Philippa Kaur

1.
Gallego-Sala, A. V. et al. Latitudinal limits to the predicted increase of the peatland carbon sink with warming. Nat. Clim. Change 8, 907–913 (2018).
CAS  Article  Google Scholar 
2.
IPCC Climate Change 2007: The Physical Science Basis (eds Solomon, S. et al.) (Cambridge Univ. Press, 2007).

3.
Xu, J., Morris, P. J., Liu, J. & Holden, J. PEATMAP: refining estimates of global peatland distribution based on a meta-analysis. Catena 160, 134–140 (2018).
Article  Google Scholar 

4.
Yu, Z., Loisel, J., Brosseau, D. P., Beilman, D. W. & Hunt, S. J. Global peatland dynamics since the Last Glacial Maximum. Geophys. Res. Lett. 37, L13402 (2010).
Google Scholar 

5.
Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).
CAS  Article  Google Scholar 

6.
Frolking, S., Roulet, N. & Fuglestvedt, S. How northern peatlands influence the Earth’s radiative budget: sustained methane emission versus sustained carbon sequestration. J. Geophys. Res. 111, G01008 (2006).
Google Scholar 

7.
IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

8.
Frolking, S. et al. Peatlands in the Earth’s 21st century climate system. Environ. Rev. 19, 371–396 (2011).
CAS  Article  Google Scholar 

9.
Kleinen, T., Brovkin, V. & Schuldt, R. J. A dynamic model of wetland extent and peat accumulation: results for the Holocene. Biogeosciences 9, 235–248 (2012).
Article  Google Scholar 

10.
Müller, J. & Joos, F. Peatland area and carbon over the past 21 000 years – a global process based model investigation. Biogeosci. Discuss. Preprint at https://doi.org/10.5194/bg-2020-110 (2020).

11.
Todd-Brown, K. E. et al. Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations. Biogeosciences 10, 1717–1736 (2013).
Article  Google Scholar 

12.
Hugelius, G. et al. Large stocks of peatland carbon and nitrogen are vulnerable to permafrost thaw. Proc. Natl Acad. Sci. USA 117, 20438–20446 (2020).
CAS  Article  Google Scholar 

13.
Turetsky, M. R. et al. Global vulnerability of peatlands to fire and carbon loss. Nat. Geosci. 8, 11–14 (2015).
CAS  Article  Google Scholar 

14.
Miettinen, J., Shi, C. & Liew, S. C. Land cover distribution in the peatlands of Peninsular Malaysia, Sumatra and Borneo in 2015 with changes since 1990. Glob. Ecol. Conserv. 6, 67–78 (2016).
Article  Google Scholar 

15.
Rommain, R. et al. A radiative forcing analysis of tropical peatlands before and after their conversion to agricultural plantations. Glob. Change Biol. 24, 5518–5533 (2018).
Article  Google Scholar 

16.
Page, S. E. & Baird, A. J. Peatlands and global change: response and resilience. Annu. Rev. Environ. Resour. 41, 35–57 (2016).
Article  Google Scholar 

17.
Warren, M., Frolking, S., Zhaohua, D. & Kurnianto, S. Impacts of land use, restoration, and climate change on tropical peat carbon stocks in the twenty-first century: implications for climate mitigation. Mitig. Adapt. Strateg. Glob. Chang. 22, 1041–1061 (2017).
Article  Google Scholar 

18.
Parish F. et al (eds) Assessment on Peatlands, Biodiversity and Climate Change: Main Report (Global Environment Centre and Wetlands International, 2008).

19.
Leifeld, J. & Menichetti, L. The underappreciated potential of peatlands in global climate change mitigation strategies. Nat. Commun. 9, 1071 (2018).
CAS  Article  Google Scholar 

20.
Nugent, K. A. et al. Prompt active restoration of peatlands substantially reduces climate impact. Environ. Res. Lett. 14, 124030 (2019).
CAS  Article  Google Scholar 

21.
Günther, A. A. et al. Prompt rewetting of drained peatlands reduces climate warming despite methane emissions. Nat. Commun. 11, 1644 (2020).
Article  CAS  Google Scholar 

22.
Bossio, D. A. et al. The role of soil carbon in natural climate solutions. Nat. Sustain. 3, 391–398 (2020).
Article  Google Scholar 

23.
Bonn, A. et al. (eds) Peatland Restoration and Ecosystems: Science, Policy, and Practice (Cambridge Univ. Press, 2016).

24.
Seppälä, M. Surface abrasion of palsas by wind action in Finnish Lapland. Geomorphology 52, 141–148 (2003).
Article  Google Scholar 

25.
Treat, C. et al. Widespread global peatland establishment and persistence over the last 130,000 y. Proc. Natl Acad. Sci. USA 116, 4822–4827 (2019).
CAS  Article  Google Scholar 

26.
Beilman, D. W., MacDonald, G. M., Smith, L. C. & Reimer, P. J. Carbon accumulation in peatlands of West Siberia over the last 2000 years. Global Biogeochem. Cycles 23, GB1012 (2009).
Article  CAS  Google Scholar 

27.
Loisel, J., Gallego-Sala, A. V. & Yu, Z. Global-scale pattern of peatland Sphagnum growth driven by photosynthetically active radiation and growing season length. Biogeosciences 9, 2737–2746 (2012).
CAS  Article  Google Scholar 

28.
Charman, D. J. et al. Climate-related changes in peatland carbon accumulation during the last millennium. Biogeosciences 10, 929–944 (2013).
Article  CAS  Google Scholar 

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

30.
Wang, S., Zhuang, Q., Lähteenoja, O., Draper, F. C. & Cadillo-Quiroz, H. Potential shift from a carbon sink to a source in Amazonian peatlands under a changing climate. Proc. Natl Acad. Sci. USA 115, 12407–12412 (2018).
CAS  Article  Google Scholar 

31.
Sjögersten, S. et al. Temperature response of ex-situ greenhouse gas emissions from tropical peatlands: interactions between forest type and peat moisture conditions. Geoderma 324, 47–55 (2018).
Article  CAS  Google Scholar 

32.
Couwenberg, J., Dommain, R. & Joosten, H. Greenhouse gas fluxes from tropical peatlands in south-east Asia. Glob. Change Biol. 16, 1715–1732 (2010).
Article  Google Scholar 

33.
Carlson, K. M., Goodman, L. K. & May-Tobin, C. C. Modeling relationships between water table depth and peat soil carbon loss in Southeast Asian plantations. Environ. Res. Lett. 10, 074006 (2015).
Article  CAS  Google Scholar 

34.
Hoyt, A. M. et al. CO2 emissions from an undrained tropical peatland: interacting influences of temperature, shading and water table depth. Glob. Change Biol. 25, 2885–2899 (2019).
Article  Google Scholar 

35.
Freeman, C., Ostle, N. & Kang, H. An enzymatic ‘latch’ on a global carbon store. Nature 409, 149 (2001).
CAS  Article  Google Scholar 

36.
Lund, M., Christensen, T. R., Lindroth, A. & Schubert, P. Effects of drought conditions on the carbon dioxide dynamics in a temperate peatland. Environ. Res. Lett. 7, 045704 (2012).
Article  Google Scholar 

37.
Cobb, A. R. et al. How temporal patterns in rainfall determine the geomorphology and carbon fluxes of tropical peatlands. Proc. Natl Acad. Sci. USA 114, E5187–E5196 (2017).
CAS  Google Scholar 

38.
Dargie, G. C. et al. Age, extent and carbon storage of the central Congo Basin peatland complex. Nature 542, 86–90 (2017).
CAS  Article  Google Scholar 

39.
Henman, J. & Poulter, B. Inundation of freshwater peatlands by sea level rise: uncertainty and potential carbon cycle feedbacks. J. Geophys. Res. Atmos. 113, G01011 (2008).
Article  CAS  Google Scholar 

40.
Rogers, K. et al. Wetland carbon storage controlled by millennial-scale variation in relative sea-level rise. Nature 567, 91–96 (2019).
CAS  Article  Google Scholar 

41.
Dommain, R., Couwenberg, J. & Joosten, H. Development and carbon sequestration of tropical peat domes in south-east Asia: links to post-glacial sea-level changes and Holocene climate variability. Quat. Sci. Rev. 30, 999–1010 (2011).
Article  Google Scholar 

42.
Packalen, M. S. & Finkelstein, S. A. Quantifying Holocene variability in carbon uptake and release since peat initiation in the Hudson Bay Lowlands, Canada. Holocene 24, 1063–1074 (2014).
Article  Google Scholar 

43.
Grundling P.-L. The role of sea-level rise in the formation of peatlands in Maputaland. Boletim Geológico (Ministerio dos Recursos Minerais e Energia, Direccao Geral de Geologia Mozambique) 43, 58–67 (2004).

44.
Kirwan, M. L. & Mudd, S. M. Response of salt-marsh carbon accumulation to climate change. Nature 489, 550–553 (2012).
CAS  Article  Google Scholar 

45.
Briggs, J. et al. Influence of climate and hydrology on carbon in an early Miocene peatland. Earth Planet. Sci. Lett. 253, 445–454 (2007).
CAS  Article  Google Scholar 

46.
Lähteenoja, O. et al. The large Amazonian peatland carbon sink in the subsiding Pastaza-Marañón foreland basin, Peru. Glob. Change Biol. 18, 164–178 (2012).
Article  Google Scholar 

47.
Hooijer, A. et al. Subsidence and carbon loss in drained tropical peatlands. Biogeosciences 9, 1053–1071 (2012).
CAS  Article  Google Scholar 

48.
Whittle, A. & Gallego-Sala, A. V. Vulnerability of the peatland carbon sink to sea-level rise. Sci. Rep. 6, 28758 (2016).
CAS  Article  Google Scholar 

49.
Blankespoor, B., Dasgupta, S. & Laplante, B. Sea-level rise and coastal wetlands. Ambio 43, 996–1005 (2014).
Article  Google Scholar 

50.
Spencer, T. et al. Global coastal wetland change under sea-level rise and related stresses: the DIVA Wetland Change Model. Glob. Planet. Change 139, 15–30 (2016).
Article  Google Scholar 

51.
Zuidhoff, F. S. & Kolstrup, E. Changes in palsa distribution in relation to climate change in Laivadalen, northern Sweden, especially 1960–1997. Permafr. Periglac. Process. 11, 55–69 (2000).
Article  Google Scholar 

52.
Payette, S., Delwaide, A., Caccianiga, M. & Beauchemin, M. Accelerated thawing of subarctic peatland permafrost over the last 50 years. Geophys. Res. Lett. 31, L18208 (2004).
Article  Google Scholar 

