Ecology

1.
Détroit, F. et al. A new species of Homo from the Late Pleistocene of the Philippines. Nature 568, 181–186 (2019).
PubMed  Article  ADS  CAS  Google Scholar 
2.
Kaifu, Y. Archaic hominin populations in Asia before the arrival of modern humans: their phylogeny and implications for the “Southern Denisovans”. Curr. Anthropol. 58, S418–S433 (2017).
Article  Google Scholar 

3.
Reich, D. et al. Denisova admixture and the first modern human dispersals into Southeast Asia and Oceania. Am. J. Hum. Genet. 89, 516–528 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

4.
Louys, J., Curnoe, D. & Tong, H. Characteristics of Pleistocene megafauna extinctions in Southeast Asia. Palaeogeogr. Palaeoclimatol. Palaeoecol. 243, 152–173 (2007).
Article  Google Scholar 

5.
Klein, R. G. Stable carbon isotopes and human evolution. Proc. Natl Acad. Sci. USA 110, 10470–10472 (2013).
CAS  PubMed  Article  ADS  Google Scholar 

6.
Cerling, T. E. et al. Woody cover and hominin environments in the past 6 million years. Nature 476, 51–56 (2011).
CAS  PubMed  Article  ADS  Google Scholar 

7.
Heaney, L. R. in Tropical Forests and Climate (ed. Myers, N.) 53–61 (Springer, 1991).

8.
Bird, M. I., Taylor, D. & Hunt, C. Palaeoenvironments of insular Southeast Asia during the Last Glacial Period: a savanna corridor in Sundaland? Quat. Sci. Rev. 24, 2228–2242 (2005).
Article  ADS  Google Scholar 

9.
Louys, J. & Turner, A. Environment, preferred habitats and potential refugia for Pleistocene Homo in Southeast Asia. C. R. Palevol 11, 203–211 (2012).
Article  Google Scholar 

10.
Dennell, R. & Roebroeks, W. An Asian perspective on early human dispersal from Africa. Nature 438, 1099–1104 (2005).
CAS  PubMed  Article  ADS  Google Scholar 

11.
van den Bergh, G. D., de Vos, J. & Sondaar, P. Y. The Late Quaternary palaeogeography of mammal evolution in the Indonesian Archipelago. Palaeogeogr. Palaeoclimatol. Palaeoecol. 171, 385–408 (2001).
Article  Google Scholar 

12.
Steiper, M. E. Population history, biogeography, and taxonomy of orangutans (Genus: Pongo) based on a population genetic meta-analysis of multiple loci. J. Hum. Evol. 50, 509–522 (2006).
PubMed  Article  Google Scholar 

13.
Patel, R. P. et al. Two species of Southeast Asian cats in the genus Catopuma with diverging histories: an island endemic forest specialist and a widespread habitat generalist. R. Soc. Open Sci. 3, 160350 (2016).
PubMed  PubMed Central  Article  ADS  Google Scholar 

14.
Cannon, C. H., Morley, R. J. & Bush, A. B. G. The current refugial rainforests of Sundaland are unrepresentative of their biogeographic past and highly vulnerable to disturbance. Proc. Natl Acad. Sci. USA 106, 11188–11193 (2009).
CAS  PubMed  Article  ADS  Google Scholar 

15.
Sun, X., Li, X., Luo, Y. & Chen, X. The vegetation and climate at the last glaciation on the emerged continental shelf of the South China Sea. Palaeogeogr. Palaeoclimatol. Palaeoecol. 160, 301–316 (2000).
Article  Google Scholar 

16.
Louys, J. & Meijaard, E. Palaeoecology of Southeast Asian megafauna-bearing sites from the Pleistocene and a review of environmental changes in the region. J. Biogeogr. 37, 1432–1449 (2010).
Google Scholar 

17.
Raes, N. et al. Historical distribution of Sundaland’s dipterocarp rainforests at Quaternary glacial maxima. Proc. Natl Acad. Sci. USA 111, 16790–16795 (2014).
CAS  PubMed  Article  ADS  Google Scholar 

18.
Handiani, D. et al. Tropical vegetation response to Heinrich Event 1 as simulated with the UVic ESCM and CCSM3. Clim. Past Discuss. 8, 5359–5387 (2012).
Article  ADS  Google Scholar 

19.
Chabangborn, A., Brandefelt, J. & Wohlfarth, B. Asian monsoon climate during the Last Glacial Maximum: palaeo-data–model comparisons: LGM Asian monsoon climate. Boreas 43, 220–242 (2014).
Article  Google Scholar 

20.
Levin, N. E. et al. Herbivore enamel carbon isotopic composition and the environmental context of Ardipithecus at Gona, Ethiopia. Geol. S. Am. S. 446, 215–235 (2008).
Google Scholar 

21.
Cerling, T. E., Hart, J. A. & Hart, T. B. Stable isotope ecology in the Ituri Forest. Oecologia 138, 5–12 (2004).
PubMed  Article  ADS  Google Scholar 

22.
Secord, R., Wing, S. L. & Chew, A. Stable isotopes in early Eocene mammals as indicators of forest canopy structure and resource partitioning. Paleobiology 34, 282–300 (2008).
Article  Google Scholar 

23.
Fannin, L. D. & McGraw, W. S. Does oxygen stable isotope composition in primates vary as a function of vertical stratification or folivorous behaviour? Folia Primatol. 91, 219–227 (2020).
PubMed  Article  Google Scholar 

24.
Clark, P. U. et al. The middle Pleistocene transition: characteristics, mechanisms, and implications for long-term changes in atmospheric pCO2. Quat. Sci. Rev. 25, 3150–3184 (2006).
Article  ADS  Google Scholar 

25.
Sarr, A. C. et al. Subsiding Sundaland. Geology 47, 119–122 (2019).
Article  ADS  Google Scholar 

26.
Di Nezio, P. N. et al. The climate response of the Indo-Pacific warm pool to glacial sea level. Paleoceanogr 31, 866–894 (2016).
Article  ADS  Google Scholar 

27.
Roberts, P. et al. Isotopic evidence for initial coastal colonization and subsequent diversification in the human occupation of Wallacea. Nat. Commun. 11, 2068 (2020).
CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

28.
Barker, G. & Farr, L. E. Archaeological Investigations in the Niah Caves, Sarawak, The Archaeology of Niah Caves, Sarawak (McDonald Institute Monographs, 2016).

29.
Piper, P. J. & Rabett, R. J. Hunting in a tropical rainforest: evidence from the Terminal Pleistocene at Lobang Hangus, Niah Caves, Sarawak. Int. J. Osteoarchaeol. 19, 551–565 (2009).
Article  Google Scholar 

30.
Steiner, C. C., Houck, M. L. & Ryder, O. A. Genetic variation of complete mitochondrial genome sequences of the Sumatran rhinoceros (Dicerorhinus sumatrensis). Conserv. Genet. 19, 397–408 (2018).
CAS  Article  Google Scholar 

31.
Sodhi, N. S., Koh, L. P., Brook, B. W. & Ng, P. K. Southeast Asian biodiversity: an impending disaster. Trends Ecol. Evol. 19, 654–660 (2004).
PubMed  Article  Google Scholar 

32.
Spehar, S. N. et al. Orangutans venture out of the rainforest and into the Anthropocene. Sci. Adv. 4, e1701422 (2018).
PubMed  PubMed Central  Article  ADS  Google Scholar 

33.
Craig, H. The geochemistry of the stable carbon isotope. Geochim. Cosmochim. Acta 3, 53–92 (1953).
CAS  Article  ADS  Google Scholar 

34.
Smith, B. N. & Epstein, S. Two categories of 13C/12C ratios for higher plants. Plant Physiol. 47, 380–384 (1971).
CAS  PubMed  PubMed Central  Article  Google Scholar 

35.
Tieszen, L. L. Natural variations in the carbon isotope values of plants: implications for archaeology, ecology, and paleoecology. J. Archaeol. Sci. 18, 227–248 (1991).
Article  Google Scholar 

36.
Sponheimer, M. et al. Do “savanna” chimpanzees consume C4 resources? J. Hum. Evol. 51, 128–133 (2006).
CAS  PubMed  Article  Google Scholar 

37.
Sponheimer, M. et al. Isotopic evidence of early hominin diets. Proc. Natl Acad. Sci. USA 110, 10513–10518 (2013).
CAS  Article  ADS  Google Scholar 

38.
Codron, J. et al. Stable isotope series from elephant ivory reveal lifetime histories of a true dietary generalist. Proc. R. Soc. Lond. B 279, 2433–2441 (2012).
Google Scholar 

39.
Crowley, B. E. et al. Extinction and ecology retreat in a community of primates. Proc. R. Soc. Lond. B 279, 3597–3605 (2012).
Google Scholar 

40.
Farquhar, G. D., Ehleringer, J. R. & Hubick, K. T. Carbon isotope discrimination and photosynthesis. Annu. Rev. Plant Physiol. Plant Mol. Biol. 40, 503–537 (1989).
CAS  Article  Google Scholar 

41.
van der Merwe, N. J. & Medina, E. The canopy effect, carbon isotope ratios and foodwebs in Amazonia. J. Archaeol. Sci. 18, 249–259 (1991).
Article  Google Scholar 

42.
Pearcy, R. W. & Pfitsch, W. A. Influence of sunflecks on the δ13C of Adenocaulon bicolor plants occurring in contrasting forest understory microsites. Oecologia 86, 457–462 (1991).
PubMed  Article  ADS  Google Scholar 

43.
Bonafini, M., Pellegrini, M., Ditchfield, P. & Pollard A. M. Investigation of the ‘canopy effect’ in the isotope ecology of temperate woodlands. J. Archaeol. Sci. 40, 3926–3935 (2013).
Article  Google Scholar 

44.
Ehleringer, J. R., Rundel, P. W. & Nagy, K. A. Stable isotopes in physiological ecology and food web research. Trends Ecol. Evol. 1, 42–45 (1986).
CAS  PubMed  Article  Google Scholar 

45.
van der Merwe, N. J. & Medina, E. Photosynthesis and 13C/12C ratios in Amazonian rainforests. Geochim. Cosmochim. Acta 53, 1091–1094 (1989).
Article  ADS  Google Scholar 

46.
Ometto, J. P. H. B. et al. The stable carbon and nitrogen isotopic composition of vegetation in tropical forests of the Amazon Basin, Brazil. Biogeochemistry 79, 251–274 (2006).
CAS  Article  Google Scholar 

47.
Gonfiantini, R., Gratziu, S. & Tongiorgi, E. in Isotopes and Radiation in Soil Plant Nutrition Studies (Technical Report Series No. 206) (ed. Joint FAO/IAEA Division of Atomic Energy in Agriculture) 405–410 (Isotope Atomic Energy Commission, 1965).

48.
Flanagan, L. B., Comstock, J. P. & Ehleringer, J. R. Comparison of modelled and observed environmental influences on the stable oxygen and hydrogen isotope composition of leaf water in Phaseolus vulgaris L. Plant Physiol. 96, 588–596 (1991).
CAS  PubMed  PubMed Central  Article  Google Scholar 

49.
Yakir, D., Berry, J. A., Giles, L. & Osmond, C. B. Isotopic heterogeneity of water in transpiring leaves: Identification of the component that controls the δ18O of atmospheric O2 and CO2. Plant Cell Environ. 17, 73–80 (1994).
CAS  Article  Google Scholar 

50.
Sheshshayee, M. S. et al. Oxygen isotope enrichment (Δ18O) as a measure of time-averaged transpiration rate. J. Exp. Bot. 56, 3033–3039 (2005).
CAS  PubMed  Article  Google Scholar 

51.
Buchmann, N. & Ehleringer, J. R. CO2 concentration profiles, and carbon and oxygen isotopes in C3 and C4 crop canopies. Agric. For. Meteorol. 89, 45–58 (1998).
Article  ADS  Google Scholar 

52.
Buchmann, N., Guehl, J. M., Barigah, T. S. & Ehleringer, J. R. Interseasonal comparison of CO2 concentrations, isotopic composition, and carbon dynamics in an Amazonian rainforest (French Guiana). Oecologia 110, 120–131 (1997).
CAS  PubMed  Article  ADS  Google Scholar 

53.
da Silveira, L., Sternberg, L., Mulkey, S. S. & Joseph Wright, S. Oxygen isotope ratio stratification in a tropical moist forest. Oecologia 81, 51–56 (1989).
PubMed  Article  ADS  Google Scholar 

54.
McCarroll, D. & Loader, N. J. in Isotopes in Palaeonvironmental Research (ed. Leng, M. J.) 67–116 (Springer, 2006).

55.
Carter, M. L. & Bradbury, M. W. Oxygen isotope ratios in primate bone carbonate reflect amount of leaves and vertical stratification in the diet. Am. J. Primatol. 78, 1086–1097 (2016).
CAS  PubMed  Article  Google Scholar 

56.
Kohn, M. J., Schoeninger, M. J. & Valley, J. W. Herbivore tooth oxygen isotope compositions: effects of diet and physiology. Geochim. Cosmochim. Acta 60, 3889–3896 (1996).
CAS  Article  ADS  Google Scholar 

57.
Levin, N. E., Cerling, T. E., Passey, B. H., Harris, J. M. & Ehleringer, J. R. A stable isotope aridity index for terrestrial environments. Proc. Natl Acad. Sci. USA 103, 11201–11205 (2006).
CAS  PubMed  Article  ADS  Google Scholar 

58.
Lee-Thorp, J. et al. Isotopic evidence for an early shift to C4 resources by Pliocene hominins in Chad. Proc. Natl Acad. Sci. USA 109, 20369–20372 (2012).
CAS  PubMed  Article  ADS  Google Scholar 

59.
Roberts, P. et al. Fruits of the forest: Human stable isotope ecology and rainforest adaptations in Late Pleistocene and Holocene ( More

1.
Rodd, T. & Stackhouse, J. Tree: A Visual Guide (University of California Press, California, 2008).
Google Scholar 
2.
Thulin, M. Boswellia sacra. The IUCN Red List of Threatened Species 1998: e.T34533A9874201. (1998).

