Google Scholar 

    29.
    Fernández, M. D. M., Medina, P., Fereres, A., Smagghe, G. & Viñuela, E. Are mummies and adults of Eretmocerus mundus (Hymenoptera: Aphelinidae) compatible with modern insecticides?. J. Econ. Entomol. 108, 2268–2277 (2015).
    Google Scholar 

    30.
    Liu, F., Zhang, X., Gui, Q. Q. & Xu, Q. J. Sublethal effects of four insecticides on Anagrus nilaparvatae (Hymenoptera: Mymaridae), an important egg parasitoid of the rice planthopper Nilaparvata lugens (Homoptera: Delphacidae). Crop Prot. 37, 13–19 (2012).
    Google Scholar 

    31.
    Pastori, P. L. et al. Reproduction of Trichospilus diatraeae (Hymenoptera: Eulophidae) in pupae of two lepidopterans defoliators of eucalypt. Rev. Colomb. Entomol. 38, 91–93 (2012).
    Google Scholar 

    32.
    Harvey, J. A. & Gols, R. Effects of plant-mediated differences in host quality on the development of two related endoparasitoids with different host-utilization strategies. J. Insect Physiol. 107, 110–115 (2018).
    CAS  PubMed  Google Scholar 

    33.
    Pereira, F. F. et al. The density of females of Palmistichus elaeisis Delvare and Lasalle (Hymenoptera: Eulophidae) affects their reproductive performance on pupae of Bombyx mori L. (Lepidoptera: Bombycidae). An. Acad. Bras. Cienc. 82, 323–331 (2010).
    PubMed  Google Scholar 

    34.
    Desneux, N., Denoyelle, R. & Kaiser, L. A multi-step bioassay to assess the effect of the deltamethrin on the parasitic wasp Aphidius ervi. Chemosphere 65, 1697–1706 (2006).
    ADS  CAS  PubMed  Google Scholar 

    35.
    Desneux, N., Ramirez-Romero, R. & Kaiser, L. Multistep bioassay to predict recolonization potential of emerging parasitoids after a pesticide treatment. Environ. Toxicol. Chem. 25, 2675–2682 (2006).
    CAS  PubMed  Google Scholar 

    36.
    Agrolink. Bula Decis 25 EC. www.agrolink.com.br https://www.agrolink.com.br/agrolinkfito/produto/decis-25-ec_8.html (2018).

    37.
    Khan, M. A., Khan, H. & Ruberson, J. R. Lethal and behavioral effects of selected novel pesticides on adults of Trichogramma pretiosum (Hymenoptera: Trichogrammatidae). Pest Manag. Sci. 71, 1640–1648 (2015).
    CAS  PubMed  Google Scholar 

    38.
    Stark, J. D., Banks, J. E. & Vargas, R. How risky is risk assessment: the role that life history strategies play in susceptibility of species to stress. Proc. Natl. Acad. Sci. 101, 732–736 (2004).
    ADS  CAS  PubMed  Google Scholar 

    39.
    Rieth, J. P. & Levin, M. D. The repellent effect of two pyrethroid insecticides on the honey bee. Physiol. Entomol. 13, 213–218 (1988).
    CAS  Google Scholar 

    40.
    Valente, E. C. N., Broglio, S. M. F., dos Passos, E. M. & de Lima, A. S. T. Desempenho de Trichogramma galloi (Hymenoptera: Trichogrammatidae) sobre ovos de Diatraea spp. (Lepidoptera: Crambidae). Pesqui. Agropecu. Bras. 51, 293–300 (2016).
    Google Scholar 

    41.
    Rossoni, C. et al. Development of Eulophidae (Hymenoptera) parasitoids in Diatraea saccharalis (Lepidoptera: Crambidae) pupae exposed to entomopathogenic fungi. Can. Entomol. 148, 716–723 (2016).
    Google Scholar 

    42.
    Harvey, J. A., Poelman, E. H. & Tanaka, T. Intrinsic inter- and intraspecific competition in parasitoid wasps. Annu. Rev. Entomol. 58, 333–351 (2013).
    CAS  PubMed  Google Scholar 

    43.
    Jervis, M. A., Ellers, J. & Harvey, J. A. Resource acquisition, allocation, and utilization in parasitoid reproductive strategies. Annu. Rev. Entomol. 53, 361–385 (2008).
    CAS  PubMed  Google Scholar 

    44.
    Souza, D., Monteiro, A. B. & Faria, L. D. B. Morphometry, allometry, and fluctuating asymmetry of egg parasitoid Trichogramma pretiosum under insecticide influence. Entomol. Exp. Appl. 166, 298–303 (2018).
    CAS  Google Scholar 

    45.
    Rasool, S. et al. Effect of host size on larval competition of the gregarious parasitoid Bracon hebetor (Say.) (Hymenoptera: Braconidae). Pak. J. Zool. 49, 1085–1085 (2017).
    Google Scholar 

    46.
    Menezes, C. W. G. et al. Reproductive and toxicological impacts of herbicides used in Eucalyptus culture in Brazil on the parasitoid Palmistichus elaeisis (Hymenoptera: Eulophidae). Weed Res. 52, 520–525 (2012).
    CAS  Google Scholar 

    47.
    Andrade, G. S. et al. Oogenesis pattern and type of ovariole of the parasitoid Palmistichus elaeisis (Hymenoptera: Eulophidae). An. Acad. Bras. Cienc. 84, 767–774 (2012).
    PubMed  Google Scholar 

    48.
    Menezes, C. W. G. et al. Palmistichus elaeisis (Hymenoptera: Eulophidae) as an indicator of toxicity of herbicides registered for corn in Brazil. Chil. J. Agric. Res. 74, 361–365 (2014).
    Google Scholar 

    49.
    I. R. A. C. Method No: 007: Leaf eating Lepidoptera and Coleoptera. Available online: uploads/ 2009, (2010).

    50.
    Finney, D. J. P. A. Probit Analysis 318 (Cambridge University Press, London , 1971).
    Google Scholar  More

  • in

    Savanna tree evolutionary ages inform the reconstruction of the paleoenvironment of our hominin ancestors

    1.
    Domínguez-Rodrigo, M. Is the “Savanna Hypothesis” a dead concept for explaining the emergence of the earliest hominins?. Curr. Anthropol. 55, 59–81 (2014).
    Google Scholar 
    2.
    Cerling, T. E. et al. Woody cover and hominin environments in the past 6 million years. Nature 476, 52–56 (2011).
    ADS  Google Scholar 

    3.
    Potts, R. Hominin evolution in settings of strong environmental variability. Q. Sci. Rev. 73, 1–13 (2013).
    ADS  Google Scholar 

    4.
    Magill, C. R., Ashley, G. M. & Freeman, K. H. Ecosystem variability and early human habitats in eastern Africa. Proc. Natl Acad. Sci. USA 110, 1167–1174 (2013).
    ADS  CAS  PubMed  Google Scholar 

    5.
    Levin, N. E. Environment and climate of early human evolution. Annu. Rev. Earth Planet. Sci. 43, 405–429 (2015).
    ADS  CAS  Google Scholar 

    6.
    Uno, K. T., Polissar, P. J., Jackson, K. E. & deMenocal, P. B. Neogene biomarker record of vegetation change in eastern Africa. Proc. Natl Acad. Sci. USA 113, 6355–6363 (2016).
    ADS  CAS  PubMed  Google Scholar 

    7.
    Jacobs, B. F. Paleobotanical studies from tropical Africa: relevance to the evolution of forest, woodland, and savannah biomes. Phil. Trans. R. Soc. B 359, 1573–1583 (2004).
    PubMed  Google Scholar 

    8.
    Beerling, D. J. & Osborne, C. P. The origin of the savanna biome. Glob. Chang. Biol. 12, 2023–2031 (2006).
    ADS  Google Scholar 

    9.
    Bond, W. J. What limits trees in C4 grasslands and savannas?. Annu. Rev. Ecol. Evol. Syst. 39, 641–659 (2008).
    Google Scholar 

    10.
    Cerling, T. E. et al. Global vegetation change through the Miocene/Pliocene boundary. Nature 389, 153–158 (1997).
    ADS  CAS  Google Scholar 

    11.
    Ehleringer, J. R., Cerling, T. E. & Helliker, B. R. C4 photosynthesis, atmospheric CO2, and climate. Oecologia 112, 285–299 (1997).
    ADS  PubMed  Google Scholar 

    12.
    Beerling, D. J. & Royer, D. L. Convergent cenozoic CO2 history. Nat. Geosci. 4, 418–420 (2011).
    ADS  CAS  Google Scholar 

    13.
    Pagani, M., Zachos, J. C., Freeman, K. H., Tipple, B. & Bohaty, S. Marked decline in atmospheric carbon dioxide concentrations during the Paleogene. Science 309, 600–603 (2005).
    ADS  CAS  PubMed  Google Scholar 

    14.
    Pagani, M., Freeman, K. H. & Arthur, M. A. Late Miocene atmospheric CO2 concentrations and the expansion of C4 grasses. Science 285, 876–879 (1999).
    CAS  PubMed  Google Scholar 

    15.
    Bolton, C. T. et al. Decrease in coccolithophore calcification and CO2 since the middle Miocene. Nat. Commun. 7, 10284 (2016).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    16.
    Herbert, T. D. et al. Late Miocene global cooling and the rise of modern ecosystems. Nat. Geosci. 9, 843–847 (2016).
    ADS  CAS  Google Scholar 

    17.
    Sponheimer, M. et al. Isotopic evidence of early hominin diets. Proc. Natl Acad. Sci. USA 110, 10513–10518 (2013).
    ADS  CAS  Google Scholar 

    18.
    Feakins, S. J. et al. Northeast African vegetation change over 12 my. Geology 41, 295–298 (2013).
    ADS  Google Scholar 

    19.
    Ségalen, L., Lee-Thorp, J. A. & Cerling, T. E. Timing of C4 grass expansion across sub-Saharan Africa. J. Hum. Evol. 53, 549–559 (2007).
    PubMed  Google Scholar 

    20.
    Pennington, R. T., Cronk, Q. C. B. & Richardson, J. A. Introduction and synthesis: plant phylogeny and the origin of major biomes. Phil. Trans. R. Soc. B. 359, 1455–1464 (2004).
    PubMed  Google Scholar 

    21.
    Pennington, R. T., Richardson, J. E. & Lavin, M. Insights into the historical construction of species-rich biomes from dated plant phylogenies, neutral ecological theory and phylogenetic community structure. New Phytol. 172, 605–616 (2006).
    PubMed  Google Scholar 

    22.
    Bytebier, B., Antonelli, A., Bellstedt, D. U. & Linder, H. P. Estimating the age of fire in the Cape flora of South Africa from an orchid phylogeny. Proc. R. Soc. B 278, 188–195 (2010).
    PubMed  Google Scholar 