53.
Borge, A. F., Westermann, S., Solheim, I. & Etzelmüller, B. Strong degradation of palsas and peat plateaus in northern Norway during the last 60 years. Cryosphere 11, 1–16 (2017).
Article  Google Scholar 

54.
Cooper, M. D. A. et al. Limited contribution of permafrost carbon to methane release from thawing peatlands. Nat. Clim. Change 7, 507–511 (2017).
CAS  Article  Google Scholar 

55.
Bubier, J., Moore, T., Bellisario, L., Comer, N. T. & Crill, P. M. Ecological controls on methane emissions from a Northern Peatland Complex in the zone of discontinuous permafrost, Manitoba, Canada. Global Biogeochem. Cycles 9, 455–470 (1995).
CAS  Article  Google Scholar 

56.
Christensen, T. R. et al. Thawing sub-arctic permafrost: effects on vegetation and methane emissions. Geophys. Res. Lett. 31, L04501 (2004).
Article  CAS  Google Scholar 

57.
Olefeldt, D., Turetsky, M. R., Crill, P. M. & McGuire, A. D. Environmental and physical controls on northern terrestrial methane emissions across permafrost zones. Glob. Change Biol. 19, 589–603 (2013).
Article  Google Scholar 

58.
O’Donnell, J. A. et al. The effects of permafrost thaw on soil hydrologic, thermal, and carbon dynamics in an Alaskan peatland. Ecosystems 15, 213–229 (2012).
Article  CAS  Google Scholar 

59.
Jones, M. C. et al. Rapid carbon loss and slow recovery following permafrost thaw in boreal peatlands. Glob. Change Biol. 23, 1109–1127 (2017).
Article  Google Scholar 

60.
Turetsky, M. R. et al. Carbon release through abrupt permafrost thaw. Nat. Geosci. 13, 138–143 (2020).
CAS  Article  Google Scholar 

61.
Jones, M. C., Grosse, G., Jones, B. M. & Walter Anthony, K. Peat accumulation in drained thermokarst lake basins in continuous, ice‐rich permafrost, northern Seward Peninsula, Alaska. J. Geophys. Res. Biogeosci. 117, G00M07 (2012).
Google Scholar 

62.
Walter Anthony, K. M. et al. A shift of thermokarst lakes from carbon sources to sinks during the Holocene epoch. Nature 511, 452–456 (2014).
Article  CAS  Google Scholar 

63.
Turetsky, M. R., Wieder, R. K., Vitt, D. H., Evans, R. J. & Scott, K. D. The disappearance of relict permafrost in boreal North America: effects on peatland carbon storage and fluxes. Glob. Change Biol. 13, 1922–1934 (2007).
Article  Google Scholar 

64.
Rossi, S. et al. FAOSTAT estimates of greenhouse gas emissions from biomass and peat fires. Climatic Change 135, 699–711 (2016).
CAS  Article  Google Scholar 

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

66.
Field, R. D. et al. Indonesian fire activity and smoke pollution in 2015 show persistent nonlinear sensitivity to El Niño-induced drought. Proc. Natl Acad. Sci. USA 113, 9204–9209 (2016).
CAS  Article  Google Scholar 

67.
Gaveau, D. L. A. et al. Major atmospheric emissions from peat fires in Southeast Asia during non-drought years: evidence from the 2013 Sumatran fires. Sci. Rep. 4, 6112 (2014).
CAS  Article  Google Scholar 

68.
Lyu, Z. et al. The role of environmental driving factors in historical and projected carbon dynamics of wetland ecosystems in Alaska. Ecol. Appl. 28, 1377–1395 (2018).
Article  Google Scholar 

69.
Gibson, C. M. et al. Wildfire as a major driver of recent permafrost thaw in boreal peatlands. Nat. Commun. 9, 3041 (2018).
Article  CAS  Google Scholar 

70.
Dadap, N. C., Cobb, A. R., Hoyt, A. M., Harvey, C. F. & Konings, A. G. Satellite soil moisture observations predict burned area in Southeast Asian peatlands. Environ. Res. Lett. 14, 094014 (2019).
Article  Google Scholar 

71.
Zaccone, C. et al. Smouldering fire signatures in peat and their implications for palaeoenvironmental reconstructions. Geochim. Cosmochim. Acta 137, 134–146 (2014).
CAS  Article  Google Scholar 

72.
Kettridge, N. et al. Burned and unburned peat water repellency: implications for peatland evaporation following wildfire. J. Hydrol. 513, 335–341 (2014).
Article  Google Scholar 

73.
Koh, L. P., Miettinen, J., Liew, S. C. & Ghazoul, J. Remotely sensed evidence of tropical peatland conversion to oil palm. Proc. Natl Acad. Sci. USA 108, 5127–5132 (2011).
CAS  Article  Google Scholar 

74.
Rooney, R. C., Bayley, S. E. & Schindler, D. W. Oil sands mining and reclamation cause massive loss of peatland and stored carbon. Proc. Natl Acad. Sci. USA 109, 4933–4937 (2012).
CAS  Article  Google Scholar 

75.
Turunen, J. Development of Finnish peatland area and carbon storage 1950–2000. Boreal Environ. Res. 13, 319–334 (2008).
CAS  Google Scholar 

76.
Wild, B. et al. Rivers across the Siberian Arctic unearth the patterns of carbon release from thawing permafrost. Proc. Natl Acad. Sci. USA 116, 10280–10285 (2019).
CAS  Article  Google Scholar 

77.
Hoyt, A. M., Chaussard, E., Seppalainen, S. S. & Harvey, C. F. Widespread subsidence and carbon emissions across Southeast Asian peatlands. Nat. Geosci. 13, 435–440 (2020).
CAS  Article  Google Scholar 

78.
Tuittila, E.-S. et al. Methane dynamics of a restored cut-away peatland. Glob. Change Biol. 6, 569–581 (2000).
Article  Google Scholar 

79.
Waddington, J. M. & Day, S. M. Methane emissions from a peatland following restoration. J. Geophys. Res. Biogeosci. 112, G03018 (2007).
Article  CAS  Google Scholar 

80.
Vet, R. et al. A global assessment of precipitation chemistry and deposition of sulfur, nitrogen, sea salt, base cations, organic acids, acidity and pH, and phosphorus. Atmos. Environ. 93, 3–100 (2014).
CAS  Article  Google Scholar 

81.
Dentener, F. et al. Nitrogen and sulfur deposition on regional and global scales: a multimodel evaluation. Global Biogeochem. Cycles 20, GB4003 (2006).
Article  CAS  Google Scholar 

82.
Bubier, J. L., Moore, T. R. & Bledzki, L. A. Effects of nutrient addition on vegetation and carbon cycling in an ombrotrophic bog. Glob. Change Biol. 13, 1168–1186 (2007).
Article  Google Scholar 

83.
Juutinen, S. et al. Responses of mosses Sphagnum capillifolium and Polytrichum strictum to nitrogen deposition in a bog: height growth, ground cover, and CO2 exchange. Botany 94, 127–138 (2016).
CAS  Article  Google Scholar 

84.
Wieder, R. K. et al. Experimental nitrogen addition alters structure and function of a boreal bog: critical load and thresholds revealed. Ecol. Monogr. 89, e01371 (2019).
Article  Google Scholar 

85.
Limpens, J. et al. Climatic modifiers of the response to nitrogen deposition in peat-forming Sphagnum mosses: a meta-analysis. New Phytol. 191, 496–507 (2011).
CAS  Article  Google Scholar 

86.
Larmola, T. et al. Vegetation feedbacks of nutrient deposition lead to a weaker carbon sink in an ombrotrophic bog. Glob. Change Biol. 19, 3729–3739 (2013).
Article  Google Scholar 

87.
Pinsonneault, A. J., Moore, T. R. & Roulet, N. T. Effects of long-term fertilization on peat stoichiometry and associated microbial enzyme activity in an ombrotrophic bog. Biogeochemistry 129, 149–164 (2016).
CAS  Article  Google Scholar 

88.
Bragazza, L. et al. Atmospheric nitrogen deposition promotes carbon loss from peat bogs. Proc. Natl Acad. Sci. USA 103, 19386–19389 (2006).
CAS  Article  Google Scholar 

89.
Juutinen, S. et al. Long-term nutrient addition increased CH4 emission from a bog through direct and indirect effects. Sci. Rep. 8, 3838 (2018).
Article  CAS  Google Scholar 

90.
Olid, C., Nilsson, M. B., Eriksson, T. & Klaminder, J. The effects of temperature and nitrogen and sulfur additions on carbon accumulation in a nutrient-poor boreal mire: decadal effects assessed using 210Pb peat chronologies. J. Geophys. Res. Biogeosci. 119, 392–403 (2014).
CAS  Article  Google Scholar 

91.
Alexandrov, G. A., Brovkin, V. A., Kleinen, T. & Yu, Z. The capacity of northern peatlands for long-term carbon sequestration. Biogeosciences 17, 47–54 (2020).
CAS  Article  Google Scholar 

92.
Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. USA 114, 11645–11650 (2017).
CAS  Article  Google Scholar 

93.
Christensen, T. R., Arora, V. K., Gauss, M., Höglund-Isaksson, L. & Parmentier, F.-J. W. Tracing the climate signal: mitigation of anthropogenic methane emissions can outweigh a large Arctic natural emission increase. Sci. Rep. 9, 1146 (2019).
Article  CAS  Google Scholar 

94.
Mach, K. J., Mastrandrea, M. D., Freeman, P. T. & Field, C. B. Unleashing expert judgment in assessment. Glob. Environ. Change 44, 1–14 (2017).
Article  Google Scholar 

95.
Schuur, E. A. G. et al. Expert assessment of vulnerability of permafrost carbon to climate change. Climatic Change 119, 359–374 (2013).
CAS  Article  Google Scholar 

96.
Bamber, J. L., Oppenheimer, M., Kopp, R. E., Aspinall, W. P. & Cooke, R. M. Ice sheet contributions to future sea-level rise from structured expert judgment. Proc. Natl Acad. Sci. USA 116, 11195–11200 (2019).
CAS  Article  Google Scholar  More

1.
Zhang, Z., Lu, X., Song, X., Guo, Y. & Xue, Z. Soil C, N and P stoichiometry of Deyeuxia angustifolia and Carex lasiocarpa wetlands in Sanjiang Plain Northeast China. J. Soils Sediments 9(12), 1309–1315 (2012).
Article  CAS  Google Scholar 
2.
Sardans, J. et al. Plant invasion is associated with higher plant-soil nutrient concentrations in nutrient-poor environments. Global Change Biol. 23(3), 1282–1291 (2017).
ADS  Article  Google Scholar 