3.
Gebrehiwot, K., Muys, B., Haile, M. & Mitloehner, R. Introducing Boswellia papyrifera (Del.) Hochst and its non-timber forest product, frankincense. Int. For. Rev. 5, 348–353 (2003).
Google Scholar 

4.
Tolera, M. et al. Resin secretory structures of Boswellia papyrifera and implications for frankincense yield. Ann. Bot. 111, 61–68 (2013).
PubMed  Article  Google Scholar 

5.
Khan, A. L. et al. Endogenous phytohormones of frankincense producing Boswellia sacra tree populations. PLoS ONE 13, e0207910 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

6.
Environment Society of Oman. Annual report 2014. 53 (2014). Available at: https://www.eso.org.om/index/images/file/2016-03/ESO_2014_Annual_Report_English.pdf. (Accessed: 17th August 2020)

7.
Bari, R. & Jones, J. D. G. Role of plant hormones in plant defence responses. Plant Mol. Biol. 69, 473–488 (2009).
CAS  PubMed  Article  Google Scholar 

8.
Miller, B., Madilao, L. L., Ralph, S. & Bohlmann, J. Insect-induced conifer defense. White pine weevil and methyl jasmonate induce traumatic resinosis, de novo formed volatile emissions, and accumulation of terpenoid synthase and putative octadecanoid pathway transcripts in sitka spruce. Plant Physiol. 137, 369–382 (2005).
CAS  PubMed  PubMed Central  Article  Google Scholar 

9.
El Atta, H. A., Aref, I. M. & Khalil, S. A. Increased gum arabic production after infestation of acacia senegal with Aspergillus flavus and Pseudomonas pseudoalcaligenes transmitted by Agrilus nubeculosus. Afr. J. Biotechnol. 10, 7166–7173 (2011).
Google Scholar 

10.
Evert, R. F. & Eichhorn, S. E. Esau’s Plant Anatomy: Meristems, Cells, and Tissues of the Plant Body (John Wiley & Sons, New York, 2006).
Google Scholar 

11.
Miyamoto, K. Physiological and biochemical studies on exudation of gum polysaccharides in plants. Regul. Plant Growth Dev. 50, 2–11 (2015).
CAS  Google Scholar 

12.
Cabrita, P. A model for resin flow. In Plant cell and tissue differentiation and secondary metabolites 1–28 (Springer, Cham, 2019).
Google Scholar 

13.
Kuroda, K. & Shimaji, K. Traumatic resin canal formation as a marker of xylem growth. For. Sci. 29, 653–659 (1983).
Google Scholar 

14.
Krekling, T., Vincent, R. F., Berryman, A. A. & Christiansen, E. The structure and development of polyphenolic parenchyma cells in Norway spruce (Picea abies) bark. Flora 195, 354–369 (2000).
Article  Google Scholar 

15.
Nagy, N. E., Franceschi, V. R., Solheim, H., Krekling, T. & Christiansen, E. Wound-induced traumatic resin duct development in stems of Norway spruce (Pinaceae): Anatomy and cytochemical traits. Am. J. Bot. 87, 302–313 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

16.
Zeneli, G., Krokene, P., Christiansen, E., Krekling, T. & Gershenzon, J. Methyl jasmonate treatment of mature Norway spruce (Picea abies) trees increases the accumulation of terpenoid resin components and protects against infection by Ceratocystis polonica, a bark beetle-associated fungus. Tree Physiol. 26, 977–988 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

17.
Hudgins, J. W., Christiansen, E. & Franceschi, V. R. Methyl jasmonate induces changes mimicking anatomical defenses in diverse members of the Pinaceae. Tree Physiol. 23, 361–371 (2003).
CAS  PubMed  Article  PubMed Central  Google Scholar 

18.
Hudgins, J. W. & Franceschi, V. R. Methyl jasmonate-induced ethylene production is responsible for conifer phloem defense responses and reprogramming of stem cambial zone for traumatic resin duct formation. Plant Physiol. 135, 2134–3149 (2004).
CAS  PubMed  PubMed Central  Article  Google Scholar 

19.
Shain, L. Stem defense against pathogens. In Plant Stems: Physiology and Functional Morphology (ed. Gartner, B. L.) 383–406 (Academic Press, New York, 1995).
Google Scholar 

20.
Yang, Y.-X., Ahammed, G., Wu, C., Fan, S. & Zhou, Y.-H. Crosstalk among jasmonate, salicylate and ethylene signaling pathways in plant disease and immune responses. Curr. Protein Pept. Sci. 16, 450–461 (2015).
CAS  PubMed  Article  Google Scholar 

21.
Hudgins, J. W., Ralph, S. G., Franceschi, V. R. & Bohlmann, J. Ethylene in induced conifer defense: cDNA cloning, protein expression, and cellular and subcellular localization of 1-aminocyclopropane-1-carboxylate oxidase in resin duct and phenolic parenchyma cells. Planta 224, 865–877 (2006).
CAS  PubMed  Article  Google Scholar 

22.
Zhao, T. et al. The influence of Ceratocystis polonica inoculation and methyl jasmonate application on terpene chemistry of Norway spruce, Picea abies. Phytochemistry 71, 1332–1341 (2010).
CAS  PubMed  Article  Google Scholar 

23.
Pieterse, C. M. J. & Van Loon, L. C. Salicylic acid-independent plant defence pathways. Trends Plant Sci. 4, 52–58 (1999).
CAS  PubMed  Article  Google Scholar 

24.
Glazebrook, J. Contrasting mechanisms of defense against biotrophic and necrotrophic pathogens. Annu. Rev. Phytopathol. 43, 205–227 (2005).
CAS  PubMed  Article  Google Scholar 

25.
Derksen, H., Rampitsch, C. & Daayf, F. Signaling cross-talk in plant disease resistance. Plant Sci. 207, 79–87 (2013).
CAS  PubMed  Article  Google Scholar 

26.
Thaler, J. S., Humphrey, P. T. & Whiteman, N. K. Evolution of jasmonate and salicylate signal crosstalk. Trends Plant Sci. 17, 260–270 (2012).
CAS  PubMed  Article  Google Scholar 

27.
Saniewski, M., Nowacki, J. & Czapski, J. The effect of methyl jasmonate on ethylene production and ethylene-forming enzyme activity in tomatoes. J. Plant Physiol. 129, 175–180 (1987).
CAS  Article  Google Scholar 

28.
Yi, Xu. et al. Plant defense genes are synergistically induced by ethylene and methyl jasmonate. Plant Cell 6, 1077–1085 (1994).
Article  Google Scholar 

29.
Penninckx, I. A. M. A., Thomma, B. P. H. J., Buchala, A., Métraux, J. P. & Broekaert, W. F. Concomitant activation of jasmonate and ethylene response pathways is required for induction of a plant defensin gene in Arabidopsis. Plant Cell 10, 2103–2113 (1998).
CAS  PubMed  PubMed Central  Article  Google Scholar 

30.
Creelman, R. A. & Mullet, J. E. Biosysnthesis and action of jasmonates in plants. Annu. Rev. Plant Physiol. Plant Mol. Biol. 48, 355–381 (1997).
CAS  PubMed  Article  Google Scholar 

31.
Sudha, G. & Ravishankar, G. A. Elicitation of anthocyanin production in callus cultures of Daucus carota and the involvement of methyl jasmonate and salicylic acid. Acta Physiol. Plant. 25, 249–256 (2003).
CAS  Article  Google Scholar 

32.
Kunkel, B. N. & Brooks, D. M. Cross talk between signaling pathways in pathogen defense. Curr. Opin. Plant Biol. 5, 325–331 (2002).
CAS  PubMed  Article  Google Scholar 

33.
Telewski, F. W., Wakefield, A. H. & Jaffe, M. J. Computer-assisted image analysis of tissues of ethrel-treated Pinus taeda seedlings. Plant Physiol. 72, 177–181 (1983).
CAS  PubMed  PubMed Central  Article  Google Scholar 

34.
Yamamoto, F., Kozlowski, T. T. & Wolter, K. E. Effect of flooding on growth, stem anatomy, and ethylene production of Pinus halepensis seedlings. Can. J. For. Res. 17, 69–79 (1987).
CAS  Article  Google Scholar 

35.
Kusumoto, D. & Suzuki, K. Induction of traumatic resin canals in Cupressaceae by ethrel application. Mokuzaigakkaishi 47, 1–6 (2001).
Google Scholar 

36.
Lawton, K. A., Potter, S. L., Uknes, S. & Ryals, J. Acquired resistance signal transduction in Arabidopsis is ethylene independent. Plant Cell 6, 581–588 (1994).
CAS  PubMed  PubMed Central  Article  Google Scholar 

37.
Kozlowski, G., Buchala, A., Plant, J. M. P. & Métraux, J. P. Methyl jasmonate protects Norway spruce seedlings against Pythium ultimum Trow. Physiol. Mol. Plant Pathol. 55, 53–58 (1999).
CAS  Article  Google Scholar 

38.
Arango-Velez, A. et al. Differences in defence responses of Pinus contorta and Pinus banksiana to the mountain pine beetle fungal associate Grosmannia clavigera are affected by water deficit. Plant Cell Environ. 39, 726–744 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

39.
Krokene, P., Nagy, N. E. & Solheim, H. Methyl jasmonate and oxalic acid treatment of Norway spruce: Anatomically based defense responses and increased resistance against fungal infection. Tree Physiol. 28, 29–35 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
Krokene, P., Nagy, N. E. & Krekling, T. Traumatic resin ducts and polyphenolic parenchyma cells in conifers. In Induced Plant Resistance to Herbivory 147–169 (Springer, Berlin, 2008). https://doi.org/10.1007/978-1-4020-8182-8_7.
Google Scholar 

41.
WMO. World Weather Information Service. World Weather Information Service (2019). Available at: https://worldweather.wmo.int/en/city.html?cityId=113. (Accessed: 22nd August 2020)

42.
Abeles, F. B., Morgan, P. W. & Saltveit, M. E. Ethylene in plant biology (Elsevier Inc, Amsterdam, 2012). https://doi.org/10.1016/C2009-0-03226-7.
Google Scholar 

43.
Yamamoto, F., Sakata, T. & Terazawa, K. Growth, morphology, stem anatomy, and ethylene production in flooded Alnus japonica seedlings. IAWA J. 16, 47–59 (1995).
Article  Google Scholar 

44.
Du, S., Sugano, M., Tsushima, M., Nakamura, T. & Yamamoto, F. Endogenous indole-3-acetic acid and ethylene evolution in tilted Metasequoia glyptostroboides stems in relation to compression-wood formation. J. Plant Res. 117, 171–174 (2004).
CAS  PubMed  Article  Google Scholar 

45.
Wilkes, J., Dale, G. T. & Old, K. M. Production of ethylene by Endothia gyrosa and Cytospora eucalypticola and its possible relationship to kino vein formation in Eucalyptus maculata. Physiol. Mol. Plant Pathol. https://doi.org/10.1016/0885-5765(89)90024-6 (1989).
Article  Google Scholar 

46.
Popp, M. P., Johnson, J. D. & Lesney, M. S. Changes in ethylene production and monoterpene concentration in slash pine and loblolly pine following inoculation with bark beetle vectored fungi. Tree Physiol. 15, 807–812 (1995).
CAS  Article  Google Scholar 

47.
Yamamoto, F. & Kozlowski, T. Effects of flooding, tilting of stems, and ethrel application on growth, stem anatomy and ethylene production of Pinus densiflora seedlings. J. Exp. Bot. 38, 293–310 (1987).
CAS  Article  Google Scholar 

48.
Heijari, J. et al. Application of methyl jasmonate reduces growth but increases chemical defence and resistance against Hylobius abietis in Scots pine seedlings. Entomol. Exp. Appl. 115, 117–124 (2005).
CAS  Article  Google Scholar 

49.
Yalpani, N., Silverman, P., Wilson, T. M. A., Kleier, D. A. & Raskin, I. Salicylic acid is a systemic signal and an inducer of pathogenesis-related proteins in virus-infected tobacco. Plant Cell 3, 809–818 (1991).
CAS  PubMed  PubMed Central  Google Scholar 

50.
Enyedi, A. J., Yalpani, N., Silverman, P. & Raskin, I. Localization, conjugation, and function of salicylic acid in tobacco during the hypersensitive reaction to tobacco mosaic virus (systemic acquired resistance/pathogenesis-related proteins/glucosyltransferase/sigaI transduction). Plant Biol. 89, 2 (1992).
Google Scholar 

51.
Clarke, J. D., Volko, S. M., Ledford, H., Ausubel, F. M. & Dong, X. Roles of salicylic acid, jasmonic acid, and ethylene in cpr-induced resistance in Arabidopsis. Plant Cell 12, 2175–2190 (2000).
CAS  PubMed  PubMed Central  Article  Google Scholar 

52.
Kozlowski, G. & Métraux, J. P. Infection of Norway spruce (Picea abies (L.) Karst.) seedlings with Pythium irregulare Buism. and Pythium ultimum Trow.: histological and biochemical responses. Eur. J. Plant Pathol. 104, 225–234 (1998).
CAS  Article  Google Scholar 

53.
Davis, J. M. et al. Pathogen challenge, salicylic acid, and jasmonic acid regulate expression of chitinase gene homologs in pine. Mol. Plant-Microbe Interact. 15, 380–387 (2002).
CAS  PubMed  Article  Google Scholar 

54.
O’Donnell, P. J., Jones, J. B., Antoine, F. R., Ciardi, J. & Klee, H. J. Ethylene-dependent salicylic acid regulates an expanded cell death response to a plant pathogen. Plant J. 25, 315–323 (2001).
PubMed  Article  Google Scholar 

55.
Doares, S. H., Narváez-Vpsquez, J., Conconi, A. & Ryan, C. A. Salicylic acid inhibits synthesis of proteinase inhibitors in tomato leaves induced by systemin and jasmonic acid. Plant Physiol 108, 1741–1742 (1995).
CAS  PubMed  PubMed Central  Article  Google Scholar 

56.
Dekebo, A., Zewdu, M. & Dagne, E. Volatile oils of frankincense from Boswellia papyrifera. Bull. Chem. Soc. Ethiop. 13, 93–96 (1999).
CAS  Article  Google Scholar 

57.
GHCN. Global Historical Climatology Network (GHCN) | National Centers for Environmental Information (NCEI) formerly known as National Climatic Data Center (NCDC). (2020).