    23.
    Crisp, M. D., Burrows, G. E., Cook, L. G., Thornhill, A. H. & Bowman, D. M. Flammable biomes dominated by eucalypts originated at the Cretaceous-Palaeogene boundary. Nat. Comm. 2, 193 (2011).
    ADS  Google Scholar 

    24.
    Vicentini, A., Barber, J. C., Aliscioni, S. S., Giussani, L. M. & Kellogg, E. A. The age of the grasses and clusters of origins of C4 photosynthesis. Glob. Chang. Biol. 14, 2963–2977 (2008).
    ADS  Google Scholar 

    25.
    Scheiter, S. et al. Fire and fire-adapted vegetation promoted C4 expansion in the Late Miocene. New Phytol. 195, 653–666 (2012).
    PubMed  Google Scholar 

    26.
    Ramírez, S. R., Gravendeel, B., Singer, R. B., Marshall, C. R. & Pierce, N. E. Dating the origin of the Orchidaceae from a fossil orchid with its pollinator. Nature 448, 1042–1045 (2007).
    ADS  Google Scholar 

    27.
    Losos, J. B. & Schluter, D. Analysis of an evolutionary species–area relationship. Nature 408, 847–850 (2000).
    ADS  CAS  PubMed  Google Scholar 

    28.
    Linder, H. P. & Verboom, G. A. The evolution of regional species richness: the history of the southern African flora. Annu. Rev. Ecol. Evol. Syst. 46, 393–412 (2015).
    Google Scholar 

    29.
    Simon, M. F. et al. Recent assembly of the Cerrado, a neotropical plant diversity hotspot, by in situ evolution of adaptations to fire. Proc. Natl Acad. Sci. USA 106, 20359–20364 (2009).
    ADS  CAS  PubMed  Google Scholar 

    30.
    Cardoso, D. et al. A molecular-dated phylogeny and biogeography of the monotypic legume genus Haplormosia, a missing African branch of the otherwise American-Australian Brongniartieae clade. Mol. Phylogenet. Evol. 107, 431–442 (2017).
    PubMed  Google Scholar 

    31.
    Fritz, S. A. & Purvis, A. Selectivity in mammalian extinction risk and threat types: a new measure of phylogenetic signal strength in binary traits. Conserv. Biol. 24, 1042–1051 (2010).
    PubMed  Google Scholar 

    32.
    Linder, H. P. East African Cenozoic vegetation history. Evol. Anthropol. 26, 300–312 (2017).
    PubMed  Google Scholar 

    33.
    Retallack, G. J. Middle Miocene fossil plants from Fort Ternan (Kenya) and evolution of African grasslands. Paleobiology 18, 383–400 (1992).
    Google Scholar 

    34.
    Uno, K. T. et al. Late Miocene to Pliocene carbon isotope record of differential diet change among East African herbivores. Proc. Natl. Acad. Sci. USA 108, 6509–6514 (2011).
    ADS  CAS  PubMed  Google Scholar 

    35.
    Dart, R. A. Australopithecus africanus: the man-ape of South Africa. Nature 115, 195–199 (1925).
    ADS  Google Scholar 

    36.
    Dembo, M. et al. The evolutionary relationships and age of Homo naledi: An assessment using dated Bayesian phylogenetic methods. J. Hum. Evol. 97, 17–26 (2016).
    PubMed  Google Scholar 

    37.
    Davies, T. J. & Buckley, L. B. Phylogenetic diversity as a window into the evolutionary and biogeographic histories of present-day richness gradients for mammals. Phil. Trans. R. Soc. B 366, 2414–2425 (2011).
    PubMed  Google Scholar 

    38.
    Maurin, O. et al. Savanna fire and the origins of the ‘underground forests’ of Africa. New Phytol. 204, 201–214 (2014).
    PubMed  Google Scholar 

    39.
    Charles-Dominique, T. et al. Spiny plants, mammal browsers, and the origin of African savannas. Proc. Natl Acad. Sci. USA 113, E5572–E5579 (2016).
    CAS  PubMed  Google Scholar 

    40.
    Bibi, F. A multi-calibrated mitochondrial phylogeny of extant Bovidae (Artiodactyla, Ruminantia) and the importance of the fossil record to systematics. BMC Evol. Biol. 13, 166 (2013).
    PubMed  PubMed Central  Google Scholar 

    41.
    Nyakatura, K. & Bininda-Emonds, O. Updating the evolutionary history of Carnivora (Mammalia): a new species-level supertree complete with divergence time estimates. BMC Biol. 10, 12 (2012).
    PubMed  PubMed Central  Google Scholar 

    42.
    Kyalangalilwa, B., Boatwright, J. S., Daru, B. H., Maurin, O. & van der Bank, M. Phylogenetic position and revised classification of Acacia s.l. (Fabaceae: Mimosoideae) in Africa, including new combinations Vachellia and Senegalia. Bot. J. Linn. Soc. 172, 500–523 (2013).
    Google Scholar 

    43.
    Silvestro, D. & Michalak, I. raxmlGUI: a graphical front-end for RAxML. Org. Divers. Evol. 12, 335–337 (2012).
    Google Scholar 

    44.
    Webb, C. O. & Donoghue, M. J. Phylomatic: tree assembly for applied phylogenetics. Mol. Ecol. Notes 5, 181–183 (2005).
    Google Scholar 

    45.
    Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian Phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973 (2012).
    CAS  PubMed  PubMed Central  Google Scholar 

    46.
    Bell, C. D., Soltis, D. E. & Soltis, P. S. The age and diversification of the angiosperms re-revisited. Am. J. Bot. 97, 1296–1303 (2010).
    PubMed  Google Scholar 

    47.
    Phillips, S. J. et al. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
    Google Scholar 

    48.
    Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
    Google Scholar 

    49.
    Liu, C., Berry, P. M., Dawson, T. P. & Pearson, R. G. Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28, 385–393 (2005).
    Google Scholar 

    50.
    Blach-Overgaard, A., Svenning, J. C., Dransfield, J., Greve, M. & Balslev, H. Determinants of palm species distributions across Africa: the relative roles of climate, non-climatic environmental factors, and spatial constraints. Ecography 33, 380–391 (2010).
    Google Scholar 

    51.
    Ratnam, J. et al. When is a ‘forest’ a savanna, and why does it matter?. Glob. Ecol. Biogeogr. 20, 653–660 (2011).
    Google Scholar 

    52.
    Koenker, R. quantreg: Quantile Regression. R package version 5.05 (https://CRAN.R-project.org/package=quantreg, 2013).

    53.
    Richardson, J. E. et al. Rapid and recent origin of species richness in the Cape flora of South Africa. Nature 412, 181–183 (2001).
    ADS  CAS  PubMed  Google Scholar 

    54.
    Verboom, G. A. et al. Origin and diversification of the Greater Cape flora: ancient species repository, hot-bed of recent radiation, or both?. Mol. Phylog. Evol. 51, 44–53 (2009).
    Google Scholar 

    55.
    Dynesius, M. & Jansson, R. Evolutionary consequences of changes in species’ geographical distributions driven by Milankovitch climate oscillations. Proc. Natl Acad. Sci. USA 97, 9115–9120 (2000).
    ADS  CAS  PubMed  Google Scholar 

    56.
    deMenocal, P. B. African climate change and faunal evolution during the Pliocene-Pleistocene. Earth Planet. Sci. Lett. 220, 3–24 (2004).
    ADS  CAS  Google Scholar  More

  • in

    Exploring the upper pH limits of nitrite oxidation: diversity, ecophysiology, and adaptive traits of haloalkalitolerant Nitrospira

    Community composition of Nitrospira in the saline-alkaline lakes
    Members of the genus Nitrospira are the most diverse and widespread known NOB. However, reports of Nitrospira occurrence in alkaline habitats are scarce [23, 30], and a systematic assessment of their presence and activity in such extreme environments is missing. In this study, we discovered and investigated unusually alkalitolerant Nitrospira in saline-alkaline lakes of the national park “Neusiedler See-Seewinkel”, Burgenland, Austria using targeted amplicon profiling of the 16S rRNA gene and nxrB, of which the latter encodes the beta-subunit of nitrite oxidoreductase (the key enzyme for nitrite oxidation). In sediment samples from nine lakes, we detected phylogenetically diverse Nitrospira phylotypes which were affiliated with Nitrospira lineages I, II and IV (Fig. 2) [1].
    Fig. 2: Phylogenetic maximum likelihood analysis based on the 16S rRNA gene sequences of selected representatives from the genus Nitrospira and of the Nitrospira members detected in sediments from nine saline-alkaline lakes.

    Sequences obtained in this study are printed in bold. “Ca. N. alkalitolerans” is the Nitrospira species cultured and further analyzed in this study. The tree was constructed using full length sequences and a 50% conservation filter resulting in 1310 valid alignment positions. Shorter sequences from this study, generated through amplicon and Sanger sequencing were added to the tree using the Evolutionary Placement Algorithm (EPA) without changing the overall tree topology. Numbers in brackets behind these sequences firstly denote the likelihood score of the exact placement and secondly the cumulative likelihood score of the placement within the cluster. Filled, gray, and open circles denote branches with ≥90%, ≥70% and ≥50% bootstrap support, respectively. Leptospirillum ferrooxidans (AJ237903), Ca. Magnetobacterium bavaricum (FP929063), Thermodesulfovibrio yellowstonii DSM 11347 (CP001147), and Ca. Methylomirabilis oxyfera (FP565575) were used as outgroup. The scale bar indicates 6% estimated sequence divergence.