3.
Aneja, M. et al. Microbial colonization of beech and spruce litter: influence of decomposition site and plant litter species on the diversity of microbial community. Microb. Ecol. 52, 127–135 (2006).
PubMed  Article  Google Scholar 

4.
Tilman, D. The resource–ratio hypothesis of plant succession. Am. Nat. 125(6), 827–852 (1985).
Article  Google Scholar 

5.
Li, F. et al. Changes in soil microbial biomass and functional diversity with a nitrogen gradient in soil columns. Appl. Soil Ecol. 64, 1–6 (2013).
MathSciNet  Article  Google Scholar 

6.
Bowles, T. M., Acosta-Martínez, V., Calderón, F. & Jackson, L. E. Soil enzyme activities, microbial communities, and carbon and nitrogen availability in organic agroecosystems across an intensively-managed agricultural landscape. Soil Biol. Biochem. 68, 252–262 (2014).
CAS  Article  Google Scholar 

7.
Caldwell, B. A. Enzyme activities as a component of soil biodiversity: a review. Pedobiologia 49(6), 637–644 (2005).
CAS  Article  Google Scholar 

8.
Wang, B. et al. Changes in soil nutrient and enzyme activities under different vegetations in the Loess Plateau area Northwest China. CATENA 92, 186–195 (2012).
CAS  Article  Google Scholar 

9.
Singh, D. K. & Kumar, S. Nitrate reductase, arginine deaminase, urease and dehydrogenase activities in natural soil (ridges with forest) and in cotton soil after acetamiprid treatments. Chemosphere 71(3), 412–418 (2008).
ADS  MathSciNet  CAS  PubMed  Article  Google Scholar 

10.
Blagodatskaya, E., Blagodatsky, S., Khomyakov, N., Myachina, O. & Kuzyakov, Y. Temperature sensitivity and enzymatic mechanisms of soil organic matter decomposition along an altitudinal gradient on Mount Kilimanjaro. Sci. Rep. 6, 22240 (2016).
ADS  CAS  Article  Google Scholar 

11.
Yao, Y., Shao, M., Fu, X., Wang, X. & Wei, X. Effects of shrubs on soil nutrients and enzymatic activities over a 0–100 cm soil profile in the desert-loess transition zone. CATENA 174, 362–370 (2019).
CAS  Article  Google Scholar 

12.
Bartkowiak, A. & Lemanowicz, J. Effect of forest fire on changes in the content of total and available forms of selected heavy metals and catalase activity in soil. Soil Sci. Ann. 68(3), 140–148 (2017).
CAS  Article  Google Scholar 

13.
Chen, S. K., Edwards, C. A. & Subler, S. The influence of two agricultural biostimulants on nitrogen transformations, microbial activity, and plant growth in soil microcosms. Soil Biol. Biochem. 35(1), 9–19 (2003).
CAS  Article  Google Scholar 

14.
Baum, C., Leinweber, P. & Schlichting, A. Effects of chemical conditions in re-wetted peats on temporal variation in microbial biomass and acid phosphatase activity within the growing season. Appl. Soil Ecol. 22(2), 167–174 (2003).
Article  Google Scholar 

15.
Štursová, M. & Baldrian, P. Effects of soil properties and management on the activity of soil organic matter transforming enzymes and the quantification of soil-bound and free activity. Plant Soil 338(1–2), 99–110 (2011).
Article  CAS  Google Scholar 

16.
Peng, F., Quangang, Y., Xue, X., Guo, J. & Wang, T. Effects of rodent-induced land degradation on ecosystem carbon fluxes in an alpine meadow in the Qinghai-Tibet Plateau, China. Solid Earth. 6(1) 303–310 (2015).

17.
Liljedahl, A. K. et al. Pan-Arctic ice-wedge degradation in warming permafrost and its influence on tundra hydrology. Nat. Geosci. 9(4), 312–318 (2016).
ADS  CAS  Article  Google Scholar 

18.
Tietjen, B. et al. Climate change-induced vegetation shifts lead to more ecological droughts despite projected rainfall increases in many global temperate drylands. Global Change Biol. 23(7), 2743–2754 (2017).
ADS  Article  Google Scholar 

19.
Hao, L. et al. Quantifying the effects of overgrazing on mountainous watershed vegetation dynamics under a changing climate. Sci. Total Environ. 639, 1408–1420 (2018).
ADS  CAS  PubMed  Article  Google Scholar 

20.
Wen, L. et al. The impact of land degradation on the C pools in alpine grasslands of the Qinghai-Tibet Plateau. Plant Soil 368(1–2), 329–340 (2013).
CAS  Article  Google Scholar 

21.
Zhang, W., Xue, X., Peng, F., You, Q. & Hao, A. Meta-analysis of the effects of grassland degradation on plant and soil properties in the alpine meadows of the Qinghai-Tibetan Plateau. Glob. Ecol. Conserv. 20, e00774 (2019).
Article  Google Scholar 

22.
Che, R. et al. Increase in ammonia-oxidizing microbe abundance during degradation of alpine meadows may lead to greater soil nitrogen loss. Biogeochemistry 136(3), 341–352 (2017).
CAS  Article  Google Scholar 

23.
Zhang, Q. et al. Distribution of soil nutrients, extracellular enzyme activities and microbial communities across particle-size fractions in a long-term fertilizer experiment. Appl. Soil Ecol. 94, 59–71 (2015).
Article  Google Scholar 

24.
Liu, X. & Chen, B. Climatic warming in the Tibetan Plateau during recent decades. Int. J. Climatol. 20, 1729–1742 (2000).
Article  Google Scholar 

25.
Wu, P. et al. Impacts of alpine wetland degradation on the composition, diversity and trophic structure of soil nematodes on the Qinghai-Tibetan Plateau. Sci. Rep. 7(1), 837 (2017).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

26.
Li, B., Dong, S. C., Jiang, X. B. & Li, Z. H. Analysis on the driving factors of grassland desertification in Zoige wetland. J. Soil Water Conserv. 15, 112–115 (2008).
Google Scholar 

27.
Zhang, Y. et al. Alpine wetland in the Lhasa River Basin China. J. Geogr Sci. 20(3), 375–388 (2010).
Article  Google Scholar 

28.
Wu, J. Q. et al. Vegetation degradation along water gradient leads to soil active organic carbon loss in Gahai wetland. Ecol. Eng. 145, 105666 (2020).
Article  Google Scholar 

29.
Liu, L. F. et al. Water table drawdown reshapes soil physicochemical characteristics in Zoige peatlands. CATENA 170, 119–128 (2018).
CAS  Article  Google Scholar 

30.
Ma, W. W. et al. Greenhouse gas emissions as influenced by wetland vegetation degradation along a moisture gradient on the eastern Qinghai-Tibet Plateau of North-West China. Nutr. Cycl. Agroecosys. 112, 335–354 (2018).
Article  Google Scholar 

31.
Yang, Z. et al. The linkage between vegetation and soil nutrients and their variation under different grazing intensities in an alpine meadow on the eastern Qinghai-Tibetan Plateau. Ecol. Eng. 110, 128–136 (2018).
Article  Google Scholar 

32.
Alhassan, A. M. et al. Response of soil organic carbon to vegetation degradation along a moisture gradient in a wet meadowon the Qinghai-Tibet Plateau. Ecol. Evol. 8, 11999–12010 (2018).
PubMed  PubMed Central  Article  Google Scholar 

33.
Li, Z. et al. Dynamics of soil respiration in alpine wetland meadows exposed to different levels of degradation in the Qinghai-Tibet Plateau China. Sci. Rep. 9, 7469 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

34.
Bechmann, M. E., Kleinman, P. J., Sharpley, A. N. & Saporito, L. S. Freeze-thaw effects on phosphorus loss in runoff from manured and catch-cropped soils. J. Environ Qual. 34, 2301–2309 (2005).
CAS  PubMed  Article  Google Scholar 

35.
Joseph, G. & Henry, H. A. Soil nitrogen leaching losses in response to freeze-thaw cycles and pulsed warming in a temperate old field. Soil Biol. Biochem. 40, 1947–1953 (2008).
CAS  Article  Google Scholar 

36.
Ren, J. et al. Shifts in soil bacterial and archaeal communities during freeze-thaw cycles in a seasonal frozen marsh Northeast China. Sci. Total Environ. 625, 782–791 (2018).
ADS  CAS  PubMed  Article  Google Scholar 

37.
McGill, W. B. & Figueiredo, C. T. Total nitrogen. In Soil Sampling and Methods of Analysis (ed. Carter, M. R.) 201–211 (Canadian Society of Soil Science/Lewis Publishers, Boca Raton, 1993).
Google Scholar 

38.
Lu, R. K. Soil and Agricultural Chemistry Analysis Method (China Agriculture Science and Technique Press, Beijing, 2000).
Google Scholar 

39.
Guan, Y. S. Soil Enzyme and Research Method 309–313 (Agricultural Press, Beijing, 1986).
Google Scholar 

40.
Yin, R., Deng, H., Wang, H. L. & Zhang, B. Vegetation type affects soil enzyme activities and microbial functional diversity following re-vegetation of a severely eroded red soil in sub-tropical China. CATENA 115, 96–103 (2014).
Article  Google Scholar 

41.
Ge, G. et al. Soil biological activity and their seasonal variations in response to long-term application of organic and inorganic fertilizers. Plant soil. 326(1–2), 31 (2010).
CAS  Article  Google Scholar 

42.
Xie, X. et al. Response of soil physicochemical properties and enzyme activities to long-term reclamation of coastal saline soil Eastern China.. Sci. Total Environ. 607, 1419–1427 (2017).
ADS  PubMed  Article  CAS  Google Scholar 

43.
Cao, J., Ji, D. & Wang, C. Interaction between earthworms and arbuscular mycorrhizal fungi on the degradation of oxytetracycline in soils. Soil Biol. Biochem. 90, 283–292 (2015).
CAS  Article  Google Scholar 

44.
Li, Q., Liang, J. H., He, Y. Y., Hu, Q. J. & Yu, S. Effect of land use on soil enzyme activities at karst area in Nanchuan, Chongqing Southwest China. Plant Soil Environ. 60(1), 15–20 (2014).
CAS  Article  Google Scholar 

45.
Mitsch, W. J. & Gosselink, J. G. Wetland biogeochemistry. Wetlands. 3, 155–204 (2000).
Google Scholar 

46.
Dijkstra, F., Cheng, W. & Johnson, D. Plant biomass influences rhizosphere priming effects on soil organic matter decomposition in two differently managed soils. Soil Biol. Biochem. 38, 2519–2526 (2006).
CAS  Article  Google Scholar 