58.
Ahmed, A. E. M. et al. Effects of ethephon and methyl jasmonate on physicochemical properties of Acacia seyal var. seyal (L.) gum produced in Sudan. Food Hydrocoll. 90, 413–420 (2019).
CAS  Article  Google Scholar  More

In this section, we first present the general formulation of our anthrax transmission model following both a deterministic and a stochastic approach. The latter seems particularly suitable in this case because of its ability to capture the dynamics of discrete events furthering pathogen spread, especially in the case of a small host population or episodic disease transmission. Then, we illustrate the methods used to derive conditions for the establishment of sustained disease transmission, in particular considering seasonal variations of environmental forcings and herding practice.
The anthrax transmission model
Our formulation builds on a compartmental model describing the epidemiological dynamics that affect a target population (composed of susceptible and infected individuals) being exposed to environmental contamination. We focus on domestic herbivores because they are both the most at risk and the most socio-economically valuable for Arctic communities. We thus neglect any direct interaction between infected carcasses and carnivores or scavengers, and consider environmental spores as the only source of infection (i.e. by ingestion while herds graze). As little is known yet on how age and sex of the animals may influence anthrax transmission, we suppose that all animals are equally vulnerable to the infection.
Let S(t), I(t) and R(t) be the total abundances of susceptible, infected and temporarily immune animals at time t, respectively, and let H be the total size of the animal population. Differently from previous formulations, we introduce two different reservoirs of spores. The first (with abundance (B_1 (t))) accounts for fresh spores that are released after the death of infected hosts and that are immediately available on the soil surface. As these spores infiltrate, are washed away or get buried, they enter the second reservoir (with abundance (B_2 (t))), which describes long-term storage in the soil. Anthrax transmission can thus be described by the following system of ordinary differential equations:

$$begin{aligned} frac{dS}{dt}&= mu (H-S) -F(t)S +rho R end{aligned}$$
(1)

$$begin{aligned} frac{dI}{dt}&=sigma F(t)S -(mu +alpha ) I end{aligned}$$
(2)

$$begin{aligned} frac{dR}{dt}&= (1-sigma )F(t)S – (mu +rho ) R end{aligned}$$
(3)

$$begin{aligned} frac{dB_1}{dt}&=theta alpha frac{I}{A} -(delta _1+chi ) B_1 end{aligned}$$
(4)

$$begin{aligned} frac{dB_2}{dt}&= chi B_1 -delta _2 B_2 end{aligned}$$
(5)

As for susceptible animals (Eq. 1), we assume that pastoralist practices keep herd size under controlled demographic growth, with (mu H) being the constant recruitment rate compensating the natural (non disease-induced) mortality occurring at rate (mu). Susceptible animals may become infected at a rate expressed by the total force of infection, F(t), which will be described in details later. A fraction (sigma) of animals that have been exposed to anthrax spores develops symptoms and enters the infected compartment I (Eq. 2). Once infected, symptomatic animals may die as a result of the anthrax infection at rate (alpha), or die for other causes not related to the disease at rate (mu). The remaining fraction of exposed animals ((1-sigma), e.g. animals that have been exposed to lower doses of spores) may not exhibit symptom and develop a temporary immunity38. As these individuals do not shed spores, we assume that they enter directly the immune compartment R (Eq. 3). These animals lose their immunity and return to the susceptible class at rate (rho). When infected animals die of anthrax disease, spores proliferate in the host carcass. We assume that each death produces a constant number (theta) of spores, which are then released from the carcass and contaminate the surrounding environment, whose areal extent is A (Eq. 4). Both (B_1 (t)) and (B_2 (t)) are environmental densities of spores per unit area (# spores m(^{-2})). The freshly released spores decay at a rate (delta _1), or may be removed from the surface soil layer and stored in the active layer reservoir at a rate (chi) (Eq. 5). The latter rate thus conceptually encapsulates the combined effect of infiltration, runoff, and the burying of infected carcasses without appropriate sanitary precautions. Spores stored in the active layer decay at a rate (delta _2). The main processes involved in anthrax transmission dynamics have been conceptualized in Fig. 1.
The force of infection F(t) , which controls the rate at which susceptible animals get infected, depends on the concentrations of spores ((B_1), (B_2)) and the rate of exposure ((beta (t))) according to

$$begin{aligned} F(t)=beta (t)biggl (frac{B_1}{K+B_1}+eta (t)frac{B_2}{K+B_2}biggl ), end{aligned}$$
(6)

where the fraction (B_i/(K+B_i)) (for (i=1,2)) is the probability of becoming infected after being exposed to a certain density (B_i) of spores, K being the half-saturation constant (i.e. the dose of spores for which infection risk is half of its maximum value). As mentioned before, because of the processes involving active layer thaw, including e.g. cryoturbation, soil cracking, and solifluction, we assume that spores (B_2) may become available to grazing animals. The exposure to spores (B_2) is therefore influenced by active layer thawing, which has a significant seasonal component. The interaction between thawing and the release of spores is expressed through the parameter (eta (t)), which quantifies the probability of being exposed to spores (B_2) relatively to that of being exposed to freshly released spores ((B_1)). Because all the processes mentioned above are more likely to occur with thawing, we assume the probability (eta (t)) to be proportional to the depth of the active layer. We will later relax this simple assumption and investigate more complex relationships. We initially mimic the annual cycle of active layer thawing with a simple sinusoidal function (which also simplifies stability analysis via Floquet theory), so that (eta (t)) can be expressed as:

$$begin{aligned} eta (t)=max biggl (0, ; epsilon _{eta } sin biggl (frac{2pi }{365}tbiggl )biggl ) end{aligned}$$
(7)

with (epsilon _{eta }) indicating the maximum amplitude of seasonal fluctuations, i.e. the maximum probability for susceptibles to be exposed to spores (B_2) relative to spores (B_1). Note that the soil thaws only during the warmer months, during which susceptibles are potentially exposed to spores (B_2) ((eta (t) >0)). Later on, we model more realistically the annual cycle of active layer depth, relating it to a real record of surface soil temperatures via the Stefan equation39, 40, so as to better analyze anthrax risk in the Arctic environment.
Herding practices and grazing activity might vary seasonally as well, favouring increased exposure during warmer months. Therefore, we set

$$begin{aligned} beta (t)= beta _0biggl (1+epsilon _{beta }sin biggl (frac{2 pi }{365} t+2pi phi biggl )biggl ) end{aligned}$$
(8)

where (beta _0) is the average value of (beta (t)), (epsilon _{beta }) is the maximum amplitude of seasonal grazing fluctuations, while (0le phi le 1) is the temporal lag between the phases of (beta (t)) and (eta (t)). Note that t is expressed in days.
Finally, to reduce the number of model parameters, we introduce the dimensionless spore concentrations (B^{*}_1={B_1}/{K}) and (B^{*}_2={B_2}/{K}) (see equations S1–S5 in the Supplementary Information). This substitution allows the aggregation of parameters (theta), A, and K into a single one, namely (theta ^{*}=theta /(A K)).
Figure 1

Conceptual diagram of the anthrax transmission model described in Eqs. 1–5.

Full size image

Stochastic formulation
To build a stochastic version of our anthrax transmission model, we rely on an extension of the classic exact stochastic simulator algorithm (SSA)41 that has recently been proposed to describe the Haiti cholera epidemic42. In the SSA, each animal is considered individually, i.e. the abundances of susceptible and infected animals are treated as discrete variables, ({mathscr {S}}(t)) and ({mathscr {I}}(t)). Accordingly, each individual experiences stochastic events (i.e. birth, death, infection, anthrax-related death, etc.; see Table 1) that occur at different rates, (e_k), where k indicates a generic event, depending on the state of the system. The overall occurrence of events is modeled as a Poisson point process whose rate e is defined as the sum of the rates of occurrence of all possible events, i.e.

$$begin{aligned} e=sum limits _{k=1}^8 e_k . end{aligned}$$

The inter-arrival time between two subsequent events is thus an exponentially distributed random variable with mean 1/e, and the next event to occur is selected according to the probability (e_k/e)41.
Table 1 State transitions and rates of all possible events involving susceptible, infected and temporally immune (recovered) animals.
Full size table

The concentrations of anthrax spores, ({mathscr {B}}^{*}_1(t)) and ({mathscr {B}}^{*}_2(t)), are instead treated as continuous stochastic variables, because they are typically large enough to allow a continuous description. At each anthrax-related death event, ({mathscr {B}}^{*}_1(t)) undergoes a step increase of size (theta ^{*}), whereas between events spore concentrations are updated using the analytical solution of equations S4–S5 (see the Supplementary Information), with (theta ^{*}=0). In analogy with the deterministic formulation (Eq. 6), the force of infection reads

$$begin{aligned} {mathscr {F}}(t)=beta (t)biggl (frac{{mathscr {B}}^{*}_1}{K+{mathscr {B}}^{*}_1}+eta (t)frac{{mathscr {B}}^{*}_2}{K+{mathscr {B}}^{*}_2}biggl ). end{aligned}$$

Finally, a Monte Carlo approach, in which many different trajectories (realizations) of the SSA are evaluated, is used to study the long-term behaviour of the stochastic formulation of the anthrax transmission model.
Derivation of disease transmission conditions
Linear stability analysis of time-invariant systems
Conditions for long-term pathogen invasion and sustained transmission (endemicity) are first derived in the absence of seasonal fluctuations. To that end, we consider the exposure rate and the probability to be infected by spores (B_2) to be constant over time (i.e. (beta (t)=const=beta _0) and (eta (t)=const=eta _0), respectively).
Endemic anthrax transmission is possible if the disease-free equilibrium (DFE), a state of system 1–5 where ((S,I,R,B_1,B_2)=(H,0,0,0,0)), is asymptotically unstable. Linear stability analysis is used to determine a threshold condition based on the basic reproduction number43

$$begin{aligned} R_{0}=frac{sigma beta _0 theta ^{*} Halpha (delta _2+eta _0chi )}{delta _2(mu + alpha )(delta _1+chi )} . end{aligned}$$
(9)

Specifically, the DFE is asymptotically stable when (R_01), unfeasible and unstable otherwise. Clearly, (R_0=1) represents a bifurcation point, where the two equilibria collide and exchange their stability (transcritical bifurcation). For further mathematical details, the reader may refer to the Supplementary Information.
In the absence of the long-term spore reservoir (B_2), the basic reproduction number ({tilde{R}}_{0}) reads:

$$begin{aligned} {tilde{R}}_{0}=frac{sigma beta _0 theta ^{*} H alpha }{(mu + alpha )(delta _1+chi )}. end{aligned}$$
(10)