    Full size image

    The genomes of sequenced Nitrospira possess one to six paralogous copies of nxrB, and the nxrB copy numbers per genome remain unknown for the majority of uncultured Nitrospira [42]. This large variability likely affects relative abundance estimations of Nitrospira OTUs based on nxrB amplicon data. In contrast, all sequenced Nitrospira genomes contain only one ribosomal RNA (rrn) operon. Therefore, our further assessment of the Nitrospira community structures relies on the 16S rRNA gene amplicon datasets.
    The estimated alpha-diversity of Nitrospira 16S rRNA gene phylotypes was compared across the nine examined lakes (Fig. S1). The inverse Simpson’s index of the Nitrospira communities was negatively correlated with pH and the nitrite concentration (p = 0.00004, Tau-b = −0.53 for pH and p = 0.03, Tau-b = −0.36 for nitrite). The decrease of Nitrospira diversity with increasing pH may indicate that only specific Nitrospira phylotypes tolerate highly alkaline conditions.
    The Nitrospira communities clustered into two distinct major groups (Fig. 3). Group 1 mainly comprised the communities from those lakes, which are located closely to the shore of the much larger Lake Neusiedl, whereas group 2 contained the communities from the remaining lakes that are farther away from Lake Neusiedl (Fig. 1). The average pH and salinity in the water of lakes from the group 1 cluster were 9.97 ± 0.24. and 6.1 ± 4.1 g/l, respectively. These values were significantly higher (Welch’s t-test; p = 0.00001 for pH and p = 0.017 for salinity) than the mean pH of 9.37 ± 0.26 and salinity of 2.74 ± 0.88 g/l in the group 2 lakes (Table 1). None of the other determined lake properties at time of sampling differed significantly between the two groups. The Nitrospira phylotypes with the highest relative abundance in the sediments from group 1 were OTU1 and OTU20, both affiliated with Nitrospira lineage IV, whereas these OTUs were nearly absent from the sediments of the lakes in group 2 (Fig. 3). In contrast, the predominant phylotypes in the group 2 lake sediments were affiliated with Nitrospira lineage II (Fig. 3). Consistent with these results, a principal coordinate analysis showed a clear separation of the Nitrospira communities with the same two groups separated on the first axis of the ordination (Fig. S2). These results indicate a strong influence of pH and salinity on the composition of the Nitrospira communities. Members of Nitrospira lineage IV are adapted to saline conditions and are commonly found in marine ecosystems [15, 43,44,45,46,47]. However, to date no Nitrospira species have been described to tolerate elevated pH conditions. Our results show that a substantial diversity of Nitrospira is able to colonize alkaline environments. The data also indicate a niche differentiation between lineages IV and II in saline-alkaline lakes, which likely includes a higher tolerance of the detected lineage IV organisms toward an elevated pH and salinity.
    Fig. 3: Normalized abundances of Nitrospira 16S rRNA gene phylotypes detected in triplicate sediment samples from nine saline-alkaline lakes.

    Nitrospira communities are grouped by hierarchical clustering on the y-axis, and OTUs are grouped by phylogenetic affiliation on the x-axis. Lake names are abbreviated as in  Fig. 1. Lin. IV, Nitrospira lineage IV ; Lin. II, Nitrospira lineage II; I, Nitrospira lineage I; Freq normalized frequency counts; Grp.1, group 1 lakes; Grp.2, group 2 lakes (see also Fig. 1).

    Full size image

    Metagenome sequencing and physiology of alkalitolerant Nitrospira enrichments
    Following the inoculation of mineral nitrite medium flasks with sediment and/or water samples from four saline-alkaline lakes (LL, WW, KS and OEW; abbreviations see Table 1), we initially obtained 17 enrichment cultures that oxidized nitrite to nitrate. Based on FISH analyses with Nitrospira-specific 16S rRNA gene-targeted probes and Sanger sequencing of cloned 16S rRNA genes, several of these preliminary enrichment cultures contained co-existing phylotypes from Nitrospira lineages I, II, and IV as well as from the genus Nitrobacter (data not shown). Members of the genera Nitrotoga and Nitrospina were screened for by FISH or PCR, but were not detected.
    We used three of the enrichments which contained only Nitrospira NOB and originated from different lakes (referred to as EN_A from lake OEW, EN_B from lake LL, and EN_C from lake WW comprising ~35% Nitrospira in relation to the total microbial community based on FISH analysis) to determine the pH range for activity of the enriched Nitrospira members. Enrichment cultures EN_A and EN_C contained phylotypes from Nitrospira lineages I and II, while EN_B contained phylotypes from lineages I, II, and IV as determined by 16 rRNA gene amplicon cloning and Sanger sequencing (Fig. 2). The continued presence of these Nitrospira phylotypes for more than 2 years, despite several serial dilution transfers, demonstrates their tolerance to the alkaline incubation conditions and suggests that they were native to the saline-alkaline environment which they were sampled from. Hence, we conclude that at least the highly similar uncultured Nitrospira OTUs detected by amplicon sequencing (Fig. 2) were most likely also native inhabitants of the saline-alkaline lakes. Aliquots of each enrichment culture were incubated with nitrite as the sole added energy source for six weeks at pH 7.61–7.86 and 9–9.04, respectively. During this period, pH had no significant effect on nitrite utilization (Pearson correlation coefficient ≥0.96 with, p ≤ 0.01 for all three enrichments) and nitrate production (Pearson correlation coefficient ≥0.98 with, p ≤ 0.01 for all three enrichments) over time for any of the three enrichments (Fig. S3). Subsequently, the enrichment culture aliquots that had been incubated at pH 9–9.04 were sequentially incubated at pH 9.97–10, 10.24–10.52, and 10.72–11.02 for eight to nine days at each pH (Table S1). For all three enrichments, the observed nitrate production tended to be slower at pH 9.97–10 and 10.24–10.52 than at pH 9–9.04 (Fig. S3 and S4). At pH 10.72–11.02, no nitrite consumption was detected (Fig. S4). The trends observed at pH 10.24–10.52 and above were in stark contrast to the persistently high nitrite-oxidizing activity of the enrichments when routinely cultured at pH 9–10 for several weeks. While it was not possible to determine based on our data whether all Nitrospira phylotypes present in the three enrichments responded equally to the tested pH conditions, we can conclude that the activity of at least some Nitrospira remained unaffected up to pH 9 and had an upper limit between pH 10.5 and 10.7. This is remarkable, because previously enriched or isolated Nitrospira strains were not cultivated above pH 8.0 except for two Nitrospira cultures from geothermal springs, which showed activity up to pH 8.8 [4] or pH 9.0 [7]. To our knowledge, this is the first report of nitrite oxidation by Nitrospira at pH values above 9 and as high as 10.5.
    Further analyses focused on one additional enrichment, which had been inoculated with sediment from lake Krautingsee, belonging to the group 2 of the analyzed lakes (KS, Table 1). In contrast to the other enrichment cultures, this enrichment contained only lineage IV Nitrospira based on FISH analysis (Fig. 4a). Nitrospira-specific, 16S rRNA gene and nxrB-targeted PCR and phylogeny detected one phylotype from Nitrospira lineage IV that was related to other phylotypes detected from the lakes, specifically OTU 5 and EN_B_1 (16S rRNA gene, 100% and 98% nucleotide sequence identity, respectively; Fig. 2) and OTU 2 (nxrB, 98.5% nucleotide sequence identity; Fig. S5). Both these OTU phylotypes occurred in most of the analyzed lakes (Fig. 3). Thus, the closely related enrichment from lake KS may represent Nitrospira that could adapt to a relatively broad range of conditions, while some of the other OTUs were more abundant in specific lakes only (Fig. 3). The enriched Nitrospira reached a high relative abundance in the enrichment culture of ~60% of all bacteria based on metagenomic read abundance (see below) and observation by FISH.
    Fig. 4: Visualization and metagenomic analysis of the “Ca. N. alkalitolerans” enrichment.

    a FISH image showing dense cell clusters of “Ca. N. alkalitolerans” in the enrichment culture. The “Ca. N. alkalitolerans” cells appear in red (labeled by probe Ntspa1151 which has 1 mismatch at the 3’ end to the 16S rRNA gene sequence of “Ca. N. alkalitolerans”; the absence of lineage II Nitrospira in the enrichment culture was confirmed by the application of the competitor oligonucleotides c1Ntspa1151 and c2Ntspa1151 as indicated in the Supplementary text). Other organisms were stained by DAPI and are shown in light gray. Scale bar, 25 µm. b Phylogenetic affiliation of the metagenome scaffolds from the “Ca. N. alkalitolerans” enrichment, clustered based on sequence coverage and the GC content of DNA. Closed circles represent scaffolds, scaled by the square root of their length. Clusters of similarly colored circles represent potential genome bins.

    Full size image

    High-throughput metagenome sequencing, scaffold assembly, and binning revealed that the enrichment contained three Nitrospira strains that could be separated into three genome bins based on sequence coverage data (Table S2, Fig. S6). No other NOB were identified in the metagenome, and the three Nitrospira bins represented the most abundant organisms in the enrichment culture (Fig. 4b). Since the genome-wide average nucleotide identity (gANI) values were above the current species threshold of 95% [48] (Table S2), the three bins likely represented very closely related strains of the same Nitrospira lineage IV species with unique genetic components. From the predominant (based on coverage data) Nitrospira sequence bin, an almost complete metagenome-assembled genome (MAG) was reconstructed, which met the criteria for a “high-quality draft” genome [49] (Table S2), and used for comparative genomic analysis. Genome-wide, pairwise comparison of the gANI and average amino acid (gAAI) identity between this MAG and Nitrospira marina as the only other genome-sequenced and cultured Nitrospira lineage IV representative resulted in values of 80.1 and 77.3, respectively. The 16S rRNA gene, which had been retrieved from the MAG, was 97.90% identical to the 16S rRNA gene of N. marina, 97.87% identical to “N. strain Ecomares 2.1”, 94.92% to “Ca. N. salsa”, and 94.51% to “Nitrospira strain Aa01”, which are the other cultured members of Nitrospira lineage IV [15, 43, 46, 47]. These values are below the current species threshold of 98.7–99% for 16S rRNA genes [50]. Based on the low gANI and 16S rRNA gene sequence identities to described Nitrospira species, and additionally considering the distinct haloalkalitolerant phenotype (see also below), we conclude that the enriched Nitrospira represent a new species and propose “Ca. Nitrospira alkalitolerans” as the tentative name.
    The enrichment culture was maintained at a pH of 9–10 and a salt concentration of 2 g/l, resembling the natural conditions in the saline-alkaline lakes based on available data from 5 years. “Ca. N. alkalitolerans” grew in dense flocks (Fig. 4a), thereby possibly relieving the pH stress [51]. Its nitrite-oxidizing activity was not affected when the pH in the cultivation medium decreased below 8. However, no nitrite oxidation was observed when the enrichment culture was transferred into medium with 4× to 8× higher salt concentrations, the latter resembling marine conditions. Thus, “Ca. N. alkalitolerans” is best described as a facultatively haloalkalitolerant organism that oxidizes nitrite as an energy source over a wide range of pH and under hyposaline conditions. This phenotype is certainly advantageous in the investigated saline-alkaline lakes, as these lakes are prone to evaporation in summer, which causes a temporarily elevated salinity and alkalinity in the remaining water body and the sediment [35].
    The enrichment culture of “Ca. N. alkalitolerans” oxidized nitrite over a broad range of initial nitrite concentrations tested, although an extended lag phase of 10–15 days occurred at the higher concentrations of 0.7 and 1 mM nitrite (Fig. S7). Similarly, a lag phase at elevated nitrite concentrations was also observed for the Nitrospira lineage II member Nitrospira lenta [52]. A preference for low nitrite levels is consistent with the presumed ecological role of nitrite-oxidizing Nitrospira as slow-growing K-strategists, which are adapted to low nitrite concentrations [50, 52, 53].
    Genomic adaptations to the saline-alkaline environment
    As described below, comparative genomic analysis of “Ca. N. alkalitolerans” revealed several features that distinguish this organism from other known NOB and likely form the basis of its tolerance toward elevated alkalinity and salinity (Fig. 5).
    Fig. 5: Cell metabolic cartoon constructed from the genome annotation of “Ca. N. alkalitolerans”.