47.
Zhang, F., Shen, J., Li, L. & Liu, X. An overview of rhizosphere processes related with plant nutrition in major cropping systems in China. Plant Soil 260(1–2), 89–99 (2004).
CAS  Article  Google Scholar 

48.
Wang, W. et al. Responses of soil nutrient concentrations and stoichiometry to different human land uses in a subtropical tidal wetland. Geoderma 232–234, 459–470 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

49.
Yan, J. et al. Plant litter composition selects different soil microbial structures and in turn drives different litter decomposition pattern and soil carbon sequestration capability. Geoderma 319, 194–203 (2018).
ADS  CAS  Article  Google Scholar 

50.
Tesfaye, M. A., Bravo, F., Ruiz-Peinado, R., Pando, V. & Bravo-Oviedo, A. Impact of changes in land use, species and elevation on soil organic carbon and total nitrogen in Ethiopian Central Highlands. Geoderma 261, 70–79 (2016).
ADS  CAS  Article  Google Scholar 

51.
Enriquez, A. S., Chimner, R. A., Cremona, M. V., Diehl, P. & Bonvissuto, G. L. Grazing intensity levels influence C reservoirs of wet and mesic meadows along a precipitation gradient in Northern Patagonia. Wetl. Ecol. Manag. 23, 439–451 (2015).
CAS  Article  Google Scholar 

52.
Hassan, A. et al. Depth distribution of soil organic carbon fractions in relation to tillage and cropping sequences in some dry lands of Punjab Pakistan. Land Degrad. Dev. 27, 1175–1185 (2016).
Article  Google Scholar 

53.
Li, J. et al. Soil labile organic carbon fractions and soil organic carbon stocks as affected by long-term organic and mineral fertilization regimes in the North China plain. Soil Tillage Res. 175, 281–290 (2018).
Article  Google Scholar 

54.
Liu, E. et al. Seasonal changes and vertical distributions of soil organic carbon pools under conventional and no-till practices on Loess Plateau in China. Soil Sci. Soc. Am. J. 79(2), 517–526 (2015).
ADS  CAS  Article  Google Scholar 

55.
Wuest, S. Seasonal variation in soil organic carbon. Soil Sci. Soc. Am. J. 78(4), 1442–1447 (2014).
ADS  Article  CAS  Google Scholar 

56.
Suyker, A. E. & Verma, S. B. Year-round observations of the net ecosystem exchange of carbon dioxide in a native tallgrass prairie. Global Change Biol. 7, 279–289 (2001).
ADS  Article  Google Scholar 

57.
Tang, S. et al. Decomposition of soil organic carbon influenced by soil temperature and moisture in Andisol and Inceptisol paddy soils in a cold temperate region of Japan. J. Soils Sediments. 17(7), 1843–1851 (2017).
CAS  Article  Google Scholar 

58.
Li, Y. et al. Changes of soil microbial community under different degraded gradients of alpine meadow. Agric. Ecosyst. Environ. 222, 213–222 (2016).
Article  Google Scholar 

59.
Luo, W. et al. Plant nutrients do not covary with soil nutrients under changing climatic conditions. Global Biogeochem. Cycle 29, 1298–1308 (2015).
ADS  CAS  Article  Google Scholar 

60.
Foote, J. A., Boutton, T. W. & Scott, D. A. Soil C and N storage and microbial biomass in US southern pine forests: influence of forest management. For. Ecol. Manag. 355, 48–57 (2015).
Article  Google Scholar 

61.
Lost, S., Landgraf, D. & Makeschin, F. Chemical soil properties of reclaimed marsh soil from Zhejiang Province P.R China. Geoderma 142(3–4), 245–250 (2007).
ADS  Google Scholar 

62.
Zhang, L. P., Jia, G. M. & Xi, Y. The soil enzyme activities with age of tea in three gorges reservoir area. Adv. Mat. Res. 989, 1292–1296 (2014).
Google Scholar 

63.
Manzoni, S., Trofymow, J. A., Jackson, R. B. & Porporato, A. Stoichiometric controls on carbon, nitrogen, and phosphorus dynamics in decomposing litter. Ecol. Monogr. 80(1), 89–106 (2010).
Article  Google Scholar 

64.
Chaparro, J. M. et al. Root exudation of phytochemicals in Arabidopsis follows specific patterns that are developmentally programmed and correlate with soil microbial functions. PLoS ONE 8(2), 55731 (2013).
ADS  Article  CAS  Google Scholar 

65.
Bais, H. P., Weir, T. L., Perry, L. G., Gilroy, S. & Vivanco, J. M. The role of root exudates in rhizosphere interactions with plants and other organisms. Ann. Rev. Plant Biol. 57, 233–266 (2006).
CAS  Article  Google Scholar 

66.
Hinsinger, P. et al. P for two, sharing a scarce resource: soil phosphorus acquisition in the rhizosphere of intercropped species. Plant Physiol. 156(3), 1078–1086 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

67.
Peng, J., Li, Y., Tian, L., Liu, Y. & Wang, Y. Vegetation dynamics and associated driving forces in Eastern China during 1999–2008. Remote Sens-Basel 7(10), 13641–13663 (2015).
ADS  Article  Google Scholar 

68.
Yu, C. et al. Soil nutrient changes induced by the presence and intensity of plateaupika (ochotona curzoniae) disturbances in the qinghai-tibet plateau, china. Ecol. Eng. 106, 1–9 (2017).
Article  Google Scholar 

69.
Saggar, S., Parfitt, R. L., Salt, G. & Skinner, M. F. Carbon and phosphorus transformations during decomposition of pine forest floor with different phosphorus status. Biol. Fert. Soils. 27(2), 197–204 (1998).
CAS  Article  Google Scholar 

70.
Hanson, P. J., Edwards, N. T., Garten, C. T. & Andrews, J. A. Separating root and soil microbial contributions to soil respiration: a review of methods and observations. Biogeochemistry 48(1), 115–146 (2000).
CAS  Article  Google Scholar 

71.
De Feudis, M. et al. Effect of beech (Fagus sylvatica L.) rhizosphere on phosphorous availability in soils at different altitudes (Central Italy). Geoderma 276, 53–63 (2016).
ADS  Article  CAS  Google Scholar 

72.
Huang, W. & Spohn, M. Effects of long-term litter manipulation on soil carbon, nitrogen, and phosphorus in a temperate deciduous forest. Soil Biol. Biochem. 83, 12–18 (2015).
CAS  Article  Google Scholar 

73.
Peng, S. Z., Yang, S. H., Xu, J. Z., Luo, Y. F. & Hou, H. J. Nitrogen and phosphorus leaching losses from paddy fields with different water and nitrogen managements. Paddy Water Environ. 9(3), 333–342 (2011).
Article  Google Scholar 

74.
Deng, J. et al. Soil C, N, P and its stratification ratio affected by artificial vegetation in subsoil Loess Plateau China. PLoS ONE 11(3), e0151446 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

75.
Liu, J. et al. Effect of seasonal freeze–thaw cycle on net nitrogen mineralization of soil organic layer in the subalpine/alpine forests of western Sichuan China. Acta Ecol. Sin. 33(1), 32–37 (2013).
Article  Google Scholar 

76.
Wang, Y., Wu, Q., Tian, L., Niu, F. & Tan, L. Correlation of alpine vegetation degradation and soil nutrient status of permafrost in the source regions of the Yangtze River China. Environ. Earth Sci. 67(4), 1215–1223 (2012).
CAS  Article  Google Scholar 

77.
Bauhus, J. & Khanna, P. K. Carbon and nitrogen turnover in two acid forest soils of southeast Australia as affected by phosphorus addition and drying and rewetting cycles. Biol. Fert. Soils. 17(3), 212–218 (1994).
CAS  Article  Google Scholar 

78.
Mehnaz, K. R., Corneo, P. E., Keitel, C. & Dijkstra, F. A. Carbon and phosphorus addition effects on microbial carbon use efficiency, soil organic matter priming, gross nitrogen mineralization and nitrous oxide emission from soil. Soil Biol. Biochem. 134, 175–186 (2019).
CAS  Article  Google Scholar 

79.
Acosta-Martinez, V., Cano, A. & Johnson, J. Simultaneous determination of multiple soil enzyme activities for soil health-biogeochemical indices. Appl. Soil Ecol. 126, 121–128 (2018).
Article  Google Scholar 

80.
Poeplau, C., Bolinder, M., Kirchmann, H. & Kätterer, T. Phosphorus fertilisation under nitrogen limitation can deplete soil carbon, stocks: evidence from Swedish meta-replicated long-term field experiments. Biogeosciences 13(4), 1119–1127 (2016).
ADS  CAS  Article  Google Scholar 

81.
Xiao, Y., Huang, Z. G. & Lu, X. G. Changes of soil labile organic carbon fractions and their relation to soil microbial characteristics in four typical wetlands of Sanjiang Plain Northeast China. Ecol. Eng. 82, 381–389 (2015).
Article  Google Scholar 

82.
Vergani, C. & Graf, F. Soil permeability, aggregate stability and root growth: a pot experiment from a soil bioengineering perspective. Ecohydrology. 9(5), 830–842 (2016).
Article  Google Scholar 

83.
Wang, X., Yan, B., Fan, B., Shi, L. & Liu, G. Temperature and soil microorganisms interact to affect Dodonaea viscosa, growth on mountainsides. Plant Ecol. 219(7), 759–774 (2018).