This definition will become useful in the following section.
Periodic systems: Floquet analysis
Conditions for endemic anthrax transmission to occur in a seasonally forced environment can be studied by applying Floquet theory36,37. The disease-free equilibrium of model 1–5 subject to periodic fluctuations is unstable when its maximum Floquet exponent, (xi _{max}), is positive (for further theoretical details see Supplementary Information). To compare transmission dynamics between periodic and time-independent conditions we calculated also ({overline{R}}_0) by assuming (eta (t)) and (beta (t)) to be constant and equal to their average value. For any parameter set, ({overline{R}}_0) provides information regarding the stability conditions of the system if temporal fluctuations of parameters were neglected.
Note that other parameters may vary seasonally: for instance, the spore transition rate (chi) and the decay rates of the spores stored in the two reservoirs may vary over time because of fluctuations in the environment, temperature, and freezing or thawing conditions. However, for the sake of simplicity, and also due to the lack of detailed information, in the following we limit our analyses on the coupled effect of (beta (t)) and (eta (t)) (Eqs. 8 and 7, respectively).
Model setting and data
Most of the model parameters have been estimated according to reference values proposed in the literature, as shown in Table 2. The average lifespan of domestic livestock varies widely (on average between 5 and 20 years, or even more44), depending on the animal species and herding management. Since animals with shorter life expectancy are more likely to be infected (see Supplementary Fig. S1), we assumed an average mortality rate of 0.2 years(^{-1}) as a representative case, i.e. domestic cattle with an average lifespan of 5 years. Given high mortality rates among infected herbivores14, we assumed that about 70% of infected animals develop symptoms. The remaining 30% grow a temporary immune response, ensuring animal immunity for about 6 months38. Typically, infection with anthrax bacterium leads symptomatic animals to death in about 14 days14. Then, as the spores are released from infected carcasses, we assumed they remain directly available for about 10 days before their removal from the soil surface15,32. Spores can be viable for decades14, thus we assumed an average viability of 10 years. Finally, we assumed that the probability (eta) can vary between 0 and 1, thus implying that animals can be equally exposed to the two spore reservoirs during the periods of maximum thawing. The parameters (beta) and (theta ^{*}), which quantify overall exposure and contamination, respectively, are critical in determining transmission dynamics. However, the lack of suitable epidemiological records prevents a proper estimation. Therefore, we explored different combinations of these parameters to compare different scenarios for anthrax transmission dynamics and discuss the results. While the maximum exposure rate (beta) has an easily interpretable physical meaning, the parameter (theta ^{*}) has a less immediate interpretation. We therefore illustrate results in terms of the corresponding ({tilde{R}}_{0}) (Eq. 10), that is, the basic reproduction number of the simplified model that does not account for the long-term spore reservoir (B_2). All simulations have been run with a total population size of (H=10^4) animals.
Finally, we exploited real data on the current variability of climate and permafrost dynamics to investigate the relationship between warm years (and related deeper active layers) and the risk of anthrax outbreaks. To that end, we run model simulations using the stochastic formulation and realistic forcing. To obtain the latter, we exploited a 17-year-long (2002–2018) dataset of thawing depth available at the Samoylov monitoring site (Lena River delta, northern Siberia)45 which we combined with records of soil surface temperature ((hbox {T}_{S})). We then modeled the yearly cycle of the active layer depth Z via the Stefan equation39,40 according to which (Z=Esqrt{C_S}), where E is the edaphic factor taking into account soil properties and (C_S) is the cumulative soil surface temperature, calculated when the top soil temperature is above (0,^{circ }hbox {C}). The estimated value of parameter E is equal to (2.58,hbox {cm}^circ hbox {C}^{-0.5}), when Z is in cm and (C_S) in (^{circ }hbox {C}) (see Supplementary Fig. S2). To produce synthetic time series of active layer depth to be used in the simulations, we first calculated the mean annual soil surface temperature and fitted a normal probability distribution to the 17 records. We then produced 200-year-long time-series of daily soil surface temperature randomly sampling the mean annual temperature from the normal distribution and assigning an annual pattern obtained shifting the trajectory of the average year (i.e. the year whose daily values are the averages across the available record for that specific day). Soil surface temperature is then transformed into active layer depth using the calibrated Stefan equation. In each 200-year-long model simulation, we discarded the first 100 years, which were used as model spin-up period, and retained the last 100 years for analysis. We run 100 replicas of the process, thus obtaining a total of 10,000 years of simulated anthrax incidence. Note that a 100-year-long simulation should not be interpreted as a future projection for which the hypothesis of steady climate is hardly justifiable, but rather as a computational way to obtain a large sample of simulations exploring the current climate variability without the need to repeat the spin-up phase of the model.
In the analysis described so far, we assumed the probability of contact between animals and spores (B_2), i.e. the parameter (eta (t)), to be proportional to the active layer depth. This implicitly assumes that the underground concentration of spores is uniform. However, as the potential sources of spores are on the surface, a negative gradient of spore concentration for increasing depth could be expected. Mathematically, this can be mimicked assuming a saturating relationship between the probability (eta (t)) and the active layer depth Z(t) so that the marginal increase of risk associated with a unit increase of Z decreases with the depth of the active layer itself. We have therefore explored two scenarios: in the first one (case 1) we assumed a linear relationship, i.e. (eta (t)propto Z(t)); in the second (case 2) a saturating relationship of the type (eta (t)propto Z(t)/(Z(t)+Z_0)), where (Z_0) represents the semi-saturation depth, which has been set to 0.2 m. We then scaled (eta (t)) so that the maximum value for case 1 is equal to 0.2. Accordingly, (eta (t)) in case 2 has been scaled so that it has the same long-term mean of case 1.
Table 2 Parameter values and their literature sources.
Full size table More

1.
Barlow, J. et al. The future of hyperdiverse tropical ecosystems. Nature 559, 517–526. https://doi.org/10.1038/s41586-018-0301-1 (2018).
ADS  CAS  Article  PubMed  Google Scholar 
2.
Stork, N. E. How many species of insects and other terrestrial arthropods are there on earth?. Annu. Rev. Entomol. 63, 31–45 (2018).
CAS  Article  Google Scholar 

3.
Rahbek, C. et al. Humboldt’s enigma: What causes global patterns of mountain biodiversity?. Science 365, 1108–1113. https://doi.org/10.1126/science.aax0149 (2019).
ADS  CAS  Article  PubMed  Google Scholar 

4.
Basset, Y. et al. Arthropod diversity in a tropical forest. Science 338, 1481–1484. https://doi.org/10.1126/science.1226727 (2012).
ADS  CAS  Article  PubMed  Google Scholar 

5.
Erwin, T. L. Tropical forests: Their richness in Coleoptera and other arthropod species. Coleopterists Bull. 36(1), 74–75 (1982).
Google Scholar 

6.
Sprick, P. & Floren, A. Diversity of Curculionoidea in humid rain forest canopies of Borneo: A taxonomic blank spot. Diversity 10, 116 (2018).
Article  Google Scholar 

7.
Watson, J. E. M. et al. The exceptional value of intact forest ecosystems. Nat. Ecol. Evolut. 2, 599–610. https://doi.org/10.1038/s41559-018-0490-x (2018).
Article  Google Scholar 

8.
Hammond, P. M. in Insects and the Rain Forest of South East Asia (Wallacea) (eds W. J. Knight & J. D. Holloway) 197–252 (Royal Entomological Society of London, 1990).

9.
Socolar, J. B., Gilroy, J. J., Kunin, W. E. & Edwards, D. P. How should beta-diversity inform biodiversity conservation?. Trends Ecol. Evol. 31, 67–80. https://doi.org/10.1016/j.tree.2015.11.005 (2016).
Article  PubMed  Google Scholar 

10.
Novotny, V. et al. Low beta diversity of herbivorous insects in tropical forests. Nature 448, 692–697 (2007).
ADS  CAS  Article  Google Scholar 

11.
Thormann, B. et al. Small-scale topography modulates elevational α-, β- and γ-diversity of Andean leaf beetles. Oecologia 187, 181–189. https://doi.org/10.1007/s00442-018-4108-4 (2018).
ADS  Article  PubMed  Google Scholar 

12.
12Allison, A., Samuelson, G. A. & Miller, S. E. in Canopy Arthropods (eds N.E. Stork, J. Adis, & R.K. Didham) 237–265 (Chapman & Hall, 1997).

13.
Mupepele, A.-C., Müller, T., Dittrich, M. & Floren, A. Are temperate canopy spiders tree-species specific?. PLoS ONE 9, e86571. https://doi.org/10.1371/journal.pone.0086571 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

14.
Ratnasingham, S. & Hebert, P. D. N. A DNA-based registry for all animal species: The Barcode Index Number (BIN) system. PLoS ONE 8, e66213. https://doi.org/10.1371/journal.pone.0066213 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

15.
15Miller, S. E., Hausmann, A., Hallwachs, W. & Janzen, D. H. Advancing taxonomy and bioinventories with DNA barcodes. Philos. Trans. R. Soc. B Biol. Sci.371, https://doi.org/10.1098/rstb.2015.0339 (2016).

16.
D’Souza, M. L. & Hebert, P. D. N. Stable baselines of temporal turnover underlie high beta diversity in tropical arthropod communities. Mol. Ecol. 27, 2447–2460. https://doi.org/10.1111/mec.14693 (2018).
Article  PubMed  Google Scholar 

17.
Floren, A. & Linsenmair, K. E. in Arthropods of Tropical Forests: Spatio-Temporal Dynamics and Resource Use in the Canopy (eds Y. Basset, V. Novotny, S. Miller, & R. Kitching) 190–197 (Cambridge University Press, 2003).

18.
Gill, B. A. et al. Cryptic species diversity reveals biogeographic support for the “mountain passes are higher in the tropics” hypothesis. Proc. R. Soc. B Biol. Sci. 283, 20160553. https://doi.org/10.1098/rspb.2016.0553 (2016).
Article  Google Scholar 

19.
Schmidt, S., Schmid-Egger, C., Morinière, J., Haszprunar, G. & Hebert, P. D. DNA barcoding largely supports 250 years of classical taxonomy: Identifications for Central European bees (Hymenoptera, Apoidea partim). Mol. Ecol. Resour. 15, 985–1000. https://doi.org/10.1111/1755-0998.12363 (2015).
CAS  Article  PubMed  Google Scholar 

20.
García-Robledo, C., Kuprewicz, E. K., Staines, C. L., Erwin, T. L. & Kress, W. J. Limited tolerance by insects to high temperatures across tropical elevational gradients and the implications of global warming for extinction. Proc. Natl. Acad. Sci. U.S.A. 113, 680–685. https://doi.org/10.1073/pnas.1507681113 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

21.
Ghalambor, C. K., Huey, R. B., Martin, P. R., Tewksbury, J. J. & Wang, G. Are mountain passes higher in the tropics? Janzen’s hypothesis revisited. Integr. Comp. Biol. 46, 5–17. https://doi.org/10.1093/icb/icj003 (2006).
Article  PubMed  Google Scholar 

22.
Janzen, D. H. Why mountain passes are higher in the tropics. Am. Nat. 101, 233–249. https://doi.org/10.1086/282487 (1967).
Article  Google Scholar 

23.
de Bruyn, M. et al. Borneo and Indochina are major evolutionary hotspots for Southeast Asian biodiversity. Syst. Biol. 63, 879–901, https://doi.org/10.1093/sysbio/syu047 (2014).

24.
Laurance, W. F., Sayer, J. & Cassman, K. G. Agricultural expansion and its impact on tropical nature. Trends Ecol. Evol. 29, 107–116. https://doi.org/10.1016/j.tree.2013.12.001 (2014).
Article  PubMed  Google Scholar 

25.
Carolyn, R. D., Baskoro, D. P. T. & Prasetyo, L. B. Analisis Degradasi Untuk Penyususnan Arahan Strategi Pengendaliannya Di Taman Nasional Gunung Halimun Salak Provinsi Jawa Barat. Globe 15, 39–47 (2013).
Google Scholar 

26.
Priyadi, H. et al.Five Hundred Plant Species in Gunung Halimun Salak National Park, West Java: A Checklist Including Sundanese Names, Distribution and Use (2010).

27.
Floren, A. in Manual on Field Recording Techniques and Protocols for All Taxa Biodiversity Inventories ABC Taxa Vol. Part 1 (eds J. Eymann, J. Degreff, & C. Häuser) 158–172 (2010).

28.
Schoonhoven, L. M., van Loon, J. J. A. & Dicke, M. Insect-Plant Biology. (Oxford University Press, 2010).

29.
deWaard, J. R., Ivanova, N. V., Hajibabaei, M. & Hebert, P. D. N. in Methods in Molecular Biology: Environmental Genetics (ed C. Martin) 275–293 (Humana Press, 2008).

30.
Ivanova, N. V., deWaard, J. R. & Hebert, P. D. N. An inexpensive, automation-friendly protocol for recovering high-quality DNA. Mol. Ecol. Notes6, 998–1002 (2006).

31.
Hebert, P. D. N., Cywinska, A., Ball, S. L. & deWaard, J. R. Biological identifications through DNA barcodes. Proc. R. Soc. B Biol. Sci. 313–321 (2003).

32.
Kimura, M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evolut., 111–120 (1980).

33.
Schmidt, S., Schmid-Egger, C., Morinière, J., Haszprunar, G. & Hebert, P. D. N. DNA barcoding largely supports 250 years of classical taxonomy: identifications for Central European bees (Hymenoptera, Apoideapartim). Mol. Ecol. Resour. 15, 985–1000 (2015).
CAS  Article  Google Scholar 

34.
Pentinsaari, M., Hebert, P. D. N. & Mutanen, M. Barcoding Beetles: A regional survey of 1872 species reveals high identification success and unusually deep interspecific divergences. PLoS ONE9, pdf_724, https://doi.org/10.1371/journal.pone.0108651 (2014).

35.
Paradis, E., Claude, J. & Strimmer, K. APE; analyses of phylogenetics and evolution. Bioinformatics, 289–290 (2014).

36.
Pagès, H., Aboyoun, P., Gentleman, R. & DebRoy, S. Biostrings: Efficient Manipulation of Biological Strings. R Package Version 2.48.0. (2018).

37.
R, C. T. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna). https://www.R-project.org/. (2017).

38.
Oksanen, J. et al. Vegan: Community Ecology Package. R Package Version 2.5-4. https://CRAN.R-project.org/package=vegan (2019).

39.
Hsieh, T. C., Ma, K. H. & Cho, A. iNEXT: iNterpolation and EXTrapolation for Species Diversity. R Package Version 2.0.19. https://chao.stat.nthu.edu.tw/blog/software-download/. (2019).