    Features putatively involved in the adaptation to high alkalinity and salinity, and selected core metabolic pathways of chemolithoautotrophic nitrite-oxidizing Nitrospira, are shown. Note that the transport stoichiometry of the ion transporters in “Ca. N. alkalitolerans” remains unknown. Colors of text labels indicate whether adaptive features are present (i.e., have homologs) in the genomes of other NOB (red, feature is not present in any other characterized NOB; blue, feature is present only in the marine Nitrospina gracilis; purple, feature is present in several other characterized NOB).

    Full size image

    Cytoplasmic pH and ion homeostasis
    At high pH, alkaliphilic and alkalitolerant microbes maintain a higher transmembrane electrical potential (ΔΨ) component of the proton motive force (PMF) than usually found in neutrophiles. The high ΔΨ is required to maintain PMF, because the ΔpH component of the PMF is reversed when the extracellular pH is higher than the intracellular pH [54]. Like in neutrophiles, the ΔΨ of alkaliphiles is negative inside the cell relative to the outside [54]. Furthermore, the intracellular pH must be kept below the (extremely) alkaline extracellular pH. At elevated salinity, resistance against high salt concentrations is an additional, fundamental necessity for survival. All this requires a tightly regulated pH and ion homeostasis, in which cation transmembrane transporters play key roles [54,55,56]. The “Ca. N. alkalitolerans” genome codes for various Na+-dependent transporters (Fig. 5, Table S3) including secondary Na+/H+ antiporters that are involved in pH homeostasis in other organisms: two copies of a group 3 Mrp-type Na+/H+ antiporter [57, 58] encoded by the seven genes mrpA-G, and monovalent cation-proton antiporters of the types NhaA and NhaB, each of which is encoded by a single gene [59]. The Mrp antiporter is crucial for growth at high pH and elevated salinity in alkaliphilic Halomonas spp. and Bacillus spp., where it exports Na+ and imports H+, thus contributing to the maintenance of a lower intracellular pH compared to the environment (e.g., cytoplasmic pH 8.3 at external pH ~ 10.5) [[60] and references cited therein, [55]]. The Mrp proteins may form a large surface at the outside of the cytoplasmic membrane that could support proton capture under alkaline conditions [54, 57]. Nha-type antiporters are widely distributed among non-extremophilic and extremophilic organisms [55]. Being involved in the homeostasis of Na+ and H+, they are important for survival under saline and/or alkaline conditions [56]. In E. coli, NhaA is regulated by the cytoplasmic pH and it catalyzes the import of 2H+ with the concurrent export of one Na+. This electrogenic activity is driven by ΔΨ and maintains pH homeostasis at elevated external pH [[52] and references cited therein]. The simultaneous presence of the two antiporters NhaA and NhaB has been associated with halophilic or haloalkaliphilic phenotypes in other organisms [55, 59]. Although the regulation and cation transport stoichiometry of the homologs in “Ca. N. alkalitolerans” remain unknown, the Mrp- and Nha-family antiporters most likely exhibit important physiological roles in this organism and support its survival under haloalkaline conditions. Possibly, “Ca. N. alkalitolerans” can even combine its growth in dense flocks with the extrusion of protons by its numerous proton transporters thereby lowering the pH inside the flock [51].
    One of the two nhaB genes present in the “Ca. N. alkalitolerans” genome is located in an interesting genomic region that also contains all genes encoding the group 3 Mrp-type Na+/H+ antiporter (Fig. S8). The two genes downstream from mrpD display sequence similarity to the NADH dehydrogenase (complex I) subunits NuoM and NuoL. However, based on the genomic context they are more likely additional mrpA- and/or mrpD-like genes, as these Na+/H+ antiporter subunits are evolutionary related to NuoM and NuoL [61]. Multiple copies of subunits NuoM and NuoL of the NADH dehydrogenase are encoded elsewhere in the genome, partially in larger nuo operons (see Table S3). Moreover, the locus contains one gene coding for the low-affinity, high flux Na+/HCO3− uptake symporter BicA [62] and gene motB encoding a H+-translocating flagellar motor component (Fig. S8). In the haloalkalitolerant cyanobacterium Aphanothece halophytica, a similar clustering of bicA with genes coding for Na+/H+ antiporters has been described. The authors proposed a model of cooperation between these transporters, where Na+ extruded by the Na+/H+ antiporters could drive the uptake of HCO3− by BicA under alkaline conditions when CO2 becomes limiting [63]. Sodium-driven import of HCO3− could be an essential feature for “Ca. N. alkalitolerans”, because bicarbonate is the main source of inorganic carbon for autotrophic organisms, but becomes less accessible at high pH >10 [55]. A carbonic anhydrase, which is also present in the genome (Fig. 5, Table S3), can convert the imported HCO3− to CO2 for carbon fixation via the reductive tricarboxylic acid cycle (Fig. 5).
    Since cytoplasmic K+ accumulation may compensate for Na+ toxicity at elevated intracellular pH [64], many alkaliphiles retain an inward directed K+ gradient [55]. The potassium uptake transporters of the Trk family contribute to pH and K+ homeostasis of halo- and/or alkaliphiles [55]. TrkAH catalyzes the NAD+-regulated uptake of K+ possibly coupled with H+ import [65]. Moreover, kinetic experiments revealed that TrkAH of the gammaproteobacterium Alkalimonas amylolytica is salt-tolerant and functions optimally at pH > 8.5 [66]. “Ca. N. alkalitolerans” encodes a TrkAH complex (Fig. 5, Table S3), which may be a specific adaptation to its haloalkaline environment as no homologous K+ transporter has been identified yet in any other NOB genome. Under more neutral pH conditions, Kef-type K+ efflux pumps, which are present in two copies in the “Ca. N. alkalitolerans” genome, could excrete excess K+ (Fig. 5, Table S3).
    Adaptations of the energy metabolism
    Aside from the different cation transporters (see above), “Ca. N. alkalitolerans” also encodes several mechanisms for cation homeostasis that are linked to membrane-bound electron transport and energy conservation. Like in other aerobic alkaliphiles [56], ATP synthesis is likely catalyzed by a canonical, H+-translocating F1FO-ATPase (Fig. 5, Table S3). In addition, the genome contains all genes of a predicted Na+-translocating N-ATPase [67] (Fig. 5, Fig. S9, Table S3). N-ATPases form a separate subfamily of F-type ATPases and have been suggested to be ATP-driven ion pumps that extrude Na+ cations [67] or H+ [68]. The c subunit of the N-ATPase in the genome of “Ca. N. alkalitolerans” contains the typical amino acid motifs for Na+ binding and transport [67] (Fig. S10). Subunits a and c of the N-ATPase, which are involved in ion transport, are most similar to homologs from the halotolerant, sulfate-reducing Desulfomicrobium baculatum (81.5% AA identity) and the haloalkalitolerant, sulfur-oxidizing Sulfuricella denitrificans (88.2% AA identity), respectively. Hence, in “Ca. N. alkalitolerans”, the N-ATPase may contribute to the maintenance of ΔΨ, the generation of a sodium motive force (SMF), and salt resistance (Fig. 5).
    The genome of “Ca. N. alkalitolerans” encodes two different types of NADH:quinone oxidoreductase (complex I of the electron transport chain) (Fig. 5, Table S3). Firstly, the organism possesses all 14 genes of type I NADH dehydrogenase (nuoA to nuoN). They are present in one to three copies each. The nuo genes are mostly clustered at several genomic loci (Table S3) and are most similar to either of the two nuo operons present in Nitrospira defluvii [39], with AA identities between 41% and 90%. As mentioned above, nuoL/M-like genes at loci without other nuo genes might represent subunits of cation antiporters.
    The genome furthermore contains a locus encoding all six subunits of a Na+-dependent NADH:quinone oxidoreductase (Nqr or type III NAD dehydrogenase) (Fig. 5, Table S3). The locus is situated on a single contig in the vicinity of transposase genes, indicating that “Ca. N. alkalitolerans” might have received this type of complex I by lateral gene transfer. The gene of subunit E, which takes part in Na+ translocation [69], is most similar to a homolog in the ammonia-oxidizing bacterium Nitrosomonas nitrosa (86% AA identity).
    The metabolic model for N. defluvii [39] assumes that two different versions of the H+-dependent complex I (Nuo) are used for forward or reverse electron transport, respectively. Nitrospira possess a canonical Nuo that is likely used for PMF generation during the forward flow of low-potential electrons from the degradation of intracellular glycogen or from hydrogen as an alternative substrate (see also below). In addition, reverse electron transport is essential in NOB to generate reducing power for CO2 fixation. In Nitrospira, a second (modified) form of Nuo with duplicated proton-translocating NuoM subunits might use PMF to lift electrons from quinol to ferredoxin [70]. The reduced ferredoxin is required for CO2 fixation via the rTCA cycle. As expected, “Ca. N. alkalitolerans” possesses these two Nuo forms that are conserved in other characterized Nitrospira members. In addition, the Na+-dependent Nqr complex might function in two directions in “Ca. N. alkalitolerans” as well. During forward electron flow, Nqr would contribute to SMF generation (Fig. 5). Reverse operation of the Nqr could generate NADH while importing Na+, thus utilizing SMF for the reduction of NAD+ with electrons derived from quinol (Fig. 5). Hence, the two types of complex I are likely involved in essential electron transport and the fine-tuning of PMF and SMF. They probably cooperate with the Na+- and the H+-translocating ATPases and the various cation transporters (see above) to adjust the cytoplasmic ion concentrations and the membrane potential in response to the environmental salinity and pH.
    In addition to a novel “bd-like” cytochrome c oxidase, which is commonly found in Nitrospira genomes [16, 39], the genome of “Ca. N. alkalitolerans” contains a locus with fused genes for a cbb3-type cytochrome c oxidase (Fig. 5, Table S3) similar to the one present in the marine nitrite oxidizer Nitrospina gracilis [41]. The cbb3-type terminal oxidases usually exhibit high affinities for O2 [71] and may allow “Ca. N. alkalitolerans” to sustain respiration at low oxygen levels.
    Interestingly, “Ca. N. alkalitolerans” encodes two different hydrogenases and the accessory proteins for hydrogenase maturation (Fig. 5, Table S3). First, it possesses a group 2a uptake hydrogenase that is also found in N. moscoviensis, which can grow autotrophically on H2 as the sole energy source [16]. Second, “Ca. N. alkalitolerans” codes for a putative bidirectional group 3b (sulf)hydrogenase that also occurs in other NOB and in comammox Nitrospira [18, 41] but has not been functionally characterized in these organisms. Experimental confirmation of H2 utilization as an alternative energy source and electron donor by “Ca. N. alkalitolerans” is pending. However, we assume that this capability would confer ecophysiological flexibility, especially if nitrite concentrations fluctuate and H2 is available at oxic-anoxic boundaries in biofilms or upper sediment layers. While electrons from the group 2a hydrogenase are probably transferred to quinone [16], the group 3b hydrogenase might reduce NAD+ [41] and fuel forward electron transport through the Nuo and Nqr complexes (see above).
    Osmoadaptation
    The intracellular accumulation of compatible solutes is an important mechanism allowing microorganisms to withstand the high osmotic pressure in saline habitats [55]. “Ca. N. alkalitolerans” has the genetic capacity to synthesize or import the compatible solutes trehalose, glycine betaine, and glutamate (Fig. 5). For trehalose synthesis the gene treS of trehalose synthase (Table S3), which enables trehalose synthesis from maltose, is present. The genes opuD and opuCB for glycine betaine import (Table S3) have been identified in the marine Nitrospina gracilis [41], but not yet in any Nitrospira species. For glutamate synthesis, the genes gltB and gltD were identified (Table S3). They code for the alpha and beta subunits of glutamate synthase, which catalyzes L-glutamate synthesis from L-glutamine and 2-oxoglutarate with NADPH as cofactor. In addition, we identified adaptations of “Ca. N. alkalitolerans” to the low availability of iron and the presence of toxic arsenite in saline-alkaline systems (Supplementary text). More