84.
Ross, D. J. A seasonal study of oxygen uptake of some pasture soils and activities of enzymes hydrolysing sucrose and starch. Eur. J. Soil Sci. 16(1), 73–85 (1965).
CAS  Article  Google Scholar 

85.
Tierney, G. L. et al. Soil freezing alters fine root dynamics in a northern hardwood forest. Biogeochemistry 56(2), 175–190 (2001).
CAS  Article  Google Scholar 

86.
Koponen, H. T. et al. Microbial communities, biomass, and activities in soils as affected by freeze thaw cycles. Soil Biol. Biochem. 38(7), 1861–1871 (2006).
CAS  Article  Google Scholar 

87.
Brzezińska, M., Włodarczyk, T., Stępniewski, W. & Przywara, G. Soil aeration status and catalase activity. Acta Agrophys. 5(3), 555–565 (2005).
Google Scholar 

88.
Tegeder, M. & Masclaux-Daubresse, C. Source and sink mechanisms of nitrogen transport and use. New Phytol. 217, 35–53 (2018).
PubMed  Article  Google Scholar 

89.
Bremner, J. M. & Mulvaney, R. L. Urease activity in soils. Soil Enzymes, 149–196 (Academic Press, London, 1978).

90.
Zornoza, R. et al. Assessing air drying and rewetting pretreatment effect on some soil enzyme activities under Mediterranean conditions. Soil Biol. Biochem. 38, 2125–2134 (2006).
CAS  Article  Google Scholar 

91.
Fernandez, D. P., Neff, J. C., Belnap, J. & Reynolds, R. L. Soil respiration in the cold desert environment of the Colorado Plateau (USA): abiotic regulators and thresholds. Biogeochemistry 78(3), 247–265 (2006).
Article  Google Scholar 

92.
Jing, X. et al. No temperature acclimation of soil extracellular enzymes to experimental warming in an alpine grassland ecosystem on the Tibetan Plateau. Biogeochemistry 117(1), 39–54 (2014).
CAS  Article  Google Scholar 

93.
Schindlbacher, A., Schnecker, J., Takriti, M., Borken, W. & Wanek, W. Microbial physiology and soil CO2 efflux after 9 years of soil warming in atemperate forest-no indications for thermal adaptations. Global Change Biol. 21(11), 4265–4277 (2015).
ADS  Article  Google Scholar 

94.
Wallenstein, M. D., Mcmahon, S. K. & Schimel, J. P. Seasonal variation in enzyme activities and temperature sensitivities in Arctic tundra soils. Global Change Biol. 15(7), 1631–1639 (2009).
ADS  Article  Google Scholar 

95.
Kivlin, S. N. & Treseder, K. K. Soil extracellular enzyme activities correspond with abiotic factors more than fungal community composition. Biogeochemistry 117(1), 23–37 (2014).
CAS  Article  Google Scholar 

96.
Allison, S. D. & Treseder, K. K. Warming and drying suppress microbial activity and carbon cycling in boreal forest soils. Global Change Biol. 14(12), 2898–2909 (2008).
ADS  Article  Google Scholar 

97.
Brzostek, E. R. & Finzi, A. C. Seasonal variation in the temperature sensitivity of proteolytic enzyme activity in temperate forest soils. J. Geophys. Res. 117(G1) (2012).

98.
Weintraub, S. R., Wieder, W. R., Cleveland, C. C. & Townsend, A. R. Organic matter inputs shift soil enzyme activity and allocation patterns in a wettropical forest. Biogeochemistry 114(1/3), 313–326 (2013).
CAS  Article  Google Scholar 

99.
Wang, B., Liu, G. B., Xue, S. & Zhu, B. Changes in soil physico-chemical and microbiological properties during natural succession on abandoned farmland in the Loess Plateau. Environ. Earth Sci. 62(5), 915–925 (2011).
ADS  CAS  Article  Google Scholar 

100.
Yang, L., Li, T., Li, F., Lemcoff, J. H. & Cohen, S. Fertilization regulates soil enzymatic activity and fertility dynamics in a cucumber field. Sci. Hortic. 116(1), 21–26 (2008).
CAS  Article  Google Scholar 

101.
Alkorta, I. et al. Soil enzyme activities as biological indicators of soil health. Rev. Environ. Health. 18(1), 65–73 (2003).
PubMed  Article  Google Scholar 

102.
Burns, R. G. et al. Soil enzymes in a changing environment: Current knowledge and future directions. Soil Biol. Biochem. 58, 216–227 (2013).
CAS  Article  Google Scholar 

103.
Cao, C. et al. Soil chemical and microbiological properties along a chronosequence of Caragana microphylla Lam. plantations in the Horqin sandy land of Northeast China. Appl. Soil Ecol. 40(1), 0–85 (2008).
Article  Google Scholar 

104.
Hao, Y., Chang, Q., Li, L. H. & Wei, X. R. Impacts of landform, land use and soil type on soil chemical properties and enzymatic activities in a Loessial Gully watershed. Soil Res. 52(5), 453 (2014).
CAS  Article  Google Scholar 

105.
Qi, R. et al. Temperature effects on soil organic carbon, soil labile organic carbon fractions, and soil enzyme activities under long-term fertilization regimes. Appl. Soil Ecol. 102, 36–45 (2016).
Article  Google Scholar 

106.
Zhang, H., Zeng, Q., An, S., Dong, Y. & Darboux, F. Soil carbon fractions and enzyme activities under different vegetation types on the Loess Plateau of China. Solid Earth Discuss. 2016, 1–27. https://doi.org/10.5194/se-2016-137 (2016). More

1.
Paerl, H. W. & Huisman, J. Blooms like it hot. Science 320, 57–58 (2008).
CAS  PubMed  Article  Google Scholar 
2.
Ma, J. & Li, H. Preliminary discussion on eutrophication status of lakes, reservoirs and reivers in China and overseas. Resour. Environ. Yangtze Val. 11, 575–578 (2002).
CAS  Google Scholar 

3.
Huisman, J. et al. Cyanobacterial blooms. Nat. Rev. Microbiol. 16, 471–483 (2018).
CAS  PubMed  Article  Google Scholar 

4.
Paerl, H. W. Transfer of N2 and CO2 fixation products from Anabaena oscillarioides to associated bacteria during inorganic carbon sufficiency and deficiency. J. Phycol. 20, 600–608 (1984).
CAS  Article  Google Scholar 

5.
Danillo, O. A., Marli, F. F. & Alessandro, M. V. A metagenomic approach to cyanobacterial genomics. Front. Microbiol. 8, 809–824 (2017).
Article  Google Scholar 

6.
Schindler, W. D. Eutrophication and recovery in experimental lakes: Implications for lake management. Science 184, 897–899 (1974).
CAS  PubMed  Article  Google Scholar 

7.
Zhou, J. et al. Phycosphere microbial succession patterns and assembly mechanisms in a marine Dinoflagellate bloom. Appl. Environ. Microbiol. 85, e00349–19 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

8.
Codd, G. A., Lindsay, J., Young, F. M., Morrison, L. F. & Metcalf, J. S. Harmful cyanobacteria. In: Harmful Cyanobacteria. Aquatic Ecology Series (eds Huisman J., Matthijs H. C. & Visser P. M.). Vol. 3, 1–23 (Springer Netherlands: Dordrecht, The Netherlands,2005).

9.
Christoffersen, K. & Kaas, H. Toxic cyanobacteria in water. A guide to their public health consequences, monitoring, and management. Limnol. Oceanogr. 45, 1212–1212 (2000).
Article  Google Scholar 

10.
Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).
Article  Google Scholar 

11.
Zhu, Y. G. et al. Continental-scale pollution of estuaries with antibiotic resistance genes. Nat. Microbiol. 2, 16270 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

12.
Laxminarayan, R. et al. Antibiotic resistance—the need for global solutions. Lancet Infect. Dis. 13, 1057–1098 (2013).
PubMed  Article  PubMed Central  Google Scholar 

13.
El-Tahawy, A. T. A. The crisis of antibiotic-resistance in bacteria. Saudi. Med. J. 25, 837–842 (2004).
PubMed  PubMed Central  Google Scholar 

14.
Tripathi, V. & Tripathi, P. in Perspectives in Environmental Toxicology. Environmental Science and Engineering. (ed. Kesari, K.) 183–201 (Springer, Cham: Cham, Switzerland, 2017).

15.
Gorokhova, E. et al. Bacteria-mediated effects of antibiotics on Daphnia nutrition. Environ. Sci. Technol. 49, 5779–5787 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

16.
Willing, B. P., Russell, S. L. & Finlay, B. B. Shifting the balance: antibiotic effects on host-microbiota mutualism. Nat. Rev. Microbiol. 9, 233–243 (2011).
CAS  PubMed  Article  Google Scholar 

17.
Garcia-Armisen, T. et al. Antimicrobial resistance of heterotrophic bacteria in sewage-contaminated rivers. Water Res. 45, 788–796 (2011).
CAS  PubMed  Article  Google Scholar 

18.
Czekalski, N., Sigdel, R., Birtel, J., Matthews, B. & Bürgmann, H. Does human activity impact the natural antibiotic resistance background? Abundance of antibiotic resistance genes in 21 Swiss lakes. Environ. Int. 81, 45–55 (2015).
CAS  PubMed  Article  Google Scholar 

19.
Bondarczuk, K., Markowicz, A. & Piotrowska-Seget, Z. The urgent need for risk assessment on the antibiotic resistance spread via sewage sludge land application. Environ. Int. 87, 49–55 (2016).
CAS  PubMed  Article  Google Scholar 

20.
Holger, H. & Kornelia, S. Manure and sulfadiazine synergistically increased bacterial antibiotic resistance in soil over at least two months. Environ. Microbiol. 9, 657–666 (2010).
Google Scholar 

21.
Baquero, F., Martinez, J. L. & Canton, R. Antibiotics and antibiotic resistance in water environments. Curr. Opin. Biotechnol. 19, 260–265 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

22.
Xi, C. et al. Prevalence of antibiotic resistance in drinking water treatment and distribution systems. Appl. Environ. Microbiol. 75, 5714–5718 (2009).
CAS  PubMed  PubMed Central  Article  Google Scholar 

23.
Witte, W. Ecological impact of antibiotic use in animals on different complex microflora: environment. Int. J. Antimicrob. Agents 14, 321–325 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

24.
Feng, J. L. et al. Identification and characterization of integron-associated antibiotic resistant Laribacter hongkongensis isolated from aquatic products in China. Int. J. Food Microbiol. 144, 337–341 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

25.
Luo, Y. et al. Trends in antibiotic resistance genes occurrence in the Haihe River, China. Environ. Sci. Technol. 44, 7220–7225 (2010).
PubMed  Article  CAS  PubMed Central  Google Scholar 

26.
Guo, Y. et al. The antibiotic resistome of free-living and particle-attached bacteria under a reservoir cyanobacterial bloom. Environ. Int. 117, 107–115 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

27.
Song, H. et al. Allelopathic interactions of linoleic acid and nitric oxide increase the competitive ability of Microcystis aeruginosa. ISME J. 11, 1865–1876 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

28.
Zhang, Z. et al. Alteration of dominant cyanobacteria in different bloom periods caused by abiotic factors and species interactions. J. Environ. Sci. 99, 1–9 (2021).
Article  Google Scholar 

29.
Zhang, Q. et al. The fungicide azoxystrobin perturbs the gut microbiota community and enriches antibiotic resistance genes in Enchytraeus crypticus. Environ. Int. 131, 104965 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

30.
Zhu, D. et al. Antibiotics disturb the microbiome and increase the incidence of resistance genes in the gut of a common soil collembolan. Environ. Sci. Technol. 52, 3081–3090 (2018).
CAS  PubMed  Article  Google Scholar 