40.
Smith, M. A., Fernandez-Triana, J., Roughley, E. & Hebert, P. D. N. DNA barcode accumulation curves for understudied taxa and areas. Mol. Ecol. Resour. 9, 208–216. https://doi.org/10.1111/j.1755-0998.2009.02646.x (2009).
CAS  Article  PubMed  Google Scholar 

41.
Legendre, P. & De Cáceres, M. Beta diversity as the variance of community data: Dissimilarity coefficients and partitioning. Ecol. Lett. 16, 951–963. https://doi.org/10.1111/ele.12141 (2013).
Article  PubMed  Google Scholar 

42.
McArdle, B. H. & Anderson, M. J. Fitting multivariate models to community data: A comment on distance-based redundancy analysis. Ecology 82, 290–297. https://doi.org/10.1890/0012-9658(2001)082[0290:FMMTCD]2.0.CO;2 (2001).
Article  Google Scholar 

43.
Chao, A., Chazdon, R., Colwell, R. & Shen, T.-J. Abundance-based similarity indices and their estimation when there are unseen species in samples. Biometrics 62, 361–371. https://doi.org/10.1111/j.1541-0420.2005.00489.x (2006).
MathSciNet  Article  PubMed  MATH  Google Scholar 

44.
Paradis, E. Pegas: An R package for population genetics with an integrated-modular approach. Bioinformatics 419–420 (2010).

45.
Tajima, F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123, 585–595 (1989).
CAS  PubMed  PubMed Central  Google Scholar 

46.
Schliep, K. P. Phangorn: Phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).

47.
Miettinen, J., Shi, C. & Liew, S. C. Deforestation rates in insular Southeast Asia between 2000 and 2010. Glob. Change Biol. 17, 2261–2270. https://doi.org/10.1111/j.1365-2486.2011.02398.x (2011).
ADS  Article  Google Scholar 

48.
Turubanova, S., Potapov, P. V., Tyukavina, A. & Hansen, M. C. Ongoing primary forest loss in Brazil, Democratic Republic of the Congo, and Indonesia. Environ. Res. Lett. 13, 074028. https://doi.org/10.1088/1748-9326/aacd1c (2018).
ADS  Article  Google Scholar 

49.
Longino, J. T. & Branstetter, M. G. The truncated bell: An enigmatic but pervasive elevational diversity pattern in Middle American ants. Ecography 42, 272–283. https://doi.org/10.1111/ecog.03871 (2019).
Article  Google Scholar 

50.
Smith, M. A., Hallwachs, W. & Janzen, D. H. Diversity and phylogenetic community structure of ants along a Costa Rican elevational gradient. Ecography 37, 720–731. https://doi.org/10.1111/j.1600-0587.2013.00631.x (2014).
Article  Google Scholar 

51.
Floren, A., Biun, A. & Linsenmair, K. E. Arboreal ants as key predators in tropical lowland rainforest trees. Oecologia 131, 137–144. https://doi.org/10.1007/s00442-002-0874-z (2002).
ADS  Article  PubMed  Google Scholar 

52.
Supriya, K., Moreau, C. S., Sam, K. & Price, T. D. Analysis of tropical and temperate elevational gradients in arthropod abundance. Front. Biogeogr. 11, 1–11, https://doi.org/10.21425/F5FBG43104 (2019).

53.
Kress, W. J., García-Robledo, C., Uriarte, M. & Erickson, D. L. DNA barcodes for ecology, evolution, and conservation. Trends Ecol. Evol. 30, 25–35. https://doi.org/10.1016/j.tree.2014.10.008 (2015).
Article  PubMed  Google Scholar 

54.
Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406. https://doi.org/10.1126/science.1251817 (2014).
ADS  CAS  Article  Google Scholar 

55.
Guo, Q. et al. Global variation in elevational diversity patterns. Sci. Rep. 3, 3007. https://doi.org/10.1038/srep03007 (2013).
Article  PubMed  PubMed Central  Google Scholar 

56.
Bertuzzo, E. et al. Geomorphic controls on elevational gradients of species richness. Proc. Natl. Acad. Sci. 113, 1737–1742. https://doi.org/10.1073/pnas.1518922113 (2016).
ADS  CAS  Article  PubMed  Google Scholar 

57.
Floren, A. & Schmidl, J. Canopy Arthropod Research in Central Europe—Basic and Applied Studies from the High Frontier. (Bioform, 2008).

58.
Hodkinson, I. D. & Casson, D. A lesser predilection for bugs: Hemiptera (Insecta) diversity in tropical rain forests. Biol. J. Lin. Soc. 43, 101–109 (1991).
Article  Google Scholar 

59.
Guerrero-Jiménez, C. J. et al. Pattern of genetic differentiation of an incipient speciation process: The case of the high Andean killifish Orestias. PLoS ONE 12, e0170380. https://doi.org/10.1371/journal.pone.0170380 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

60.
Merckx, V. S. F. T. et al. Evolution of endemism on a young tropical mountain. Nature524, 347–350, https://doi.org/10.1038/nature14949. https://www.nature.com/nature/journal/v524/n7565/abs/nature14949.html#supplementary-information (2015).

61.
Schluter, D. & Pennell, M. W. Speciation gradients and the distribution of biodiversity. Nature546, 48, https://doi.org/10.1038/nature22897. https://www.nature.com/articles/nature22897#supplementary-information (2017). More

1.
Delgado-Baquerizo M, Reich PB, Trivedi C, Eldridge DJ, Abades S, Alfaro FD, et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat Ecol Evol. 2020;4:210–20.
PubMed  Article  PubMed Central  Google Scholar 
2.
Delgado-Baquerizo M, Maestre FT, Reich PB, Jeffries TC, Gaitan JJ, Encinar D, et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat Commun. 2016;7:10541.
CAS  PubMed  PubMed Central  Article  Google Scholar 

3.
Shah F, Wu W. Soil and crop management strategies to ensure higher crop productivity within sustainable environments. Sustainability. 2019;11:1485.
Article  Google Scholar 

4.
Leff JW, Jones SE, Prober SM, Barberán A, Borer ET, Firn JL, et al. Consistent responses of soil microbial communities to elevated nutrient inputs in grasslands across the globe. Proc Natl Acad Sci USA. 2015;112:10967.
CAS  PubMed  Article  PubMed Central  Google Scholar 

5.
Sun R, Zhang X-X, Guo X, Wang D, Chu H. Bacterial diversity in soils subjected to long-term chemical fertilization can be more stably maintained with the addition of livestock manure than wheat straw. Soil Biol Biochem. 2015;88:9–18.
CAS  Article  Google Scholar 

6.
Mougi A, Kondoh M. Diversity of interaction types and ecological community stability. Science. 2012;337:349–51.
CAS  PubMed  Article  PubMed Central  Google Scholar 

7.
Kumar A, Patel JS, Meena VS. Rhizospheric microbes for sustainable agriculture: an overview. In: Meena VS, editor. Role of rhizospheric microbes in soil: volume 1: stress management and agricultural sustainability. Singapore: Springer Singapore; 2018. p. 1–31.

8.
Yeates GW, Bongers T. Nematode diversity in agroecosystems. In: Paoletti MG, editor. Invertebrate biodiversity as bioindicators of sustainable landscapes. Amsterdam: Elsevier; 1999. p. 113–35.

9.
Chaffron S, Rehrauer H, Pernthaler J, von Mering C. A global network of coexisting microbes from environmental and whole-genome sequence data. Genome Res. 2010;20:947–59.
CAS  PubMed  PubMed Central  Article  Google Scholar 

10.
Banerjee S, Schlaeppi K, van der Heijden MGA. Keystone taxa as drivers of microbiome structure and functioning. Nat Rev Microbiol. 2018;16:567–76.
CAS  PubMed  Article  PubMed Central  Google Scholar 

11.
Cai G, Chen D, Ding H, Pacholski A, Fan X, Zhu Z. Nitrogen losses from fertilizers applied to maize, wheat and rice in the North China Plain. Nutr Cycl Agroecosys. 2002;63:187–95.
CAS  Article  Google Scholar 

12.
Fan K, Delgado-Baquerizo M, Guo X, Wang D, Wu Y, Zhu M, et al. Suppressed N fixation and diazotrophs after four decades of fertilization. Microbiome. 2019;7:143.
PubMed  PubMed Central  Article  Google Scholar 

13.
Biddle JF, Fitz-Gibbon S, Schuster SC, Brenchley JE, House CH. Metagenomic signatures of the Peru Margin subseafloor biosphere show a genetically distinct environment. Proc Natl Acad Sci USA. 2008;105:10583–8.
CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Bokulich NA, Mills DA. Improved selection of internal transcribed spacer-specific primers enables quantitative, ultra-high-throughput profiling of fungal communities. Appl Environ Microbiol. 2013;79:2519–26.
CAS  PubMed  PubMed Central  Article  Google Scholar 

15.
Lumini E, Orgiazzi A, Borriello R, Bonfante P, Bianciotto V. Disclosing arbuscular mycorrhizal fungal biodiversity in soil through a land-use gradient using a pyrosequencing approach. Environ Microbiol. 2010;12:2165–79.
CAS  PubMed  Google Scholar 

16.
Porazinska D, Giblin-Davis, Robin M, Faller LF, William K, Natsumi M, et al. Evaluating high-throughput sequencing as a method for metagenomic analysis of nematode diversity. Mol Ecol Resour. 2009;9:1439–50.
CAS  PubMed  Article  Google Scholar 

17.
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335.
CAS  PubMed  PubMed Central  Article  Google Scholar 

18.
Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.
CAS  PubMed  PubMed Central  Article  Google Scholar 

19.
McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A, et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 2011;6:610.
PubMed  PubMed Central  Article  CAS  Google Scholar 

20.
Kõljalg U, Larsson K-H, Abarenkov K, Nilsson RH, Alexander IJ, Eberhardt U, et al. UNITE: a database providing web-based methods for the molecular identification of ectomycorrhizal fungi. N. Phytol. 2005;166:1063–8.
Article  CAS  Google Scholar 

21.
Öpik M, Vanatoa A, Vanatoa E, Moora M, Davison J, Kalwij JM, et al. The online database MaarjAM reveals global and ecosystemic distribution patterns in arbuscular mycorrhizal fungi (Glomeromycota). N Phytol. 2010;188:223–41.
Article  CAS  Google Scholar 

22.
Quast C, Pruesse E, Gerken J, Peplies J, Yarza P, Yilmaz P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41:D590–D6.
PubMed  PubMed Central  Article  CAS  Google Scholar 

23.
Nguyen NH, Song Z, Bates ST, Branco S, Tedersoo L, Menke J, et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 2016;20:241–8.
Article  Google Scholar 

24.
Dean R, Kan JALV, Pretorius ZA, Hammond‐Kosack KE, Pietro AD, Spanu PD, et al. The Top 10 fungal pathogens in molecular plant pathology. Mol Plant Pathol. 2012;13:804.
PubMed Central  Article  Google Scholar 

25.
Wang F-H, Qiao M, Su J-Q, Chen Z, Zhou X, Zhu Y-G. High throughput profiling of antibiotic resistance genes in urban park soils with reclaimed water irrigation. Environ Sci Technol. 2014;48:9079–85.
CAS  PubMed  Article  PubMed Central  Google Scholar 

26.
Zheng B, Zhu Y, Sardans J, Peñuelas J, Su J. QMEC: a tool for high-throughput quantitative assessment of microbial functional potential in C, N, P, and S biogeochemical cycling. Sci China Life Sci. 2018;61:1451–62.
CAS  PubMed  Article  PubMed Central  Google Scholar 

27.
Pfaffl MW. A new mathematical model for relative quantification in real-time RT–PCR. Nucleic Acids Res. 2001;29:e45–e45.
CAS  PubMed  PubMed Central  Article  Google Scholar 

28.
Langfelder P, Horvath S. Fast R functions for robust correlations and hierarchical clustering. J Stat Softw. 2012;46:11.

29.
Benjamini Y, Krieger AM, Yekutieli D. Adaptive linear step-up procedures that control the false discovery rate. Biometrika. 2006;93:491–507.

30.
Hines J, van der Putten WH, De Deyn GB, Wagg C, Voigt W, Mulder C, et al. Chapter four-Towards an integration of biodiversity–ecosystem functioning and food web theory to evaluate relationships between multiple ecosystem services. Adv Ecol Res. 2015;53:161–99.

31.
Menezes AB, Prendergast-Miller MT, Richardson AE, Toscas P, Farrell M, Macdonald LM, et al. Network analysis reveals that bacteria and fungi form modules that correlate independently with soil parameters. Environ Microbiol. 2015;17:2677–89.
PubMed  Article  CAS  PubMed Central  Google Scholar 

32.
Heleno R, Devoto M, Pocock M. Connectance of species interaction networks and conservation value: Is it any good to be well connected? Ecol Indic. 2012;14:7–10.
Article  Google Scholar 

33.
Ramírez-Flandes S, González B, Ulloa O. Redox traits characterize the organization of global microbial communities. Proc Natl Acad Sci USA. 2019;116:3630.
PubMed  Article  CAS  PubMed Central  Google Scholar 

34.
Pérez Castro S, Cleland EE, Wagner R, Sawad RA, Lipson DA. Soil microbial responses to drought and exotic plants shift carbon metabolism. ISME J. 2019;13:1776–87.
PubMed  PubMed Central  Article  CAS  Google Scholar 

35.
Zhang C, Song Z, Zhuang D, Wang J, Xie S, Liu G. Urea fertilization decreases soil bacterial diversity, but improves microbial biomass, respiration, and N-cycling potential in a semiarid grassland. Biol Fert Soils. 2019;55:229–42.
CAS  Article  Google Scholar 

36.
Fraser TD, Lynch DH, Bent E, Entz MH, Dunfield KE. Soil bacterial phoD gene abundance and expression in response to applied phosphorus and long-term management. Soil Biol Biochem. 2015;88:137–47.
CAS  Article  Google Scholar 

37.
García-Palacios P, Shaw EA, Wall DH, Hättenschwiler S. Temporal dynamics of biotic and abiotic drivers of litter decomposition. Ecol Lett. 2016;19:554–63.
PubMed  Article  PubMed Central  Google Scholar 

38.
Hug LA, Castelle CJ, Wrighton KC, Thomas BC, Sharon I, Frischkorn KR, et al. Community genomic analyses constrain the distribution of metabolic traits across the Chloroflexi phylum and indicate roles in sediment carbon cycling. Microbiome. 2013;1:22.
PubMed  PubMed Central  Article  Google Scholar 

39.
Lu J, Yang F, Wang S, Ma H, Liang J, Chen Y. Co-existence of rhizobia and diverse non-rhizobial bacteria in the rhizosphere and nodules of dalbergia odorifera seedlings inoculated with Bradyrhizobium elkanii, Rhizobium multihospitium–like and Burkholderia pyrrocinia–like strains. Front Microbiol. 2017;8:2255.