  • in

    Climate and atmospheric deposition effects on forest water-use efficiency and nitrogen availability across Britain

    Site and sampling
    We selected twelve monoculture tree stands of the most common tree species in Britain, Scots pine (Pinus sylvestris L.), Sitka spruce (Picea sitchensis Bong. Carr.), pedunculate oak (Quercus robur L.) and common beech (Fagus sylvatica L.). The majority of the stands were experimental sites within the Level II- ICP intensive forest monitoring network (http://icp-forests.net/), with the exception of Covert Wood, Shobdon and Goyt. The Goyt site was added as a high Ndep site as a contrast to the low Ndep Sitka spruce site in Scotland (Fig. 1, Table 1, Supplementary Table 1). For each species, forests were selected with similar soil type and age, but with contrasting Ndep, Sdep and climate, particularly rainfall and temperature, as described in Fig. 1, Table 1 and Supplementary Table 1. Stand information (mean tree height, mean diameter at the breast height and basal area) as measured for target years and for some of the forest stands are shown in Fig. S4.
    At each ICP forest site, a plot of 0.25 ha was established in 1995 to carry out monitoring30 and a similar protocol was followed at the Goyt and Shobdon sites. Within each plot, 10 trees were selected for the collection of 3 wood cores per tree by using a 5 mm diameter increment borer, which were placed in paper straws for transport. Sampling was carried out between November 2010 and March 2011. Once in the laboratory, samples were dried at 70 °C for 48 h. Of the three wood cores sampled, one was kept for dendrochronology, with the other two used for stable isotope analyses.
    Climate and atmospheric deposition data
    Climate data (Temperature, T, Vapour Pressure Deficit, VPD, Precipitation, P) were obtained from automated weather stations at the sites and/or the nearest local meteorological stations (data were provided by the British Atmospheric Data Centre). Annual mean (Ta) and mean maximum (Tamax) values for temperature were calculated from monthly mean and maximum air temperature, T, respectively, and annual precipitation (Pa) was calculated as the sum of total monthly precipitations. Annual VPD was calculated from averaging monthly values obtained from mean monthly maximum temperature and minimum monthly relative humidity. For all the parameters, mean values were also calculated over the growing season, i.e., from May to September. We also considered the standardised precipitation-evapo-transpiration index, SPEI, relative to August, with 1 (SPEI8_1), 2 (SPEI8_2) and 3 (SPEI8_3) months time-scale and SPEI relative to December, with 1 and 12 months time-scale, the latter providing year-cumulated soil moisture conditions. SPEI values were obtained from the global database with 0.5 degrees spatial resolution available online at: https://sac.csic.es/spei/.
    Yearly wet nitrogen (Ndep) and sulphur deposition (Sdep) were obtained from measured bulk precipitation and throughfall water volumes at the sites and measured elemental concentrations (NO3−, NH4+ and SO2–4) as previously described30. For the spatial analyses, we considered mean of annual deposition (sNdep and sSdep), obtained as the sum of total (NH4-N + NO3-N for Ndep) wet and dry deposition. The latter were estimated as difference between throughfall and bulk precipitation N fluxes30. For Rogate only 1 year (2010) of monitoring was available. For Goyt site, atmospheric deposition data collected at Ladybower were considered, as the two sites are 30 km apart. For two sites (i.e., Shobdon and Covert Wood), which were not part of the regular ICP forest sites, the wet deposition obtained from the UK 5 × 5 km grid Ndep and Sdep dataset was used4. The estimate included wet and dry NHx-N (NH4, NH3), NOy-N (NO2, NO3, HNO3) and S (SOx = SO2 and SO4) deposition, modelled using FRAME with 2005 emissions data4. However, only the total wet deposition was included in the analyses here, as we previously reported a good agreement between modelled and measured wet Ndep50.
    For the temporal analyses, only wet deposition (as calculated from NO3−, NH4+ and SO2–4 concentrations in bulk precipitation) was considered (indicated as aNdep and aSdep), given the uncertainties associated with the quantification of the dry deposition. For instance, when differences between throughfall and bulk precipitation are  More

  • in

    Elevational is the main factor controlling the soil microbial community structure in alpine tundra of the Changbai Mountain

    1.
    Ruan, H., Zou, X., Scatena, F. & Zimmerman, J. Asynchronous fluctuation of soil microbial biomass and plant litterfall in a tropical wet forest. Plant Soil 260, 147–154 (2004).
    CAS  Google Scholar 
    2.
    Ibekwe, A. M. et al. Impact of fumigants on soil microbial communities. Appl. Environ. Microbiol. 67, 3245–3257 (2001).
    CAS  PubMed  PubMed Central  Google Scholar 

    3.
    Reiss, J., Bridle, J. R., Montoya, J. M. & Woodward, G. Emerging horizons in biodiversity and ecosystem functioning research. Trends Ecol. Evol. 24, 505–514 (2009).
    PubMed  Google Scholar 

    4.
    Fuhrman, J. A. & Fuhrman, J. A. Microbial community structure and its functional implications. Nature 459, 193–199 (2009).
    ADS  CAS  PubMed  Google Scholar 

    5.
    Lanzen, A. et al. Multi-targeted metagenetic analysis of the influence of climate and environmental parameters on soil microbial communities along an elevational gradient. Sci. Rep. 6, 28257. https://doi.org/10.1038/srep28257 (2016).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    6.
    Berendsen, R. L., Pieterse, C. M. J. & Bakker, P. A. H. M. The rhizosphere microbiome and plant health. Trends Plant Sci. 17, 478–486 (2012).
    CAS  PubMed  Google Scholar 

    7.
    Oh, Y. M. et al. Distinctive bacterial communities in the rhizoplane of four tropical tree species. Microb. Ecol. 64, 1018–1027 (2012).
    PubMed  Google Scholar 

    8.
    Walker, T. S., Bais, H. P., Grotewold, E. & Vivanco, J. M. Root exudation and rhizosphere biology. Plant Physiol. 132, 44–51 (2003).
    CAS  PubMed  PubMed Central  Google Scholar 

    9.
    Herre, E. A. Negative plant-soil feedback predicts tree-species relative abundance in a tropical forest. Nature 466, 752–755 (2010).
    ADS  PubMed  Google Scholar 

    10.
    Philippot, L., Raaijmakers, J. M., Lemanceau, P. & Wh, V. D. P. Going back to the roots: The microbial ecology of the rhizosphere. Nat. Rev. Microbiol. 11, 789–799 (2013).
    CAS  PubMed  Google Scholar 

    11.
    Hackl, E., Pfeffer, M., Donat, C., Bachmann, G. & Zechmeister-Boltenstern, S. Composition of the microbial communities in the mineral soil under different types of natural forest. Soil Biol. Biochem. 37, 661–671 (2005).
    CAS  Google Scholar 

    12.
    Park, S. et al. Principal component analysis and discriminant analysis (PCA–DA) for discriminating profiles of terminal restriction fragment length polymorphism (T-RFLP) in soil bacterial communities. Soil Biol. Biochem. 38, 2344–2349 (2006).
    CAS  Google Scholar 

    13.
    Mendes, R. et al. Deciphering the rhizosphere microbiome for disease-suppressive bacteria. Science 332, 1097–1100 (2011).
    ADS  CAS  PubMed  Google Scholar 

    14.
    Klimeš, L. Alpine plant life. Functional plant ecology of high mountain ecosystems by C. Körner. Folia Geobot. 41, 454–455 (2006).
    Google Scholar 

    15.
    Diaz, H. F., Grosjean, M. & Graumlich, L. Climate variability and change in high elevation regions: Past, present and future. Clim. Change 59, 1–4 (2003).
    Google Scholar 

    16.
    Schinner, F. & Gstraunthaler, G. Adaptation of microbial activities to the environmental conditions in alpine soils. Oecologia 50, 113–116 (1981).
    ADS  PubMed  Google Scholar 

    17.
    Cui, H.-J. et al. Soil microbial community composition and its driving factors in alpine grasslands along a mountain elevational gradient. J. Mt. Sci. 13, 1013–1023. https://doi.org/10.1007/s11629-015-3614-7 (2016).
    Article  Google Scholar 

    18.
    Zhang, B., Liang, C., He, H. & Zhang, X. Variations in soil microbial communities and residues along an altitude gradient on the northern slope of changbai mountain, china. PLoS ONE 8, e66184 (2013).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    19.
    Collins, C. G., Carey, C. J., Aronson, E. L., Kopp, C. W. & Diez, J. M. Direct and indirect effects of native range expansion on soil microbial community structure and function. J. Ecol. 104, 1271–1283 (2016).
    Google Scholar 

    20.
    Margesin, R., Jud, M., Tscherko, D. & Schinner, F. Microbial communities and activities in alpine and subalpine soils. FEMS Microbiol. Ecol. 67, 208–218 (2009).
    CAS  PubMed  Google Scholar 