31.
Zhu, D. et al. Exposure of a soil collembolan to Ag nanoparticles and AgNO3 disturbs its associated microbiota and lowers the incidence of antibiotic resistance genes in the gut. Environ. Sci. Technol. 52, 12748–12756 (2018).
CAS  PubMed  Article  Google Scholar 

32.
Chen, Q. L. et al. Application of struvite alters the antibiotic resistome in soil, rhizosphere, and phyllosphere. Environ. Sci. Technol. 51, 8149–8157 (2017).
CAS  PubMed  Article  Google Scholar 

33.
Shi, K. et al. Long-term MODIS observations of cyanobacterial dynamics in Lake Taihu: Responses to nutrient enrichment and meteorological factors. Sci. Rep. 7, 40326 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

34.
Zhang, M. et al. Feedback regulation between aquatic microorganisms and the bloom-forming cyanobacterium Microcystis aeruginosa. Appl. Environ. Microbiol. 85, e01362–19 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

35.
Scheibner, M. V. et al. Impact of warming on phyto-bacterioplankton coupling and bacterial community composition in experimental mesocosms. Environ. Microbiol. 16, 718–733 (2014).
Article  Google Scholar 

36.
Woodhouse, J. N. et al. Microbial communities reflect temporal changes in cyanobacterial composition in a shallow ephemeral freshwater lake. ISME J. 10, 1337–1351 (2016).
CAS  PubMed  Article  Google Scholar 

37.
Martin, U. et al. PHI-base: a new interface and further additions for the multi-species pathogen–host interactions database. Nucleic Acids Res. 45, 604–610 (2016).
Google Scholar 

38.
Chen, H., Jing, L., Yao, Z., Meng, F. & Teng, Y. Prevalence, source and risk of antibiotic resistance genes in the sediments of Lake Tai (China) deciphered by metagenomic assembly: a comparison with other global lakes. Environ. Int. 127, 267–275 (2019).
CAS  PubMed  Article  Google Scholar 

39.
Zhang, Y., Sua, Y., Liu, Z., Yua, J. & Jina, M. Lipid biomarker evidence for determining the origin and distribution of organic matter in surface sediments of Lake Taihu, Eastern China. Ecol. Indic. 77, 397–408 (2017).
CAS  Article  Google Scholar 

40.
Jothikumar, N., Kahler, A., Strockbine, N., Gladney, L. & Hill, V. R. Draft genome sequence of Buttiauxella agrestis, isolated from surface water. Genome Announc. 2, e01060–14 (2014).
PubMed  PubMed Central  Google Scholar 

41.
Igbinosa, H. I. Antibiogram profiling and pathogenic status of Aeromonas species recovered from chicken. Saudi J. Biol. Sci. 21, 481–485 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

42.
Nguyen, H. N. K. et al. Molecular characterization of antibiotic resistance in Pseudomonas and Aeromonas isolates from catfish of the Mekong Delta, Vietnam. Vet. Microbiol. 171, 397–405 (2014).
CAS  PubMed  Article  Google Scholar 

43.
Gaze, W. H. et al. Impacts of anthropogenic activity on the ecology of class 1 integrons and integron-associated genes in the environment. ISME J. 5, 1253–1261 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

44.
Gillings, M. et al. The evolution of class 1 integrons and the rise of antibiotic resistance. J. Bacteriol. 190, 5095–5100 (2008).
CAS  PubMed  PubMed Central  Article  Google Scholar 

45.
Partridge, S. R., Tsafnat, G., Coiera, E. & Iredell, J. R. Gene cassettes and cassette arrays in mobile resistance integrons. FEMS Microbiol. Rev. 33, 757–784 (2009).
CAS  PubMed  Article  Google Scholar 

46.
Bahram, M. et al. Structure and function of the global topsoil microbiome. Nature 560, 233–237 (2018).
CAS  PubMed  Article  Google Scholar 

47.
Louati, I. et al. Correction: structural diversity of bacterial communities associated with bloom-forming freshwater cyanobacteria differs according to the cyanobacterial genus. PLoS ONE 10, e0140614 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

48.
Dias, E. et al. Assessing the antibiotic susceptibility of freshwater Cyanobacteria spp. Front. Microbiol. 6, 799 (2015).
PubMed  PubMed Central  Article  Google Scholar 

49.
Dias, E., Oliveira, M., Manageiro, V., Vasconcelos, V. & Caniça, M. Deciphering the role of cyanobacteria in water resistome: Hypothesis justifying the antibiotic resistance (phenotype and genotype) in Planktothrix genus. Sci. Total. Environ. 652, 447–454 (2018).
CAS  PubMed  Article  Google Scholar 

50.
Vaz-Moreira, I., Nunes, O. C. & Manaia, C. M. Bacterial diversity and antibiotic resistance in water habitats: searching the links with the human microbiome. FEMS Microbiol. Rev. 38, 761–778 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

51.
Cox, G. & Wright, G. D. Intrinsic antibiotic resistance: mechanisms, origins, challenges and solutions. Int. J. Med. Microbiol. 303, 287–292 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

52.
Myklestad, S. M. in Marine Chemistry. The Handbook of Environmental Chemistry. (eds Wangersky, P. J.) Vol. 5 Series: Water Pollution, vol 5D. 111–148 (Springer: Berlin, Heidelberg, Germany, 2000).

53.
Pancrace, C. et al. Rearranged biosynthetic gene cluster and synthesis of Hassallidin E in Planktothrix serta PCC 8927. ACS Chem. Biol. 12, 1796–1804 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

54.
Zhao, Y. et al. Feed additives shift gut microbiota and enrich antibiotic resistance in swine gut. Sci. Total. Environ. 621, 1224–1232 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

55.
Peng, S., Feng, Y., Wang, Y., Guo, X. & Lin, X. Prevalence of antibiotic resistance genes in soils after continually applied with different animal manure for 30 years. J. Hazard. Mater. 340, 16–25 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

56.
Szekeres, E. et al. Abundance of antibiotics, antibiotic resistance genes and bacterial community composition in wastewater effluents from different Romanian hospitals. Environ. Pollut. 225, 304–315 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

57.
Zhu, L. et al. Bacterial Communities associated with four cyanobacterial genera display structural and functional differences: evidence from an experimental approach. Front. Microbiol. 7, 1662 (2016).
PubMed  PubMed Central  Google Scholar 

58.
Dantas, G., Sommer, M. O. A., Oluwasegun, R. D. & Church, G. M. Bacteria subsisting on antibiotics. Science 320, 100–103 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

59.
D’Costa, V. M. et al. Antibiotic resistance is ancient. Nature 477, 457–461 (2011).
PubMed  Article  CAS  PubMed Central  Google Scholar 

60.
Sun, D. L., Jiang, X., Wu, Q. L. & Zhou, N. Y. Intragenomic heterogeneity of 16S rRNA genes causes overestimation of prokaryotic diversity. Appl. Environ. Microbiol. 79, 5962 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

61.
Ouyang, W. Y., Huang, F. Y., Zhao, Y., Li, H. & Su, J. Q. Increased levels of antibiotic resistance in urban stream of Jiulongjiang River, China. Appl. Microbiol. Biotechnol. 99, 5697–5707 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

62.
Qian, H. F. et al. Bio-safety assessment of validamycin formulation on bacterial and fungal biomass in soil monitored by real-time PCR. B. Environ. Contam. Tox. 78, 239–244 (2007).
CAS  Article  Google Scholar  More

1.
Diez, J. et al. Altitudinal upwards shifts in fungal fruiting in the Alps. Proc. R. Soc. B 287, 20192348. https://doi.org/10.1098/rspb.2019.2348 (2020).
Article  PubMed  Google Scholar 
2.
Gange, A. C. et al. Trait-dependent distributional shifts in fruiting of common British fungi. Ecography 41, 51–61. https://doi.org/10.1111/ecog.03233 (2018).
Article  Google Scholar 

3.
Boddy, L. et al. Climate variation effects on fungal fruiting. Fungal Ecol. 10, 20–33. https://doi.org/10.1016/j.funeco.2013.10.006 (2014).
Article  Google Scholar 

4.
Andrew, C. et al. Open-source data reveal how collections-based fungal diversity is sensitive to global change. Appl. Plant Sci. 7, e01227. https://doi.org/10.1002/aps3.1227 (2019).
Article  PubMed  PubMed Central  Google Scholar 

5.
Marx, D. H., Marrs, L. F. & Cordell, C. E. Practical use of the mycorrhizal fungal technology in forestry, reclamation, arboriculture, and horticulture. Dendrobiology 47, 27–40 (2002).
Google Scholar 

6.
Parmesan, C. & Yohe, G. A. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42. https://doi.org/10.1038/nature01286 (2003).
ADS  CAS  Article  Google Scholar 

7.
Fordham, D. A. et al. Plant extinction risk under climate change: are forecast range shifts alone a good indicator of species vulnerability to global warming?. Glob. Chang. Biol. 18, 1357–1371. https://doi.org/10.1111/j.1365-2486.2011.02614.x (2012).
ADS  Article  Google Scholar 

8.
Harrison, P., Berry, P. M., Butt, N. & New, M. Modelling climate change impacts on species’ distributions at the European scale: implications for conservation policy. Environ. Sci. Policy 9, 116–128. https://doi.org/10.1016/j.envsci.2005.11.003 (2006).
Article  Google Scholar 

9.
Guo, Y. et al. Prediction of the potential geographic distribution of the ectomycorrhizal mushroom Tricholoma matsutake under multiple climate change scenarios. Sci. Rep. 7, 46221. https://doi.org/10.1038/srep46221 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

10.
Araújo, M. B. & Peterson, A. T. Uses and misuses of bioclimatic envelope modeling. Ecology 93, 1527–1539. https://doi.org/10.1890/11-1930.1 (2012).
Article  PubMed  Google Scholar 

11.
Ehrlén, J. & Morris, W. F. Predicting changes in the distribution and abundance of species under environmental change. Ecol. Lett. 18, 303–314. https://doi.org/10.1111/ele.12410 (2015).
Article  PubMed  PubMed Central  Google Scholar 

12.
Chambers, D., Périé, C., Casajus, N. & de Blois, S. Challenges in modelling the abundance of 105 tree species in eastern North America using climate, edaphic, and topographic variables. For. Ecol. Manag. 291, 20–29. https://doi.org/10.1016/j.foreco.2012.10.046 (2013).
Article  Google Scholar 

13.
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026 (2006).
Article  Google Scholar 

14.
Anderson, R. P. A framework for using niche models to estimate impacts of climate change on species distributions. Ann. Ny. Acad. Sci. 1297, 8–28. https://doi.org/10.1111/nyas.12264 (2013).
ADS  Article  PubMed  Google Scholar 

15.
Berch, S. M. & Bonito, G. Cultivation in Mediterranean species of Tuber (Tuberaceae) in British Columbia, Canada. Mycorrhiza 24, 473–479; https://doi.org/10.1007/s00572-014-0562-y (2014).