40.
Haack FS, Poehlein A, Kröger C, Voigt CA, Piepenbring M, Bode HB, et al. Molecular keys to the janthinobacterium and duganella spp. interaction with the plant pathogen Fusarium graminearum. Front Microbiol. 2016;7:1668.

41.
Clay K, Leuchtmann A. Infection of woodland grasses by fungal endophytes. Mycologia. 1989;81:805–11.
Article  Google Scholar 

42.
Huang X, Liu L, Wen T, Zhang J, Wang F, Cai Z. Changes in the soil microbial community after reductive soil disinfestation and cucumber seedling cultivation. Appl Microbiol Biotechnol. 2016;100:5581–93.
CAS  PubMed  Article  PubMed Central  Google Scholar 

43.
Palleroni NJ. Pseudomonas. In: M.E. Trujillo, S. Dedysh, P. DeVos, B. Hedlund, P. Kämpfer, F.A. Rainey and W.B. Whitman, editors. Bergeyʼs Manual of Systematics of Archaea and Bacteria. John Wiley & Sons, Inc. in association with Bergey’s Manual Trust; 2015. p. 1–105.

44.
Wei Z, Yang T, Friman V-P, Xu Y, Shen Q, Jousset A. Trophic network architecture of root-associated bacterial communities determines pathogen invasion and plant health. Nat Commun. 2015;6:8413.
CAS  PubMed  PubMed Central  Article  Google Scholar 

45.
Mao Y, Li X, Smyth EM, Yannarell AC, Mackie RI. Enrichment of specific bacterial and eukaryotic microbes in the rhizosphere of switchgrass (Panicum virgatum L.) through root exudates. Environ Microbiol Rep. 2014;6:293–306.
CAS  PubMed  Article  PubMed Central  Google Scholar 

46.
Barka EA, Vatsa P, Sanchez L, Gaveau-Vaillant N, Jacquard C, Klenk H-P, et al. Taxonomy, physiology, and natural products of actinobacteria. Microbiol Mol Biol Rev. 2016;80:1.
PubMed  Article  PubMed Central  Google Scholar 

47.
Agnolucci M, Battini F, Cristani C, Giovannetti M. Diverse bacterial communities are recruited on spores of different arbuscular mycorrhizal fungal isolates. Biol Fert Soils. 2015;51:379–89.
CAS  Article  Google Scholar 

48.
Levy A, Merritt AJ, Mayo MJ, Chang BJ, Abbott LK, Inglis TJJ. Association between Burkholderia species and arbuscular mycorrhizal fungus spores in soil. Soil Biol Biochem. 2009;41:1757–9.
CAS  Article  Google Scholar 

49.
Li X, Rui J, Xiong J, Li J, He Z, Zhou J, et al. Functional potential of soil microbial communities in the maize rhizosphere. PLoS ONE. 2014;9:e112609.
PubMed  PubMed Central  Article  CAS  Google Scholar 

50.
Ragot SA, Kertesz MA, Mészáros É, Frossard E, Bünemann EK. Soil phoD and phoX alkaline phosphatase gene diversity responds to multiple environmental factors. FEMS Microbiol Ecol. 2016;93:fiw212.

51.
Gianfreda L. Enzymes of importance to rhizosphere processes. J Soil Sc Plant Nutr. 2015;15:283–306.
CAS  Google Scholar 

52.
Su J-Q, Ding L-J, Xue K, Yao H-Y, Quensen J, Bai S-J, et al. Long-term balanced fertilization increases the soil microbial functional diversity in a phosphorus-limited paddy soil. Mol Ecol. 2015;24:136–50.
CAS  PubMed  Article  PubMed Central  Google Scholar 

53.
Ratliff TJ, Fisk MC. Phosphatase activity is related to N availability but not P availability across hardwood forests in the northeastern United States. Soil Biol Biochem. 2016;94:61–9.
CAS  Article  Google Scholar  More

For the samples we studied, we found a very good linear correlation (R2 = 0.99, p  More

1.
Lambert, N., Strebel, P., Orenstein, W., Icenogle, J. & Poland, G. A. Rubella. Lancet 385, 2297–2307 (2015).
PubMed  PubMed Central  Article  Google Scholar 
2.
Zhou, Y., Ushijima, H. & Frey, T. K. Genomic analysis of diverse rubella virus genotypes. J. Gen. Virol. 88, 932–941 (2007).
CAS  PubMed  Article  Google Scholar 

3.
Chen, J.-P., Strauss, J. H., Strauss, E. G. & Frey, T. K. Characterization of the rubella virus nonstructural protease domain and its cleavage site. J. Virol. 70, 4707–4713 (1996).
CAS  PubMed  PubMed Central  Article  Google Scholar 

4.
Perelygina, L. et al. Infectious vaccine-derived rubella viruses emerge, persist, and evolve in cutaneous granulomas of children with primary immunodeficiencies. PLoS Pathog. 15, e1008080 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

5.
DuBois, R. M. et al. Functional and evolutionary insight from the crystal structure of rubella virus protein E1. Nature 493, 552–556 (2013).
CAS  PubMed  Article  ADS  Google Scholar 

6.
McCarthy, M., Lovett, A., Kerman, R. H., Overstreet, A. & Wolinsky, J. S. Immunodominant T-cell epitopes of rubella virus structural proteins defined by synthetic peptides. J. Virol. 67, 673–681 (1993).
CAS  PubMed  PubMed Central  Article  Google Scholar 

7.
Maton, W. G. Some account of a rash liable to be mistaken for scarlatina. Med. Trans. R. Coll. Physicians 5, 149–165 (1815).
Google Scholar 

8.
Cooper, L. Z. The history and medical consequences of rubella. Rev. Infect. Dis. 7, S2–S10 (1985).
PubMed  Article  Google Scholar 

9.
Gregg, N. M. Congenital cataract following German measles in the mother. Aust. N. Z. J. Ophthalmol. 3, 35–46 (1941).
Google Scholar 

10.
Parkman, P. D., Buescher, E. L. & Artenstein, M. S. Recovery of rubella virus from army recruits. Proc. Soc. Exp. Biol. Med. 111, 225–230 (1962).
CAS  PubMed  Article  Google Scholar 

11.
Weller, T. H. & Neva, F. A. Propagation in tissue culture of cytopathic agents from patients with rubella-like illness. Proc. Soc. Exp. Biol. Med. 111, 215–225 (1962).
Article  Google Scholar 

12.
Swan, C., Tostevin, A. L. & Black, G. H. Final observations on congenital defects in infants following infectious diseases during pregnancy, with special reference to rubella. Med. J. Aust. 2, 889–908 (1946).
CAS  PubMed  Article  Google Scholar 

13.
Edmunds, W. J., Gay, N. J., Kretzschmar, M., Pebody, R. G. & Wachmann, H. The pre-vaccination epidemiology of measles, mumps and rubella in Europe: implications for modelling studies. Epidemiol. Infect. 125, 635–650 (2000).
CAS  PubMed  PubMed Central  Article  Google Scholar 

14.
Gonzales, J. A. et al. Association of ocular inflammation and rubella virus persistence. JAMA Ophthalmol. 137, 435–438 (2019).
PubMed  Article  Google Scholar 

15.
Grant, G. B., Reef, S. E., Patel, M., Knapp, J. K. & Dabbagh, A. Progress in rubella and congenital rubella syndrome control and elimination — worldwide, 2000–2016. MMWR Morb. Mortal. Wkly. Rep. 66, 1256–1260 (2017).
PubMed  PubMed Central  Article  Google Scholar 

16.
Namuwulya, P. et al. Phylogenetic analysis of rubella viruses identified in Uganda, 2003–2012. J. Med. Virol. 86, 2107–2113 (2014).
PubMed  PubMed Central  Article  Google Scholar 

17.
Kretsinger, K., Strebel, P., Kezaala, R. & Goodson, J. L. Transitioning lessons learned and assets of the global polio eradication initiative to global and regional measles and rubella elimination. J. Infect. Dis. 216, S308–S315 (2017).
PubMed  PubMed Central  Article  Google Scholar 

18.
Wolfe, N. D., Dunavan, C. P. & Diamond, J. Origins of major human infectious diseases. Nature 447, 279–283 (2007).
CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

19.
Fahr, J. in Mammals of Africa. Vol. IV: Hedgehogs, Shrews and Bats (eds Happold, M. & Happold, D. C. D.) 380–383 (Bloomsbury, 2013).

20.
Jetz, W., McPherson, J. M. & Guralnick, R. P. Integrating biodiversity distribution knowledge: toward a global map of life. Trends Ecol. Evol. 27, 151–159 (2012).
Article  Google Scholar 

21.
O’Shea, T. J., Bogan, M. A. & Ellison, L. E. Monitoring Trends in Bat Populations of the United States and Territories: Status of the Science and Recommendations for the Future. Information and Technology Report USGS/BRD/ITR–2003–0003 (US Department of the Interior, US Geological Survey Washington, 2003).

22.
Landau, I. & Chabaud, A.-G. Description de Plasmodium cyclopsi n. sp. parasite du Microchirotère Hipposideros cyclops à Makokou (Gabon). Ann. Parasitol. Hum. Comp. 53, 247–253 (1978).
CAS  PubMed  Article  Google Scholar 

23.
Schaer, J. et al. High diversity of West African bat malaria parasites and a tight link with rodent Plasmodium taxa. Proc. Natl Acad. Sci. USA 110, 17415–17419 (2013).
CAS  PubMed  Article  ADS  Google Scholar 

24.
Michaux, J. R., Libois, R. & Filippucci, M.-G. So close and so different: comparative phylogeography of two small mammal species, the yellow-necked fieldmouse (Apodemus flavicollis) and the woodmouse (Apodemus sylvaticus) in the Western Palearctic region. Heredity 94, 52–63 (2005).
CAS  PubMed  Article  Google Scholar 

25.
Labuda, M. et al. Tick-borne encephalitis virus transmission between ticks cofeeding on specific immune natural rodent hosts. Virology 235, 138–143 (1997).
CAS  PubMed  Article  Google Scholar 

26.
Klempa, B. et al. Complex evolution and epidemiology of Dobrava–Belgrade hantavirus: definition of genotypes and their characteristics. Arch. Virol. 158, 521–529 (2013).
CAS  PubMed  Article  Google Scholar 

27.
Sibold, C. et al. Dobrava hantavirus causes hemorrhagic fever with renal syndrome in central Europe and is carried by two different Apodemus mice species. J. Med. Virol. 63, 158–167 (2001).
CAS  PubMed  Article  Google Scholar 

28.
Oktem, I. M. et al. Dobrava–Belgrade virus in Apodemus flavicollis and A. uralensis mice, Turkey. Emerg. Infect. Dis. 20, 121–125 (2014).
PubMed  PubMed Central  Article  Google Scholar 

29.
Doty, J. B. et al. Isolation and characterization of Akhmeta virus from wild-caught rodents (Apodemus spp.) in Georgia. J. Virol. 93, e00966-19 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

30.
Prpić, J. et al. First evidence of hepatitis E virus infection in a small mammal (yellow-necked mouse) from Croatia. PLoS ONE 14, e0225583 (2019).
PubMed  PubMed Central  Article  CAS  Google Scholar 

31.
Hofmann, J., Renz, M., Meyer, S., von Haeseler, A. & Liebert, U. G. Phylogenetic analysis of rubella virus including new genotype I isolates. Virus Res. 96, 123–128 (2003).
CAS  PubMed  Article  Google Scholar 

32.
Abernathy, E. et al. Analysis of whole genome sequences of 16 strains of rubella virus from the United States, 1961–2009. Virol. J. 10, 32 (2013).
PubMed  PubMed Central  Article  Google Scholar 

33.
Kelley, L. A., Mezulis, S., Yates, C. M., Wass, M. N. & Sternberg, M. J. E. The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protocols 10, 845–858 (2015).
CAS  PubMed  Article  Google Scholar 

34.
Wolinsky, J. S. et al. An antibody- and synthetic peptide-defined rubella virus E1 glycoprotein neutralization domain. J. Virol. 67, 961–968 (1993).
CAS  PubMed  PubMed Central  Article  Google Scholar 

35.
Guy, C., Thiagavel, J., Mideo, N. & Ratcliffe, J. M. Phylogeny matters: revisiting ‘a comparison of bats and rodents as reservoirs of zoonotic viruses’. R. Soc. Open Sci. 6, 181182 (2019).
PubMed  PubMed Central  Article  ADS  Google Scholar 

36.
Luis, A. D. et al. A comparison of bats and rodents as reservoirs of zoonotic viruses: are bats special? Proc. R. Soc. Lond. B 280, 20122753 (2013).
Google Scholar 

37.
Olival, K. J. et al. Host and viral traits predict zoonotic spillover from mammals. Nature 546, 646–650 (2017).
CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

38.
Frey, T. K. Neurological aspects of rubella virus infection. Intervirology 40, 167–175 (1997).
CAS  PubMed  Article  Google Scholar 

39.
Bharadwaj, S. D. et al. Acute encephalitis with atypical presentation of rubella in family cluster, India. Emerg. Infect. Dis. 24, 1923–1925 (2018).
PubMed  PubMed Central  Article  Google Scholar 

40.
Grant, G. B. et al. Accelerating measles and rubella elimination through research and innovation — findings from the Measles & Rubella Initiative research prioritization process, 2016. Vaccine 37, 5754–5761 (2019).
PubMed  PubMed Central  Article  Google Scholar 

41.
Struhsaker, T. T. Ecology of an African Rain Forest: Logging in Kibale and the Conflict between Conservation and Exploitation (Univ. Press Florida, 1997).