    21.
    Fierer, N. & Mcculley, R. L. Reconstructing the microbial diversity and function of pre-agricultural tallgrass prairie soils in the United States. Science 342, 621–624 (2013).
    ADS  CAS  PubMed  Google Scholar 

    22.
    Shi, Y. et al. Multi-scale variability analysis reveals the importance of spatial distance in shaping Arctic soil microbial functional communities. Soil Biol. Biochem. 86, 126–134 (2015).
    CAS  Google Scholar 

    23.
    Yang, Y. et al. The microbial gene diversity along an elevation gradient of the Tibetan grassland. Isme J. Multidiscipl. J. Microb. Ecol. 8, 430–440 (2013).
    Google Scholar 

    24.
    Ding, J. et al. Integrated metagenomics and network analysis of soil microbial community of the forest timberline. Sci. Rep. 5, 7994 (2015).
    CAS  PubMed  PubMed Central  Google Scholar 

    25.
    Liu, J. et al. High throughput sequencing analysis of biogeographical distribution of bacterial communities in the black soils of northeast China. Soil Biol. Biochem. 70, 113–122 (2014).
    CAS  Google Scholar 

    26.
    Brockett, B. F. T., Prescott, C. E. & Grayston, S. J. Soil moisture is the major factor influencing microbial community structure and enzyme activities across seven biogeoclimatic zones in western Canada. Soil Biol. Biochem. 44, 9–20 (2012).
    CAS  Google Scholar 

    27.
    Uroz, S., Tech, J. J., Sawaya, N. A., Frey-Klett, P. & Leveau, J. H. J. Structure and function of bacterial communities in ageing soils: Insights from the Mendocino ecological staircase. Soil Biol. Biochem. 69, 265–274 (2014).
    CAS  Google Scholar 

    28.
    Shi, Y. et al. Vegetation-associated impacts on arctic tundra bacterial and microeukaryotic communities. Appl. Environ. Microbiol. 81, 492–501 (2014).
    PubMed  Google Scholar 

    29.
    Zeglin, L. H. & Myrold, D. D. Fate of decomposed fungal cell wall material in organic horizons of old-growth douglas-fir forest soils. Soil Sci. Soc. Am. J. 77, 489–500 (2013).
    ADS  CAS  Google Scholar 

    30.
    Wallander, H., Göransson, H. & Rosengren, U. Production, standing biomass and natural abundance of 15N and 13C in ectomycorrhizal mycelia collected at different soil depths in two forest types. Oecologia 139, 89–97 (2004).
    ADS  PubMed  Google Scholar 

    31.
    Colpaert, J. V., Laere, A. V. & Assche, J. A. V. Carbon and nitrogen allocation in ectomycorrhizal and non-mycorrhizal Pinus sylvestris L. seedlings. Tree Physiol. 16, 787–793 (1996).
    CAS  PubMed  Google Scholar 

    32.
    Zhang, M. et al. Distribution of soil organic carbon fractions along the altitudinal gradient in Changbai mountain, China. Pedosphere 21, 615–620 (2011).
    CAS  Google Scholar 

    33.
    Wenduo, X. U., Xingyuan, H. E., Chen, W. & Liu, C. Characteristics and succession rules of vegetation types in Changbai mountain. Chin. J. Ecol. 23, 162–174 (2004).
    Google Scholar 

    34.
    Mao, Y., Yannarell, A. C., Davis, S. C. & Mackie, R. I. Impact of different bioenergy crops on N-cycling bacterial and archaeal communities in soil. Environ. Microbiol. 15, 928–942 (2012).
    PubMed  Google Scholar 

    35.
    Grayston, S. J. et al. Assessing shifts in microbial community structure across a range of grasslands of differing management intensity using CLPP, PLFA and community DNA techniques. Appl. Soil. Ecol. 25, 63–84 (2004).
    Google Scholar 

    36.
    Lauber, C. L., Hamady, M., Knight, R. & Fierer, N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl. Environ. Microbiol. 75, 5111–5120 (2009).
    CAS  PubMed  PubMed Central  Google Scholar 

    37.
    Tedersoo, L. et al. Towards global patterns in the diversity and community structure of ectomycorrhizal fungi. Mol. Ecol. 21, 4160–4170 (2012).
    PubMed  Google Scholar 

    38.
    Davey, M. L., Heegaard, E., Halvorsen, R., Kauserud, H. & Ohlson, M. Amplicon-pyrosequencing-based detection of compositional shifts in bryophyte-associated fungal communities along an elevation gradient. Mol. Ecol. 22, 368–383 (2013).
    CAS  PubMed  Google Scholar 

    39.
    Mao, Y., Yannarell, A. C. & Mackie, R. I. Changes in N-transforming archaea and bacteria in soil during the establishment of bioenergy crops. PLoS ONE 6, e24750 (2011).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    40.
    Heijden, M. G. A. V. D., Bardgett, R. D. & Straalen, N. M. V. The unseen majority: Soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecol. Lett. 11, 296–310 (2008).
    PubMed  Google Scholar 

    41.
    Steltzer, H. & Bowman, W. D. Original articles: Differential influence of plant species on soil nitrogen transformations within moist meadow alpine Tundra. Ecosystems 1, 464–474 (1998).
    CAS  Google Scholar 

    42.
    Shen, C., Ni, Y., Liang, W., Wang, J. & Chu, H. Distinct soil bacterial communities along a small-scale elevational gradient in alpine tundra. Front. Microbiol. 6, 538–541 (2014).
    CAS  Google Scholar 

    43.
    Zhang, C., Liu, G. B., Xue, S. & Xiao, L. Effect of different vegetation types on the rhizosphere soil microbial community structure in the loess plateau of China. J. Integr. Agric. 12, 2103–2113 (2013).
    Google Scholar 

    44.
    Weand, M. P., Arthur, M. A., Lovett, G. M., Mcculley, R. L. & Weathers, K. C. Effects of tree species and N additions on forest floor microbial communities and extracellular enzyme activities. Soil Biol. Biochem. 42, 2161–2173 (2010).
    CAS  Google Scholar 

    45.
    Wu, Z. et al. Terminal restriction fragment length polymorphism analysis of soil bacterial communities under different vegetation types in subtropical area. PLoS ONE https://doi.org/10.1371/journal.pone.0129397 (2015).
    Article  PubMed  PubMed Central  Google Scholar 

    46.
    Singh, D. et al. Strong elevational trends in soil bacterial community composition on Mt. Halla, South Korea. Soil Biol. Biochem. 68, 140–149 (2014).
    ADS  CAS  Google Scholar 

    47.
    Fallen, M. Linking water and nutrients through the vadose zone: A fungal interface between the soil and plant systems. J. Arid Land 206, 155–163 (2011).
    Google Scholar 

    48.
    Grayston, S. J., Griffith, G. S., Mawdsley, J. L., Campbell, C. D. & Bardgett, R. D. Accounting for variability in soil microbial communities of temperate upland grassland ecosystems. Soil Biol. Biochem. 33, 533–551 (2001).
    CAS  Google Scholar 

    49.
    Studies, R. Forest floor properties across sharp compositional boundaries separating trembling aspen and jack pine stands in the southern boreal forest. Plant Soil 345, 353–364 (2011).
    Google Scholar 

    50.
    Ruzicka, S., Edgerton, D., Norman, M. & Hill, T. The utility of ergosterol as a bioindicator of fungi in temperate soils. Soil Biol. Biochem. 32, 989–1005 (2000).
    CAS  Google Scholar 

    51.
    Weete, J. D., Abril, M. & Blackwell, M. Phylogenetic distribution of fungal sterols. PLoS ONE 5, e10899 (2010).
    ADS  PubMed  PubMed Central  Google Scholar 

    52.
    Shen, C. et al. Dramatic increases of soil microbial functional gene diversity at the treeline ecotone of Changbai mountain. Front. Microbiol. https://doi.org/10.3389/fmicb.2016.01184 (2016).
    Article  PubMed  PubMed Central  Google Scholar 

    53.
    Quideau, S. A., Chadwick, O. A., Benesi, A., Graham, R. C. & Anderson, M. A. A direct link between forest vegetation type and soil organic matter composition. Geoderma 104, 41–60 (2001).
    ADS  CAS  Google Scholar 

    54.
    Tian, J. et al. Linkages between the soil organic matter fractions and the microbial metabolic functional diversity within a broad-leaved Korean pine forest. Eur. J. Soil Biol. 66, 57–64 (2015).
    CAS  Google Scholar 

    55.
    Yuan, Y., Si, G., Jian, W., Luo, T. & Zhang, G. Bacterial community in alpine grasslands along an altitudinal gradient on the Tibetan plateau. FEMS Microbiol. Ecol. 87, 121–132 (2013).
    PubMed  Google Scholar 

    56.
    Fierer, N. et al. Microbes do not follow the elevational diversity patterns of plants and animals. Ecology 92, 797–804 (2011).
    PubMed  Google Scholar 

    57.
    Shen, C., Ni, Y., Liang, W., Wang, J. & Chu, H. Distinct soil bacterial communities along a small-scale elevational gradient in alpine tundra. Front. Microbiol. 6, 582. https://doi.org/10.3389/fmicb.2015.00582 (2015).
    Article  PubMed  PubMed Central  Google Scholar 

    58.
    Hinsinger, P., Bengough, A. G., Vetterlein, D. & Young, I. M. Rhizosphere: Biophysics, biogeochemistry and ecological relevance. Plant Soil 321, 117–152 (2009).
    CAS  Google Scholar 

    59.
    Shen, C. et al. Contrasting elevational diversity patterns between eukaryotic soil microbes and plants. Ecology 95, 3190–3202 (2014).
    Google Scholar 

    60.
    Jarvis, S. G., Woodward, S. & Taylor, A. F. S. Strong altitudinal partitioning in the distributions of ectomycorrhizal fungi along a short (300 m) elevation gradient. New Phytol. 206, 1145–1155 (2015).
    CAS  PubMed  Google Scholar 

    61.
    Lanzén, A. et al. Multi-targeted metagenetic analysis of the influence of climate and environmental parameters on soil microbial communities along an elevational gradient. Sci. Rep. 6, 28257 (2016).
    ADS  PubMed  PubMed Central  Google Scholar 

    62.
    Shen, C. et al. Soil pH drives the spatial distribution of bacterial communities along elevation on Changbai mountain. Soil Biol. Biochem. 57, 204–211 (2013).
    CAS  Google Scholar 

    63.
    Siles, J. A. & Margesin, R. Abundance and diversity of bacterial, archaeal, and fungal communities along an altitudinal gradient in alpine forest soils: What are the driving factors?. Microb. Ecol. 72, 207–220 (2016).
    PubMed  PubMed Central  Google Scholar 