16.
Păcurar, H. et al. Identification of Soils Factors Influence in the Distributions of Tuber aestivum in Transylvanian Subcarpathian Hills, Romania. Not. Bot. Horti. Afrobio. 47, 478–486; https://doi.org/10.15835/nbha47111378 (2019).

17.
Rellini, I., Pavarino, M., Scopesi, C. & Zotti, M. Physical land suitability map for Tuber magnatum Pico in Piana Crixia municipality territory (Liguria-Italy). J. Maps 7, 353–362. https://doi.org/10.4113/jom.2011.1180 (2012).
Article  Google Scholar 

18.
Serrano-Notivoli, R., Martín-Santafé, M., Sánchez, S. & Barriuso, J. J. Cultivation potentiality of black truffle in Zaragoza province (Northeast Spain). J. Maps 12, 994–998. https://doi.org/10.1080/17445647.2015.1113392 (2012).
Article  Google Scholar 

19.
Trappe, J. M. & Claridge, A. W. The hidden life of truffles. Sci. Am. 302, 78–84. https://doi.org/10.1038/scientificamerican0410-78 (2010).
Article  PubMed  Google Scholar 

20.
Stobbe, U. et al. Potential and limitations of Burgundy truffle cultivation. Appl. Microbiol. Biotechnol. 97, 5215–5224. https://doi.org/10.1007/s00253-013-4956-0 (2013).
CAS  Article  PubMed  Google Scholar 

21.
Stobbe, U. et al. Spatial distribution and ecological variation of re-discovered German truffle habitats. Fungal Ecol. 5, 591–599. https://doi.org/10.1016/j.funeco.2012.02.001 (2012).
Article  Google Scholar 

22.
Delmas, J. Tuber spp. in The biology and cultivation of edible mushrooms (eds. Chang, S. T. & Hayes, W. A.) 645–681 (Academic Press, 1978).

23.
Reyna, S. & Garcia-Barreda, S. Black truffle cultivation: a global reality. Forest Syst. 23, 317–328. https://doi.org/10.5424/fs/2014232-04771 (2014).
Article  Google Scholar 

24.
Thomas, P. & Büntgen, U. First harvest of Périgord black truffle in the UK as a result of climate change. Clim. Res. 74, 67–70. https://doi.org/10.3354/cr01494 (2017).
Article  Google Scholar 

25.
Büntgen, U. et al. Truffles on the move. Front. Ecol. Environ. 17, 200–202. https://doi.org/10.1002/fee.2033 (2019).
Article  Google Scholar 

26.
Büntgen, U. et al. Drought-induced decline in Mediterranean truffle harvest. Nat. Clim. Chang. 2, 827–829. https://doi.org/10.1038/nclimate1733 (2012).
ADS  Article  Google Scholar 

27.
Thomas, P. & Büntgen, U. A risk assessment of Europe’s black truffle sector under predicted climate change. Sci. Total Environ. 655, 27–34. https://doi.org/10.1016/j.scitotenv.2018.11.252 (2019).
ADS  CAS  Article  PubMed  Google Scholar 

28.
Bonet, J. A. et al. Cultivation Methods of the Black Truffle, the Most Profitable Mediterranean Non-Wood Forest Product; A State of the Art Review. in Modelling, Valuing and Managing Mediterranean Forest Ecosystems for Non-Timber Goods and Services (eds. Palahí, M., Birot, Y., Bravo, F., & Gorriz, E.) 57–71 (European Forest Institute, 2009).

29.
Bonet, J. A., Fisher, C. R. & Colinas, C. Cultivation of black truffle to promote reforestation and land-use stability. Agron. Sustain. Dev. 26, 69–76. https://doi.org/10.1051/agro:2005059 (2006).
Article  Google Scholar 

30.
Büntgen, U., Latorre, J., Egli, S. & Martínez-Peña, F. Socio-economic, scientific, and political benefits of mycotourism. Ecosphere 8, e01870. https://doi.org/10.1002/ecs2.1870 (2017).
Article  Google Scholar 

31.
Chevalier, G. & Frochot, H. Ecology and possibility of culture in Europe of the Burgundy truffle (Tuber uncinatum Chatin). Agric. Ecosyst. Environ. 28, 71–73. https://doi.org/10.1016/0167-8809(90)90016-7 (1989).
Article  Google Scholar 

32.
Chevalier, G. The Truffle of Europe (Tuber aestivum): geographic limits, ecology and possibility of cultivation. Österr. Z. Pilzk. 19, 249–259 (2010).
Google Scholar 

33.
Chevalier, G. Europe, a continent with high potential for the cultivation of the Burgundy truffle (Tuber aestivum/uncinatum). Acta Mycol. 47, 127–132 (2012).
Article  Google Scholar 

34.
Chytrý, M. Flora and Vegetation of the Czech Republic, Plant and Vegetation 14. (Springer, 2017).

35.
Rivas-Martínez, S. Bioclimatic & Biogeographic Maps of Europe. (University of León, 2004).

36.
Trnka, M. et al. Soil moisture trends in the Czech Republic between 1961 and 2012. Int. J. Climatol. 35, 3733–3747. https://doi.org/10.1002/joc.4242 (2015).
Article  Google Scholar 

37.
Ji, D. et al. Description and basic evaluation of Beijing Normal University Earth System Model (BNU-ESM) version 1. Geosci. Model. Dev. 7, 2039–2064. https://doi.org/10.5194/gmd-7-2039-2014 (2014).
ADS  Article  Google Scholar 

38.
Voldoire, A. et al. The CNRM-CM5.1 global climate model: description and basic evaluation. Clim. Dyn. 40, 2091–2121. https://doi.org/10.1007/s00382-011-1259-y (2013).
Article  Google Scholar 

39.
Martin, G. M. et al. The HadGEM2 family of met office unified model climate configurations. Geosci. Model Dev. 4, 723–757. https://doi.org/10.5194/gmd-4-723-2011 (2011).
ADS  Article  Google Scholar 

40.
Dufresne, J.-L. et al. Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5. Clim. Dyn. 40, 2123–2165. https://doi.org/10.1007/s00382-012-1636-1 (2013).
Article  Google Scholar 

41.
Yukimoto, S. et al. A new global climate model of the meteorological research institute: MRI-CGCM3. J. Meteorol. Soc. Jpn. 90A, 23–64 (2012).
Article  Google Scholar 

42.
Dubrovský, M., Trnka, M., Holman, I. P., Svobodová, E. & Harrison, P. Developing a reduced-form ensemble of climate change scenarios for Europe and its application to selected impact indicators. Clim. Change 128, 169–186. https://doi.org/10.1007/s10584-014-1297-7 (2015).
ADS  Article  Google Scholar 

43.
Hay, L. E., Wilby, R. L. & Leavesley, G. H. A comparison of delta change and downscaled GCM scenarios for three mountainous basins in the United States. J. Am. Water Resour. Assoc. 36, 387–397. https://doi.org/10.1111/j.1752-1688.2000.tb04276.x (2007).
Article  Google Scholar 

44.
Plíva, K. Typologický systém ÚHUL. (Forest Management Institute, 1971).

45.
Novotný, I. Metodika mapování a aktualizace bonitovaných půdně ekologických jednotek. (Research Institute for Soil and Water Conservation, 2013).

46.
ESRI. ArcGIS Pro: Release 2.3.0. (Environmental Systems Research Institute, 2019).

47.
ArcData Praha. ArcČR 500, Version 3.3. (ArcData Praha, 2016).

48.
Malczewski, J. GIS and multicriteria decision analysis. (John Wiley, 1999).

49.
Jaillard, B. et al. Soil Characteristics of Tuber melanosporum Habitat. in True Truffle (Tuber spp.) in the World (eds. Zambonelli, A., Iotti, M. & Murat, C.) 169–190 (Springer International Publishing, 2016).

50.
Büntgen, U. et al. New insights into the complex relationship between weight and maturity of Burgundy Truffles (Tuber aestivum). PLoS ONE 12, e0170375. https://doi.org/10.1371/journal.pone.0170375 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

51.
Garcia-Barreda, S., Camarero, J. J., Vicente-Serrano, S. M. & Serrano-Notivoli, R. Variability and trends of black truffle production in Spain (1970–2017): Linkages to climate, host growth, and human factors. Agric. For. Meteorol. 287, 107951. https://doi.org/10.1016/j.agrformet.2020.107951 (2020).
ADS  Article  Google Scholar 

52.
Le Tacon, F. et al. Climatic variations explain annual fluctuations in French Périgord black truffle wholesale markets but do not explain the decrease in black truffle production over the last 48 years. Mycorrhiza 24(Suppl 1), S115–S125. https://doi.org/10.1007/s00572-014-0568-5 (2014).
Article  PubMed  Google Scholar 

53.
Václavík, T., Kanaskie, A., Hansen, E. M., Ohmann, J. L. & Meentemeyer, R. K. Predicting potential and actual distribution of sudden oak death in Oregon: Prioritizing landscape contexts for early detection and eradication of disease outbreaks. For. Ecol. Manag. 260, 1026–1035. https://doi.org/10.1016/j.foreco.2010.06.026 (2010).
Article  Google Scholar 

54.
Streiblová, E., Gryndlerová, H., Valda, S. & Gryndler, M. Tuber aestivum: hypogeous fungus neglected in the Czech Republic: a review. Czech Mycol. 61, 163–173 (2010).
Article  Google Scholar 

55.
Gryndler, M. et al. Detection of summer truffle (Tuber aestivum Vittad) in ectomycorrhizae and soil using specific primers. FEMS Microbiol. Lett. 318, 84–91. https://doi.org/10.1111/j.1574-6968.2011.02243.x (2011).
CAS  Article  PubMed  Google Scholar 

56.
Gryndler, M. et al. Truffle biogeography: a case study revealing ecological niche separation of different Tuber species. Ecol. Evol. 7, 4275–4288. https://doi.org/10.1002/ece3.3017 (2017).
Article  PubMed  PubMed Central  Google Scholar 

57.
Sánchez, S., Ágreda, T., Martín, M., de Miguel, A. M. & Barriuso, J. Persistence and detection of black truffle ectomycorrhizas in plantations: comparison between two field detection methods. Mycorrhiza 24, 39–46. https://doi.org/10.1007/s00572-014-0560-0 (2014).
Article  Google Scholar 

58.
Hilszczańska, D., Sierota, Z. & Palenzona, M. New Tuber species found in Poland. Mycorrhiza 18, 223–226. https://doi.org/10.1007/s00572-008-0175-4 (2008).
Article  PubMed  Google Scholar 

59.
Trnka, M. et al. Expected changes in agroclimatic conditions in Central Europe. Clim. Change 108, 261–289. https://doi.org/10.1007/s10584-011-0025-9 (2011).
ADS  Article  Google Scholar 

60.
Büntgen, U. et al. Black truffle winter production depends on Mediterranean summer precipitation. Environ. Res. Lett. 14, 074004. https://doi.org/10.1088/1748-9326/ab1880 (2019).
ADS  CAS  Article  Google Scholar 

61.
Le Tacon, F., Delmas, J., Gleyze, R. & Bouchard, D. Influence du regime hydrique du sol et de la fertilisation sur la frutification de la truffe noire du Périgord (Tuber melanosporum Vitt.) dans le sud-est de la France. Acta Oecol-Oec. Appl. 3, 291–306 (1982).