42.
Plumptre, A. J. et al. The biodiversity of the Albertine Rift. Biol. Conserv. 134, 178–194 (2007).
Article  Google Scholar 

43.
Ulrich, R. G. et al. Network “rodent-borne pathogens” in Germany: longitudinal studies on the geographical distribution and prevalence of hantavirus infections. Parasitol. Res. 103, S121–S129 (2008).
PubMed  Article  Google Scholar 

44.
Schlegel, M. et al. Molecular identification of small mammal species using novel cytochrome b gene-derived degenerated primers. Biochem. Genet. 50, 440–447 (2012).
CAS  PubMed  Article  Google Scholar 

45.
Foley, N. M. et al. How and why overcome the impediments to resolution: lessons from rhinolophid and hipposiderid bats. Mol. Biol. Evol. 32, 313–333 (2015).
CAS  PubMed  Article  Google Scholar 

46.
Zhao, G. et al. VirusSeeker, a computational pipeline for virus discovery and virome composition analysis. Virology 503, 21–30 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

47.
Bushnell, B. BBMap: a fast, accurate, splice-aware aligner. Version 37.78 https://sourceforge.net/projects/bbmap/ (2014).

48.
Andrews, S. FastQC. A quality control tool for high throughput sequence data. Version 0.11.5 https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).

49.
Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 27, 824–834 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

50.
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).
CAS  PubMed  Article  Google Scholar 

51.
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).
CAS  Article  Google Scholar 

52.
Huson, D. H. et al. MEGAN community edition — interactive exploration and analysis of large-scale microbiome sequencing data. PLOS Comput. Biol. 12, e1004957 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

53.
Wylezich, C., Papa, A., Beer, M. & Höper, D. A versatile sample processing workflow for metagenomic pathogen detection. Sci. Rep. 8, 13108 (2018).
PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

54.
Scheuch, M., Höper, D. & Beer, M. RIEMS: a software pipeline for sensitive and comprehensive taxonomic classification of reads from metagenomics datasets. BMC Bioinformatics 16, 69 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

55.
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
CAS  PubMed  PubMed Central  Article  Google Scholar 

56.
Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).
MathSciNet  CAS  PubMed  PubMed Central  Article  Google Scholar 

57.
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

58.
Hobman, T. C. & Gillam, S. In vitro and in vivo expression of rubella virus glycoprotein E2: the signal peptide is contained in the C-terminal region of capsid protein. Virology 173, 241–250 (1989).
CAS  PubMed  Article  Google Scholar 

59.
Gasteiger, E. et al. in The Proteomics Protocols Handbook (ed Walker, J. M.) 571–607 (Humana Press, 2005).

60.
Forth, L. F. & Höper, D. Highly efficient library preparation for ion torrent sequencing using Y-adapters. Biotechniques 67, 229–237 (2019).
CAS  PubMed  Article  Google Scholar 

61.
Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).
CAS  PubMed  Article  Google Scholar 

62.
Hoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35, 518–522 (2018).
CAS  PubMed  Article  Google Scholar 

63.
Mitchell, A. L. et al. InterPro in 2019: improving coverage, classification and access to protein sequence annotations. Nucleic Acids Res. 47, D351–D360 (2019).
CAS  PubMed  Article  Google Scholar 

64.
Waterhouse, A. et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 46, W296–W303 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

65.
Rose, A. S. & Hildebrand, P. W. NGL Viewer: a web application for molecular visualization. Nucleic Acids Res. 43, W576–W579 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

66.
Benkert, P., Biasini, M. & Schwede, T. Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics 27, 343–350 (2011).
CAS  PubMed  Article  Google Scholar 

67.
Korber, B. in Computational Analysis of HIV Molecular Sequences Ch. 4 (eds Rodrigo, A. G. & Learn, G. H.) 55–72 (Kluwer Academic Publishers, 2000).

68.
Leskovec, J. SNAP 2.1. http://snap.stanford.edu/snap-2.1/download.html (2013). More

1.
Prather, M. J. et al. Measuring and modeling the lifetime of nitrous oxide including its variability. J. Geophys. Res. D 120, 5693–5705 (2015).
ADS  CAS  Google Scholar 
2.
Ciais, P. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 6 (IPCC, Cambridge Univ. Press, 2013).

3.
Gidden, M. J. et al. Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century. Geosci. Model Dev. 12, 1443–1475 (2019).
ADS  CAS  Google Scholar 

4.
Davidson, E. A. Representative concentration pathways and mitigation scenarios for nitrous oxide. Environ. Res. Lett. 7, 024005 (2012).
ADS  Google Scholar 

5.
Hall, B., Dutton, G. & Elkins, J. The NOAA nitrous oxide standard scale for atmospheric observations. J. Geophys. Res. D 112, D09305 (2007).
ADS  Google Scholar 

6.
Prinn, R. G. et al. History of chemically and radiatively important atmospheric gases from the Advanced Global Atmospheric Gases Experiment (AGAGE). Earth Syst.Sci. Data10, 985–1018 (2018).
Google Scholar 

7.
Butterbach-Bahl, K., Baggs, E. M., Dannenmann, M., Kiese, R. & Zechmeister-Boltenstern, S. Nitrous oxide emissions from soils: how well do we understand the processes and their controls? Phil.Trans. R. Soc. Lond. B 368, 20130122 (2013).
Google Scholar 

8.
Tian, H. et al. The terrestrial biosphere as a net source of greenhouse gases to the atmosphere. Nature 531, 225–228 (2016).
ADS  CAS  Google Scholar 

9.
United Nations Environment Programme & Suntharalingam, P. Drawing Down N 2O to Protect Climate and the Ozone Layer (United Nations Environment Programme, 2013).

10.
Park, S. et al. Trends and seasonal cycles in the isotopic composition of nitrous oxide since 1940. Nat. Geosci. 5, 261–265 (2012).
ADS  CAS  Google Scholar 

11.
Davidson, E. A. & Kanter, D. Inventories and scenarios of nitrous oxide emissions. Environ. Res. Lett. 9, 105012 (2014).
ADS  Google Scholar 

12.
Reay, D. S. et al. Global agriculture and nitrous oxide emissions. Nat. Clim. Change 2, 410–416 (2012).
ADS  CAS  Google Scholar 

13.
Syakila, A. & Kroeze, C. The global nitrous oxide budget revisited. Greenhouse Gas Meas. Manage. 1, 17–26 (2011).
ADS  CAS  Google Scholar 

14.
IPCC 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories (eds Eggleston, H. S. et al.) (IGES, 2006).

15.
Dangal, S. R. et al. Global nitrous oxide emissions from pasturelands and rangelands: magnitude, spatio-temporal patterns and attribution. Glob. Biogeochem. Cycles 33, 200–222 (2019).
ADS  CAS  Google Scholar 

16.
Tian, H. et al. Global soil nitrous oxide emissions since the preindustrial era estimated by an ensemble of terrestrial biosphere models: magnitude, attribution, and uncertainty. Glob. Change Biol. 25, 640–659 (2019).
ADS  Google Scholar 

17.
Wang, Q. et al. Data-driven estimates of global nitrous oxide emissions from croplands. Natl Sci. Rev. 7, 441–452 (2020).
CAS  Google Scholar 

18.
Thompson, R. L. et al. Acceleration of global N2O emissions seen from two decades of atmospheric inversion. Nat. Clim. Change 9, 993–998 (2019).
ADS  CAS  Google Scholar 

19.
Zaehle, S., Ciais, P., Friend, A. D. & Prieur, V. Carbon benefits of anthropogenic reactive nitrogen offset by nitrous oxide emissions. Nat. Geosci. 4, 601–605 (2011).
ADS  CAS  Google Scholar 

20.
Davidson, E. A. et al. Recuperation of nitrogen cycling in Amazonian forests following agricultural abandonment. Nature 447, 995–998 (2007).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

21.
Verchot, L. V. et al. Land use change and biogeochemical controls of nitrogen oxide emissions from soils in eastern Amazonia. Glob. Biogeochem. Cycles 13, 31–46 (1999).
ADS  CAS  Google Scholar 

22.
Shcherbak, I., Millar, N. & Robertson, G. P. Global metaanalysis of the nonlinear response of soil nitrous oxide (N2O) emissions to fertilizer nitrogen. Proc. Natl Acad. Sci. USA 111, 9199–9204 (2014).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

23.
Van Meter, K. J., Basu, N. B., Veenstra, J. J. & Burras, C. L. The nitrogen legacy: emerging evidence of nitrogen accumulation in anthropogenic landscapes. Environ. Res. Lett. 11, 035014 (2016).
ADS  Google Scholar 

24.
Firestone, M. K. & Davidson, E. A. Microbiological basis of NO and N2O production and consumption in soil. In Exchange of Trace Gases Between Terrestrial Ecosystems and the Atmosphere Vol. 47 (Andreae, M. O. & Schimel, D. S.) 7–21 (Wiley, 1989).

25.
Griffis, T. J. et al. Nitrous oxide emissions are enhanced in a warmer and wetter world. Proc. Natl Acad. Sci. USA 114, 12081–12085 (2017).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

26.
Pärn, J. et al. Nitrogen-rich organic soils under warm well-drained conditions are global nitrous oxide emission hotspots. Nat. Commun. 9, 1135 (2018).
ADS  PubMed  PubMed Central  Google Scholar 

27.
Smith, K. The potential for feedback effects induced by global warming on emissions of nitrous oxide by soils. Glob. Change Biol. 3, 327–338 (1997).
ADS  CAS  Google Scholar 

28.
The Food and Agriculture Organization of the United Nations Statistics (FAO, accessed 7 July 2019); https://www.fao.org/faostat/en/#data/.

29.
Xu, R. et al. Increased nitrogen enrichment and shifted patterns in the world’s grassland: 1860–2016. Earth Syst. Sci. Data 11, 175–187 (2019).
ADS  Google Scholar 

30.
Beusen, A. H., Bouwman, A. F., Van Beek, L. P., Mogollón, J. M. & Middelburg, J. J. Global riverine N and P transport to ocean increased during the 20th century despite increased retention along the aquatic continuum. Biogeosciences 13, 2441–2451 (2016).
ADS  CAS  Google Scholar 

31.
MacLeod, M., Hasan, M. R., Robb, D. H. F. & Mamun-Ur-Rashid, M. Quantifying and Mitigating Greenhouse Gas Emissions from Global Aquaculture FAO Fisheries and Aquaculture technical paper no. 626 (FAO, 2019).

32.
Buitenhuis, E. T., Suntharalingam, P. & Le Quéré, C. Constraints on global oceanic emissions of N2O from observations and models. Biogeosciences 15, 2161–2175 (2018).
ADS  CAS  Google Scholar 

33.
Manizza, M., Keeling, R. F. & Nevison, C. D. On the processes controlling the seasonal cycles of the air–sea fluxes of O2 and N2O: a modelling study. Tellus B 64, 18429 (2012).
ADS  Google Scholar 

34.
Martinez-Rey, J., Bopp, L., Gehlen, M., Tagliabue, A. & Gruber, N. Projections of oceanic N2O emissions in the 21st century using the IPSL Earth system model. Biogeosciences 12, 4133–4148 (2015).
ADS  Google Scholar 

35.
Maavara, T. et al. Nitrous oxide emissions from inland waters: are IPCC estimates too high? Glob. Change Biol. 25, 473–488 (2019).
ADS  Google Scholar 

36.
Yao, Y. et al. Increased global nitrous oxide emissions from streams and rivers in the Anthropocene. Nat. Clim. Change 10, 138–142 (2020).
ADS  CAS  Google Scholar 

37.
Gerber, P. J. et al. Tackling Climate Change Through Livestock: A Global Assessment of Emissions and Mitigation Opportunities. (FAO, 2013).