    64.
    Sagovamareckova, M., Cermak, L., Omelka, M., Kyselkova, M. & Kopecky, J. Bacterial diversity and abundance of a creek valleysites reflected soil pH and season. Open Life Sci. https://doi.org/10.1515/biol-2015-0007 (2015).
    Article  Google Scholar 

    65.
    Smith, J. L., Halvorson, J. J. & Bolton, H. Soil properties and microbial activity across a 500m elevation gradient in a semi-arid environment. Soil Biol. Biochem. 34, 1749–1757 (2002).
    CAS  Google Scholar 

    66.
    Yergeau, E., Kang, S., He, Z., Zhou, J. & Kowalchuk, G. A. Functional microarray analysis of nitrogen and carbon cycling genes across an Antarctic latitudinal transect. ISME J. 1, 163–179 (2007).
    CAS  PubMed  Google Scholar 

    67.
    Kai, X. et al. Warming alters expressions of microbial functional genes important to ecosystem functioning. Front. Microbiol. 7, 668 (2016).
    ADS  Google Scholar 

    68.
    Jing, C. et al. Available nitrogen is the key factor influencing soil microbial functional gene diversity in tropical rainforest. BMC Microbiol. 15, 397–398 (2015).
    Google Scholar 

    69.
    Griffiths, R. I. et al. The bacterial biogeography of British soils. Environ. Microbiol. 13, 1642–1654 (2011).
    PubMed  Google Scholar 

    70.
    Davey, M. L., Nybakken, L., Kauserud, H. & Ohlson, M. Fungal biomass associated with the phyllosphere of bryophytes and vascular plants. Mycol. Res. 113, 1254–1260 (2009).
    CAS  PubMed  Google Scholar 

    71.
    Meier, C. L., Rapp, J., Bowers, R. M., Silman, M. & Fierer, N. Fungal growth on a common wood substrate across a tropical elevation gradient: Temperature sensitivity, community composition, and potential for above-ground decomposition. Soil Biol. Biochem. 42, 1083–1090 (2010).
    CAS  Google Scholar 

    72.
    Tu, S., Sun, J., Guo, Z. & Gu, F. On relationship between root exudates and plant nutrition in rhizosphere. Soil Environ. 9, 64–67 (2000).
    Google Scholar 

    73.
    Zhang, C., Liu, G., Xue, S. & Song, Z. Rhizosphere soil microbial activity under different vegetation types on the Loess Plateau, China. Geoderma 161, 115–125 (2011).
    ADS  CAS  Google Scholar 

    74.
    Zeng, S., Zhiyao, S. U., Chen, B. & Yuanchun, Y. U. A review on the rhizosphere nutrition ecology research. J. Nanjing For. Univ. 27, 79 (2003).
    Google Scholar 

    75.
    Wei, Z., Xiaojuan, Q. I., Jianwei, L., Zhengxiang, Y. U. & Xia, C. Characterization of microbial community structure in rhizosphere soils of cowskin Azalea (Rhododendron aureum Georgi) on northern slope of Changbai mountains, China. Chin. Geogr. Sci. 26, 78–89 (2016).
    Google Scholar 

    76.
    Yang, X. & Wu, G. The strategy for conservation and sustainable utilization of biodiversity in Changbaishan biosphere reserve. J. For. Res. 9, 217–222 (1998).
    Google Scholar 

    77.
    Zong, S. et al. Analysis of the process and impacts of Deyeuxia angustifolia invasion on the Alpine Tundra, Changbai mountain. Acta Ecol. Sin. 34, 87–104 (2014).
    Google Scholar 

    78.
    Batten, K. M., Scow, K. M., Davies, K. F. & Harrison, S. P. Two invasive plants alter soil microbial community composition in serpentine grasslands. Biol. Invas. 8, 217–230 (2006).
    Google Scholar 

    79.
    Mebius, L. J. A rapid method for the determination of organic carbon in soil. Anal. Chim. Acta 22, 120–124 (1960).
    CAS  Google Scholar 

    80.
    Industry, D. I. Design in industry. Electr. Power 28, 228–228 (1982).
    Google Scholar 

    81.
    Murphy, J. & Riley, J. P. A modified single solution method for the determination of phosphate in natural waters. Anal. Chim. Acta 27, 31–36 (1962).
    CAS  Google Scholar 

    82.
    Brookes, P. C., Landman, A., Pruden, G. & Jenkinson, D. S. Chloroform fumigation and the release of soil nitrogen: A rapid direct extraction method to measure microbial biomass nitrogen in soil. Soil Biol. Biochem. 17, 837–842 (1985).
    CAS  Google Scholar 

    83.
    Schinner, F. & Mersi, W. V. Xylanase-, CM-cellulase- and invertase activity in soil: An improved method. Soil Biol. Biochem. 22, 511–515 (1990).
    CAS  Google Scholar 

    84.
    Johnson, J. L. & Temple, K. L. Some variables affecting the measurement of “catalase activity” in Soil1. Soil Sci. Soc. Am. J. 28, 207–209 (1964).
    ADS  CAS  Google Scholar 

    85.
    Klose, S. & Tabatabai, M. A. Urease activity of microbial biomass in soils as affected by cropping systems. Biol. Fertil. Soils 31, 191–199 (2000).
    CAS  Google Scholar 

    86.
    Vaughan, D. & Ord, B. G. An effect of soil organic matter on invertase activity in soil. Soil Biol. Biochem. 12, 449–450 (1980).
    CAS  Google Scholar 

    87.
    Djajakirana, G., Joergensen, R. G. & Meyer, B. Ergosterol and microbial biomass relationship in soil. Biol. Fertil. Soils 22, 299–304 (1996).
    CAS  Google Scholar 

    88.
    Levy-Booth, D. J., Prescott, C. E. & Grayston, S. J. Microbial functional genes involved in nitrogen fixation, nitrification and denitrification in forest ecosystems. Soil Biol. Biochem. 75, 11–25 (2014).
    CAS  Google Scholar 

    89.
    Yuan, H. et al. Abundance and composition of CO_2 fixating bacteria in relation to long-term fertilization of paddy soils. Acta Ecol. Sin. 32, 183–189 (2012).
    CAS  Google Scholar 

    90.
    Chen, J., Yu, Z., Michel, F. C. Jr., Wittum, T. & Morrison, M. Development and application of real-time PCR assays for quantification of erm genes conferring resistance to macrolides-lincosamides-streptogramin B in livestock manure and manure management systems. Appl. Environ. Microbiol. 73, 4407–4416. https://doi.org/10.1128/AEM.02799-06 (2007).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    91.
    Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).
    CAS  PubMed  Google Scholar 

    92.
    Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).
    CAS  PubMed  Google Scholar 

    93.
    Maidak, B. L. et al. The ribosomal database project (RDP). Nucleic Acids Res. 24, 82–85 (1996).
    CAS  PubMed  PubMed Central  Google Scholar 

    94.
    CoreTeam, R. R: A language and environment for statistical computing. (2013).

    95.
    Oksanen, B. J., Kindt, R., Legendre, P. & O’Hara, B. vegan: Community Ecology Package. R package version 1.8-6 (accessed 10 December 2019); https://CRAN.R-project.org/package=vegan.

    96.
    Oksanen, J. et al. vegan: Community ecology package. R package version 1.17-3. J. Stat. Softw. 48, 103–132 (2010).
    Google Scholar 

    97.
    Nd, S. A., Potvin, L. R. & Lilleskov, E. A. Fertility-dependent effects of ectomycorrhizal fungal communities on white spruce seedling nutrition. Mycorrhiza 25, 649–662 (2015).
    Google Scholar 

    98.
    Arbuckle, J. L. Amos 7.0 User’s Guide. (SPSS, 2006). More

  • in

    Biological characteristics of Trissolcus urichi (Crawford) (Hymenoptera: Scelionidae) on Euschistus heros (Fabricius) and Dichelops melacanthus (Dallas) (Hemiptera: Pentatomidae) Eggs

    1.
    Panizzi, A. R. Economic importance of stink bugs (Pentatomidae). In Heteroptera of economic importance (eds Schaefer, C. W. & Panizzi, A. R.) 421–474 (CRC Press, Boca Ratón, 2000).
    Google Scholar 
    2.
    Akin, S., Phillips, J. & Johnson, D.T. Biology, identification and management of the redbanded stink bug. Arkansas, US Cooperative Extension Service, University of Arkansas, U.S. Dept. of Agriculture, and county governments cooperating. FSA7078 (2011).

    3.
    Corrêa-Ferreira, B. S. & Azevedo, J. Soybean seed damage by diferente species of stink bugs. Agric. For. Entomol. 4, 145–150 (2002).
    Google Scholar 

    4.
    Panizzi, A. R. & Slansky, F. Jr. Review of phytophagous pentatomids (Hemiptera: Pentatomidae) associated with soybean in the Americas. Fla. Entomol. 68, 184–203 (1985).
    Google Scholar 

    5.
    Zerbino, M. S. & Panizzi, A. R. The underestimated role of pest pentatomid parasitoids in Southern South America. Arth. Plant Int. 13, 703–718 (2019).
    Google Scholar 

    6.
    Panizzi, A. R. & Corrêa-Ferreira, B. S. Dynamics in the insect fauna adaptation to soybean in the tropics. Trends Entomol. 1, 71–88 (1997).
    Google Scholar 

    7.
    Gomes, E. C., Hayashida, R. & Bueno, A. F. Dichelops melacanthus and Euschistus heros injury on maize: Basis for re-evaluating stink bug thresholds for IPM decisions. Crop Prot. 130, 105050 (2020).
    CAS  Google Scholar 

    8.
    Smaniotto, L. F. & Panizzi, A. R. Interactions of selected species of stink bugs (Hemiptera: Heteroptera: Pentatomidae) from leguminous crops with plants in the Neotropics. Florida Entomol. 98, 7–17 (2015).
    Google Scholar 

    9.
    Bueno, A. F., Bortolotto, O. C., Pomari-Fernandes, A. & França-Neto, J. B. Assessment of a more conservative stink bug economic threshold for managing stink bugs in Brazilian soybean. Crop Prot. 71, 132–137 (2015).
    Google Scholar 

    10.
    Sosa-Gómez, D.R., Corso, I.C. & Morales, L. Insecticide resistance to endosulfan, monocrotophos and methamidophos in the neotropical brown stink bug, Euschistus heros (F.) Neotrop. Entomol. 30, 317–320 (2001).

    11.
    Sosa-Gómez, D. R. & Silva, J. J. D. Neotropical brown stink bug (Euschistus heros) resistance to methamidophos in Paraná Brazil. Pesq. Agrop. Bras 45, 767–769 (2010).
    Google Scholar 

    12.
    Bueno, A. F. et al. Effects of integrated pest management, biological control and prophylactic use of insecticides on the management and sustainability of soybean. Crop Prot. 30, 937–945 (2011).
    Google Scholar 

    13.
    van Lenteren, J. C., Bolckmans, K., Köhl, J., Ravensberg, W. J. & Urbaneja, A. Biological control using invertebrates and microorganisms: plenty of new opportunities. Biocontrol 63, 39–59 (2018).
    Google Scholar 

    14.
    Koppel, A. L., Herbert, D. A. Jr., Kuhar, T. P. & Kamminga, K. Survey of stink bug (Hemiptera: Pentatomidae) egg parasitoids in wheat, soybean, and vegetable crops in southeast Virginia. Environ. Entomol. 38, 375–379 (2009).
    CAS  PubMed  Google Scholar 

    15.
    Laumann, R. A. et al. Egg parasitoid wasps as natural enemies of the Neotropical stink bug Dichelops melacanthus. Pesq. Agropec. Bras. 45, 442–449 (2010).
    Google Scholar 

    16.
    Corrêa-Ferreira, B. S. & Moscardi, F. Seasonal occurrence and host spectrum of egg parasitoids associated with soybean stink bugs. Biol. Control. 5, 196–202 (1995).
    Google Scholar 

    17.
    Cividanes, F. J. Development and emergence of Trissolcus brochymenae (Ashmead) and Telenomus podisi Ashmead (Hymenoptera: Scelionidae) at different temperatures. An. Soc. Entomol. Bras. 25, 207–211 (1996).
    Google Scholar 

    18.
    Silva, G. V., Bueno, A. F., Neves, P. M. O. J. & Favetti, B. M. Biological characteristics and parasitism capacity of Telenomus podisi (Hymenoptera: Platygastridae) on eggs of Euschistus heros (Hemiptera: Pentatomidae). J. Agric. Sci. 10, 210–220 (2018).
    Google Scholar 

    19.
    Laumann, R. A. et al. Comparative biology and functional response of Trissolcus spp. (Hymenoptera: Scelionidae) and implications for stink bugs (Hemiptera: Pentatomidae) biological control. Biol. Control. 44, 32–41 (2008).
    Google Scholar 

    20.
    Favetti, B. M., Krinski, D., Butnariu, A. R. & Loiácono, M. S. Egg parasitoids of Edessa meditabunda (Fabricius) (Pentatomidae) in lettuce crop. Rev. Bras. Entomol. 57, 236–237 (2013).
    Google Scholar 

    21.
    Margaría, C. B., Loiácono, M. S. & Lanteri, A. A. New geographic and host records for scelionid wasps (Hymenoptera: Scelionidae) parasitoids of insect pests in South America. Zootaxa 2314, 41–49 (2009).
    Google Scholar 

    22.
    Peres, W. A. A. & Corrêa-Ferreira, B. S. Methodology of mass multiplication of Telenomus podisi Ashmead and Trissolcus basalis (Hymenoptera: Scelionidae) on eggs of Euschistus heros (Hemiptera: Pentatomidae). Neotrop. Entomol. 33, 457–462 (2004).
    Google Scholar 

    23.
    Panizzi, A. R., Parra, J. R. P., Santos, C. H. & Carvalho, D. R. Rearing the southern green stink bug using artificial dry diet and artificial plant. Pesq. Agropec. Bras. 35, 1709–1715 (2000).
    Google Scholar 

    24.
    Thuler, R. T., Volpe, H. X. L., Bortoli, S. A., Goulart, R. M. & Viana, C. L. T. Metodologia para avaliação da preferência hospedeira de parasitoides do gênero Trichogramma Westood. Bol. San. Veg. 33, 333–340 (2007).
    Google Scholar 

    25.
    Queiroz, A. P., Taguti, E. A., Bueno, A. F., Grande, M. L. M. & Costa, C. O. Host preferences of Telenomus podisi (Hymenoptera: Scelionidae): parasitism on eggs of Dichelops melacanthus, Euschistus heros, and Podisus nigrispinus (Hemiptera: Pentatomidae). Neotrop. Entomol. 47, 543–552 (2018).
    CAS  PubMed  Google Scholar 

    26.
    van Lenteren, J. C. Quality control and production of biological control agents: theory and testing procedures 327 (CABI, Wallingford, 2003).
    Google Scholar 

    27.
    Shapiro, S. S. & Wilk, M. B. An analysis of variance test for normality (complete samples). Biometrika 52, 591–611 (1965).
    MathSciNet  MATH  Google Scholar 

    28.
    Burr, I. W. & Foster, L. A. A Test for Equality of Variances (University of Purdue, West Lafayette, 1972).
    Google Scholar 

    29.
    Institute, S. A. S. SAS User’s Guide: Statistics, Version 8e (SAS Institute, Cary, NC, 2009).
    Google Scholar 

    30.
    Sujii, E. R., Costa, M. L. M., Pires, C. S. S., Colazza, S. & Borges, M. Inter and intra-guild interactions in egg parasitoid species of the soybean stink bug complex. Pesq. Agropec. Bras. 37, 1541–1549 (2002).
    Google Scholar 

    31.
    Zhou, Y., Abram, P. K., Boivin, G. & Brodeur, J. Increasing host age does not have the expected negative effects on the fitness parameters of an egg parasitoid. Entomol. Exp. Appl. 151, 106–111 (2014).
    Google Scholar 

    32.
    Jones, T. S., Bilton, A. R., Mak, L. & Sait, S. M. Host switching in a generalist parasitoid: contrasting transient and transgenerational costs associated with novel and original host species. Ecol. Evol. 5, 459–465 (2015).
    PubMed  PubMed Central  Google Scholar 

    33.
    Orr, D. B. Scelionid wasps as biological control agents: a review. Florida Entomol. 71, 506–528 (1988).
    Google Scholar 

    34.
    Blackiston, D. J., Casey, E. S. & Weiss, M. R. Retention of memory through metamorphosis: can a moth remember what it learned as a caterpillar?. PlosOne 3, e1736 (2008).
    ADS  Google Scholar 

    35.
    Kaiser, L., Pham-Delegue, M. H. & Masson, C. Behavioural study of plasticity in host preferences of Trichogramma maidis (Hymenoptera: Trichogrammatidae). Physiol. Entomol. 14, 53–60 (1989).
    Google Scholar 

    36.
    Gandolfi, M., Mattiacci, L. & Dorn, S. Preimaginal learning determines adult response to chemical stimuli in a parasitic wasp. Proc. R. Soc. Lond. B 270, 2623–2629 (2003).
    Google Scholar 

    37.
    Corbet, S. A. Insect chemosensory responses: a chemical legacy hypothesis. Ecol. Entomol. 10, 143–153 (1985).
    Google Scholar 

    38.
    Pluke, R. W. H. & Leibee, G. L. Host preferences of Trichogramma pretiosum and the influence of prior ovipositional experience on the parasitism of Plutella xylostella and Pseudoplusia includes eggs. Biocontrol 51, 569–583 (2006).
    Google Scholar 

    39.
    Stephens, D. W. & Krebs, J. R. Foraging theory (Princeton University Press, Princeton, 1986).
    Google Scholar 

    40.
    Vinson, S. B. & Iwantsch, G. F. Host suitability for insect parasitoids. Annu. Rev. Entomol. 25, 397–419 (1980).
    Google Scholar 

    41.
    Bin, F., Vinson, S. B., Strand, M. R., Colazza, S. & Jones, W. A. Jr. Source of an egg kairomone for Trissolcus basalis, a parasitoid of Nezara viridula. Physiol. Entomol. 18, 7–15 (1993).
    Google Scholar 

    42.
    Borges, M. et al. Semiochemical and physical stimuli involved in host recognition by Telenomus podisi (Hymenoptera: Scelionidae) toward Euschistus heros (Heteroptera: Pentatomidae). Physiol. Entomol. 24, 227–233 (1999).
    Google Scholar 

    43.
    Borges, M. & Aldrich, J. R. Attractant pheromone for Nearctic stink bug, Euschistus obscurus (Heteroptera: Pentatomidae): insight in to a Neotropical relative. J. Chem. Ecol. 20, 1095–1102 (1994).
    CAS  PubMed  Google Scholar 

    44.
    Pomari, A. F., Bueno, A. F., Bueno, R. C. O. F. & Menezes Junior, A. O. Biological Characteristics and thermal requirements of the biological control agent Telenomus remus (Hymenoptera: Platygastridae) reared on eggs of different species of the genus Spodoptera (Lepidoptera: Noctuidae). Ann. Entomol. Soc. Am. 105, 73–81 (2012).
    Google Scholar 

    45.
    Bueno, R. C. O., Parra, J. R. P. & Bueno, A. F. Biological characteristics and thermal requirements of a Brazilian strain of the parasitoid Trichogramma pretiosum reared on eggs of Pseudoplusia includes and Anticarsia gemmatalis. Biol. Control. 51, 355–361 (2009).
    Google Scholar 

    46.
    Cônsoli, F. L., Kitajima, E. W. & Parra, J. R. P. Ultrastructure of the natural and factitious host eggs of Trichogramma galloi Zucchi and Trichogramma pretiosum Riley (Hymenoptera: Trichogrammatidae). Int. J. Insect. Morphol. Embriol. 28, 211–229 (1999).
    Google Scholar 

    47.
    Bai, B., Luck, R. F., Forster, L., Stephens, B. & Janssen, J. A. M. The effect of host size on quality attributes of the egg parasitoid Trichogramma pretiosum. Entomol. Exp. Appl. 64, 37–48 (1992).
    Google Scholar 

    48.
    Schwartz, A. & Gerling, D. Adult biology of Telenomus remus (Hymenoptera: Scelionidae) under laboratory conditions. Entomophaga 19, 482–492 (1974).
    Google Scholar 

    49.
    Charnov, E. L., Los-Den Hartogh, R. L., Jones, W. T. & Van Den Assem, J. Sex ratio evolution in a variable environment. Nature 289, 27–33 (1981).
    ADS  CAS  PubMed  Google Scholar 

    50.
    Houseweart, M. W., Jennings, D. T., Welty, C. & Southard, S. G. Progeny production by Trichogramma minutum (Hymenoptera: Trichogrammatidae) utilizing eggs for Choristoneura fumiferana (Lepidoptera: Tortricidae) and Sitotroga cerealella (Lepidoptera: Gelechiidae). Can. Entomol. 115, 1245–1252 (1983).
    Google Scholar 

    51.
    Sequeira, R. & Mackauer, M. Covariance of adult size and development time in the parasitoid wasp Aphidius ervi in relation to the size of its host Acyrthosiphon pisum. Evol. Ecol. 6, 34–44 (1992).
    Google Scholar 

    52.
    Mackauer, M. Sexual size dimorphism in solitary wasps: influence of host quality. Oikos 76, 265–272 (1996).
    Google Scholar  More