38.
Herrero, M. et al. Biomass use, production, feed efficiencies, and greenhouse gas emissions from global livestock systems. Proc. Natl Acad. Sci. USA 110, 20888–20893 (2013).
ADS  CAS  Google Scholar 

39.
Yuan, J. et al. Rapid growth in greenhouse gas emissions from the adoption of industrial-scale aquaculture. Nat. Clim. Change 9, 318–322 (2019).
ADS  Google Scholar 

40.
Froehlich, H. E., Runge, C. A., Gentry, R. R., Gaines, S. D. & Halpern, B. S. Comparative terrestrial feed and land use of an aquaculture-dominant world. Proc. Natl Acad. Sci. USA 115, 5295–5300 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

41.
Winiwarter, W., Höglund-Isaksson, L., Klimont, Z., Schöpp, W. & Amann, M. Technical opportunities to reduce global anthropogenic emissions of nitrous oxide. Environ. Res. Lett. 13, 014011 (2018).
ADS  Google Scholar 

42.
Zhang, X. et al. Managing nitrogen for sustainable development. Nature 528, 51–59 (2015).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

43.
Mueller, N. D. et al. Declining spatial efficiency of global cropland nitrogen allocation. Glob. Biogeochem. Cycles 31, 245–257 (2017).
ADS  CAS  Google Scholar 

44.
European Environment Agency. Annual European Union Greenhouse Gas Inventory 1990–2017 and Inventory Report 2019 EEA/PUBL/2019/051 (EEA, 2019).

45.
Cui, Z. et al. Pursuing sustainable productivity with millions of smallholder farmers. Nature 555, 363–366 (2018).
ADS  CAS  PubMed  Google Scholar 

46.
Kanter, D. et al. A post-Kyoto partner: considering the stratospheric ozone regime as a tool to manage nitrous oxide. Proc. Natl Acad. Sci. USA 110, 4451–4457 (2013).
ADS  CAS  Google Scholar 

47.
Schneider, L., Lazarus, M. & Kollmuss, A. Industrial N 2O Projects Under the CDM: Adipic Acid – A Case of Carbon Leakage SEI working paper WP-US-1006 (Stockholm Environment Institute, 2010).

48.
O’Neill, B. C. et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).
ADS  Google Scholar 

49.
Gütschow, J. et al. The PRIMAP-hist national historical emissions time series. Earth Syst.Sci. Data 8, 571–603 (2016).
Google Scholar 

50.
Nevison, C. D., Lueker, T. J. & Weiss, R. F. Quantifying the nitrous oxide source from coastal upwelling. Glob. Biogeochem. Cycles 18, GB1018 (2004).
ADS  Google Scholar 

51.
Lauerwald, R. et al. Natural lakes are a minor global source of N2O to the atmosphere. Glob. Biogeochem. Cycles 33, 1564–1581 (2019).
ADS  CAS  Google Scholar 

52.
Kroeze, C., Mosier, A. & Bouwman, L. Closing the global N2O budget: a retrospective analysis 1500–1994. Glob. Biogeochem. Cycles 13, 1–8 (1999).
ADS  CAS  Google Scholar 

53.
Schumann, U. & Huntrieser, H. The global lightning-induced nitrogen oxides source. Atmos. Chem. Phys. 7, 3823–3907 (2007).
ADS  CAS  Google Scholar 

54.
De Klein, C. et al. N2O emissions from managed soils, and CO2 emissions from lime and urea application. In 2006 IPCC Guidelines for National Greenhouse Gas Inventories Vol. 4, Ch. 11 (IPCC, 2006).

55.
Dentener, F. J. & Crutzen, P. J. A three-dimensional model of the global ammonia cycle. J. Atmos. Chem. 19, 331–369 (1994).
CAS  Google Scholar 

56.
Röckmann, T., Kaiser, J., Crowley, J. N., Brenninkmeijer, C. A. & Crutzen, P. J. The origin of the anomalous or “mass-independent” oxygen isotope fractionation in tropospheric N2O. Geophys. Res. Lett. 28, 503–506 (2001).
ADS  Google Scholar 

57.
Kaiser, J. & Röckmann, T. Absence of isotope exchange in the reaction of N2O+O(1D) and the global Δ17O budget of nitrous oxide. Geophys. Res. Lett. 32, L15808 (2005).
ADS  Google Scholar 

58.
Schlesinger, W. H. An estimate of the global sink for nitrous oxide in soils. Glob. Change Biol. 19, 2929–2931 (2013).
ADS  Google Scholar 

59.
Syakila, A., Kroeze, C. & Slomp, C. P. Neglecting sinks for N2O at the Earth’s surface: does it matter? J. Integr. Environ. Sci. 7, 79–87 (2010).
Google Scholar 

60.
Bouwman, A. F. et al. Hindcasts and future projections of global inland and coastal nitrogen and phosphorus loads due to finfish aquaculture. Rev. Fish. Sci. 21, 112–156 (2013).
CAS  Google Scholar 

61.
Bouwman, A. F. et al. Global hindcasts and future projections of coastal nitrogen and phosphorus loads due to shellfish and seaweed aquaculture. Rev. Fish. Sci. 19, 331–357 (2011).
ADS  CAS  Google Scholar 

62.
Hu, Z., Lee, J. W., Chandran, K., Kim, S. & Khanal, S. K. Nitrous oxide (N2O) emission from aquaculture: a review. Environ. Sci. Technol. 46, 6470–6480 (2012).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

63.
Williams, J. & Crutzen, P. J. Nitrous oxide from aquaculture. Nat. Geosci. 3, 143 (2010).
ADS  CAS  Google Scholar 

64.
Andela, N. et al. A human-driven decline in global burned area. Science 356, 1356–1362 (2017).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

65.
Yang, J. et al. Spatial and temporal patterns of global burned area in response to anthropogenic and environmental factors: reconstructing global fire history for the 20th and early 21st centuries. J. Geophys. Res. Biogeosci. 119, 249–263 (2014).
ADS  Google Scholar 

66.
Dennis, R. A Review of Fire Projects in Indonesia (1982–1998) (Center for International Forestry Research, 1999).

67.
Suntharalingam, P. et al. Quantifying the impact of anthropogenic nitrogen deposition on oceanic nitrous oxide. Geophys. Res. Lett. 39, L07605 (2012).
ADS  Google Scholar 

68.
Murray, R. H., Erler, D. V. & Eyre, B. D. Nitrous oxide fluxes in estuarine environments: response to global change. Glob. Change Biol. 21, 3219–3245 (2015).
ADS  Google Scholar 

69.
Erler, D. V. et al. Applying cavity ring-down spectroscopy for the measurement of dissolved nitrous oxide concentrations and bulk nitrogen isotopic composition in aquatic systems: correcting for interferences and field application. Limnol. Oceanogr. Methods 13, 391–401 (2015).
CAS  Google Scholar 

70.
Murray, R., Erler, D., Rosentreter, J. & Eyre, B. Seasonal and spatial N2O concentrations and emissions in three tropical estuaries. Mar. Chem. 221, 103779 (2020).
CAS  Google Scholar 

71.
Vernberg, F. J. & Vernberg, W. B. The Coastal Zone: Past, Present, and Future (Univ. South Carolina Press, 2001).

72.
Houghton, R. et al. Changes in the carbon content of terrestrial biota and soils between 1860 and 1980: a net release of CO2 to the atmosphere. Ecol. Monogr. 53, 235–262 (1983).
CAS  Google Scholar 

73.
Davidson, E. A. The contribution of manure and fertilizer nitrogen to atmospheric nitrous oxide since 1860. Nat. Geosci. 2, 659–662 (2009).
ADS  CAS  Google Scholar 

74.
van Lent, J., Hergoualc’h, K. & Verchot, L. V. Soil N2O and NO emissions from land use and land-use change in the tropics and subtropics: a meta-analysis. Biogeosciences 12, 7299–7313 (2015).
ADS  Google Scholar 

75.
Keller, M. & Reiners, W. A. Soil–atmosphere exchange of nitrous oxide, nitric oxide, and methane under secondary succession of pasture to forest in the Atlantic lowlands of Costa Rica. Glob. Biogeochem. Cycles 8, 399–409 (1994).
ADS  CAS  Google Scholar 

76.
Melillo, J. M. et al. Nitrous oxide emissions from forests and pastures of various ages in the Brazilian Amazon. J. Geophys. Res. D 106, 34179–34188 (2001).
ADS  CAS  Google Scholar 

77.
McGuire, A. et al. Carbon balance of the terrestrial biosphere in the twentieth century: Analyses of CO2, climate and land use effects with four process-based ecosystem models. Glob. Biogeochem. Cycles 15, 183–206 (2001).
ADS  CAS  Google Scholar 

78.
Dlugokencky, E., Steele, L., Lang, P. & Masarie, K. The growth rate and distribution of atmospheric methane. J. Geophys. Res. D 99, 17021–17043 (1994).
ADS  CAS  Google Scholar 

79.
IPCC 2013. Annex II: Climate System Scenario Tables (eds Prather, M. et al.). In Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

80.
Prather, M. J., Holmes, C. D. & Hsu, J. Reactive greenhouse gas scenarios: systematic exploration of uncertainties and the role of atmospheric chemistry. Geophys. Res. Lett. 39, L09803 (2012).
ADS  Google Scholar 

81.
Prather, M. J. & Hsu, J. Coupling of nitrous oxide and methane by global atmospheric chemistry. Science 330, 952–954 (2010).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

82.
Mosier, A. & Kroeze, C. Potential impact on the global atmospheric N2O budget of the increased nitrogen input required to meet future global food demands. Chemosphere 2, 465–473 (2000).
CAS  Google Scholar 

83.
Mosier, A. et al. Closing the global N2O budget: nitrous oxide emissions through the agricultural nitrogen cycle. Nutr. Cycl. Agroecosyst. 52, 225–248 (1998).
CAS  Google Scholar 

84.
Houghton, J. T. et al (eds) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories Vols 1–3 (IPCC, 1997).

85.
Nevison, C. Indirect N2O emissions from agriculture. In Background Papers:IPCC Expert Meetings on Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories 381–397 (IPCC, 2000).

86.
2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories Vol. 4 (IPCC, 2019).

87.
Crutzen, P. J., Mosier, A. R., Smith, K. A. & Winiwarter, W. N2O release from agro-biofuel production negates global warming reduction by replacing fossil fuels. Atmos. Chem. Phys. 8, 389–395 (2008).
ADS  CAS  Google Scholar 

88.
Smith, K. A., Mosier, A. R., Crutzen, P. J. & Winiwarter, W. The role of N2O derived from crop-based biofuels, and from agriculture in general, in Earth’s climate. Phil.Trans. R. Soc. Lond. B 367, 1169–1174 (2012).
CAS  Google Scholar 

89.
Thompson, R. L. et al. Nitrous oxide emissions 1999 to 2009 from a global atmospheric inversion. Atmos. Chem. Phys. 14, 1801–1817 (2014).
ADS  Google Scholar 

90.
Wells, K. C. et al. Simulation of atmospheric N2O with GEOS-Chem and its adjoint: evaluation of observational constraints. Geosci. Model Dev. 8, 3179–3198 (2015).
ADS  CAS  Google Scholar 

91.
Wilson, C., Chipperfield, M., Gloor, M. & Chevallier, F. Development of a variational flux inversion system (INVICAT v1. 0) using the TOMCAT chemical transport model. Geosci. Model Dev. 7, 2485–2500 (2014).
ADS  Google Scholar 

92.
Patra, P. K. et al. Improved chemical tracer simulation by MIROC4.0-based atmospheric chemistry-transport model (MIROC4-ACTM). SOLA 14, 91–96 (2018).
ADS  Google Scholar 

93.
Tian, H. Q. et al. The global N2O Model Intercomparison Project. Bull. Am. Meteorol. Soc. 99, 1231–1251 (2018).
ADS  Google Scholar 

94.
Suntharalingam, P. et al. Anthropogenic nitrogen inputs and impacts on oceanic N2O fluxes in the northern Indian Ocean: the need for an integrated observation and modelling approach. Deep Sea Res. Part II 166, 104–113 (2019).
CAS  Google Scholar 

95.
Battaglia, G. & Joos, F. Marine N2O emissions from nitrification and denitrification constrained by modern observations and projected in multimillennial global warming simulations. Glob. Biogeochem. Cycles 32, 92–121 (2018).
ADS  CAS  Google Scholar 

96.
Galloway, J. et al. The impact of animal production systems on the nitrogen cycle. In Livestock in a Changing Landscape Vol. 1 (eds Steinfeld, H. et al.) 83–95 (Island Press, 2010).

97.
Elberling, B., Christiansen, H. H. & Hansen, B. U. High nitrous oxide production from thawing permafrost. Nat. Geosci. 3, 332–335 (2010).
ADS  CAS  Google Scholar 

98.
Wagner-Riddle, C. et al. Globally important nitrous oxide emissions from croplands induced by freeze–thaw cycles. Nat. Geosci. 10, 279–283 (2017).
ADS  CAS  Google Scholar 

99.
Suntharalingam, P. et al. Estimates of oceanic nitrous-oxide emissions from global biogeochemistry models. In American Geophysical Union, Fall Meeting 2018 B21K-2481B (2018).

100.
Janssens-Maenhout, G. et al. EDGAR v4.3.2 Global Atlas of the three major greenhouse gas emissions for the period 1970–2012. Earth Syst. Sci. Data 11, 959–1002 (2019).
ADS  Google Scholar 

101.
Tubiello, F. et al. Estimating Greenhouse Gas Emissions in Agriculture Ch. 5, 35–115 (FAO, 2015).

102.
van der Werf, G. R. et al. Global fire emissions estimates during 1997–2016. Earth Syst.Sci. Data 9, 697–720 (2017).
Google Scholar 

103.
Dentener, F. Global maps of atmospheric nitrogen deposition, 1860, 1993, and 2050. https://doi.org/10.3334/ORNLDAAC/830 (Oak Ridge National Laboratory, 2006).

104.
Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42, 153–168 (2017).
Google Scholar  More