62.
Trnka, M. et al. Assessing the combined hazards of drought, soil erosion and local flooding on agricultural land: a Czech case study. Clim. Res. 70, 231–249. https://doi.org/10.3354/cr01421 (2016).
Article  Google Scholar 

63.
European Environment Agency. Climate change adaptation in the agriculture sector in Europe. (European Environment Agency, 2019).

64.
NCA CR. Species database of nature protection. (Nature Conservation Agency of the Czech Republic, 2019).

65.
San-Miguel-Ayanz, J. European Atlas of Forest Tree Species (Publication Office of the European Union, 2016). More

1.
Bronstein, J., Dieckmann, U. & Ferrière, R. In Evolutionary Conservation Biology (eds Ferrière, R., Dieckmann, U., & Couvet, D.) (Cambridge University Press, 2004).
2.
Bronstein, J. L. Mutualism (Oxford University Press, Oxford, 2015).
Google Scholar 

3.
Herrera, C. M. Variation in mutualisms: the spatiotemporal mosaic of a pollinator assemblage. Biol. J. Linn. Soc. 35, 95–125 (1988).
Article  Google Scholar 

4.
Billick, I. & Tonkel, K. The relative importance of spatial vs. temporal variability in generating a conditional mutualism. Ecology 84, 289–295. https://doi.org/10.1890/0012-9658(2003)084[0289:Triosv]2.0.Co;2 (2003).
Article  Google Scholar 

5.
Hoeksema, J. & Bruna, E. In Mutualism (ed Bronstein, J. L.) 181–202 (Oxford University Press, 2015).

6.
Chamberlain, S. A., Bronstein, J. L. & Rudgers, J. A. How context dependent are species interactions?. Ecol. Lett. 17, 881–890 (2014).
Article  Google Scholar 

7.
Hoeksema, J. D. et al. A meta-analysis of context-dependency in plant response to inoculation with mycorrhizal fungi. Ecol. Lett. 13, 394–407 (2010).
Article  Google Scholar 

8.
Chomicki, G., Weber, M., Antonelli, A., Bascompte, J. & Kiers, E. T. The impact of mutualisms on species richness. Trends. Ecol. Evol. 34, 698–711 (2019).
Article  Google Scholar 

9.
Dirzo, R. et al. Defaunation in the anthropocene. Science 345, 401–406 (2014).
ADS  CAS  Article  Google Scholar 

10.
Holland, J. N., Ness, J. H., Boyle, A. & Bronstein, J. L. In Ecology of Predator–Prey Interactions (eds Barbosa, P. & Castellanos, I.) 17–33 (Oxford University Press, 2005).

11.
Landry, C. Mighty Mutualisms: the nature of plant-pollinator interactions. Nat. Educ. Knowl. 3, 37 (2012).
Google Scholar 

12.
Arnal, C., Côté, I. M. & Morand, S. Why clean and be cleaned? The importance of client ectoparasites and mucus in a marine cleaning symbiosis. Behav. Ecol. Sociobiol. 51, 1–7. https://doi.org/10.1007/s002650100407 (2001).
Article  Google Scholar 

13.
Breton, L. M. & Addicott, J. F. Density-dependent mutualism in an aphid-ant interaction. Ecology 73, 2175–2180 (1992).
Article  Google Scholar 

14.
Sazima, C., Guimarães, P. R., Dos Reis, S. F. & Sazima, I. What makes a species central in a cleaning mutualism network?. Oikos 119, 1319–1325. https://doi.org/10.1111/j.1600-0706.2009.18222.x (2010).
Article  Google Scholar 

15.
Noë, R. In Economics in Nature: Social Dilemmas, Mate Choice and Biological Markets (eds Noë, R. & Hammerstein, P.) 93–118 (Cambridge University Press, 2001).

16.
Palmer, T., Pringle, E., Stier, A. & Holt, R. In Mutualism Vol. 159 (ed Bronstein, J. L.) 159–180 (Oxford University Press, 2015).

17.
Bronstein, J. L. Our current understanding of mutualism. Q. Rev. Biol. 69, 31–51. https://doi.org/10.1086/418432 (1994).
Article  Google Scholar 

18.
Dunkley, K., Ioannou, C. C., Whittey, K. E., Cable, J. & Perkins, S. E. Cleaner personality and client identity have joint consequences on cleaning interaction dynamics. Behav. Ecol. 30, 703–712. https://doi.org/10.1093/beheco/arz007 (2019).
Article  PubMed  PubMed Central  Google Scholar 

19.
Feder, H. M. Cleaning symbiosis in the marine environment. Symbiosis 1, 327–380 (1966).
Google Scholar 

20.
Dunkley, K. et al. Long-term cleaning patterns of the sharknose goby (Elacatinus evelynae). Coral Reefs 38, 1–10 (2019).
Article  Google Scholar 

21.
Floeter, S. R., Vazquez, D. P. & Grutter, A. S. The macroecology of marine cleaning mutualisms. J. Anim. Ecol. 76, 105–111. https://doi.org/10.1111/j.1365-2656.2006.01178.x (2007).
Article  PubMed  Google Scholar 

22.
Whiteman, E. A. & Côté, I. M. Cleaning activity of two Caribbean cleaning gobies: intra- and interspecific comparisons. J. Fish Biol. 60, 1443–1458. https://doi.org/10.1006/jfbi.2002.1947 (2002).
Article  Google Scholar 

23.
Côté, I. M., Arnal, C. & Reynolds, J. D. Variation in posing behaviour among fish species visiting cleaning stations. J. Fish Biol. 53, 256–266. https://doi.org/10.1111/j.1095-8649.1998.tb01031.x (1998).
Article  Google Scholar 

24.
Bshary, R. & Schäffer, D. Choosy reef fish select cleaner fish that provide high-quality service. Anim. Behav. 63, 557–564. https://doi.org/10.1006/anbe.2001.1923 (2002).
Article  Google Scholar 

25.
Axelrod, R. & Hamilton, W. D. The evolution of cooperation. Science 211, 1390–1396 (1981).
ADS  MathSciNet  CAS  Article  Google Scholar 

26.
Bronstein, J. L. The exploitation of mutualisms. Ecol. Lett. 4, 277–287 (2001).
Article  Google Scholar 

27.
Eckes, M., Dove, S., Siebeck, U. E. & Grutter, A. S. Fish mucus versus parasitic gnathiid isopods as sources of energy and sunscreens for a cleaner fish. Coral Reefs 34, 823–833. https://doi.org/10.1007/s00338-015-1313-z (2015).
ADS  Article  Google Scholar 

28.
Gonzalez-Teuber, M. & Heil, M. The role of extrafloral nectar amino acids for the preferences of facultative and obligate ant mutualists. J. Chem. Ecol. 35, 459–468. https://doi.org/10.1007/s10886-009-9618-4 (2009).
CAS  Article  PubMed  Google Scholar 

29.
Grutter, A. S. Spatial and temporal variations of the ectoparasites of seven reef fish species from Lizard Island and Heron Island, Australia. Mar. Ecol. Prog. Ser. 115, 21–30 (1994).
ADS  Article  Google Scholar 

30.
Poulin, R. & Rohde, K. Comparing the richness of metazoan ectoparasite communities of marine fishes: controlling for host phylogeny. Oecologia 110, 278–283. https://doi.org/10.1007/s004420050160 (1997).
ADS  Article  PubMed  Google Scholar 

31.
Patterson, J. E. & Ruckstuhl, K. E. Parasite infection and host group size: a meta-analytical review. Parasitology 140, 803–813. https://doi.org/10.1017/S0031182012002259 (2013).
Article  PubMed  PubMed Central  Google Scholar 

32.
Bronstein, J. L. & Barbosa, P. In Multitrophic level interactions (eds Tscharntke, T. & Hawkins, B. A.) 44–65 (Cambridge University Press, 2002).

33.
Hoeksema, J. D. & Bruna, E. M. Pursuing the big questions about interspecific mutualism: a review of theoretical approaches. Oecologia 125, 321–330. https://doi.org/10.1007/s004420000496 (2000).
ADS  Article  PubMed  Google Scholar 

34.
Waser, N. M., Chittka, L., Price, M. V., Williams, N. M. & Ollerton, J. Generalization in pollination systems, and why it matters. Ecology 77, 1043–1060 (1996).
Article  Google Scholar 

35.
Arnal, C., Côté, I. M., Sasal, P. & Morand, S. Cleaner-client interactions on a Caribbean reef: influence of correlates of parasitism. Behav. Ecol. Sociobiol. 47, 353–358. https://doi.org/10.1007/s002650050676 (2000).
Article  Google Scholar 

36.
Wilson, A. D. M., Krause, J., Herbert-Read, J. E. & Ward, A. J. W. The personality behind cheating: behavioural types and the feeding ecology of cleaner fish. Ethology 120, 904–912. https://doi.org/10.1111/eth.12262 (2014).
Article  Google Scholar 

37.
Dunkley, K., Cable, J. & Perkins, S. E. The selective cleaning behaviour of juvenile blue-headed wrasse (Thalassoma bifasciatum) in the Caribbean. Behav. Process. 147, 5–12. https://doi.org/10.1016/j.beproc.2017.12.005 (2018).
Article  Google Scholar 

38.
Triki, Z. et al. Biological market effects predict cleaner fish strategic sophistication. Behav. Ecol. 30, 1548–1557 (2019).
Article  Google Scholar 

39.
Cheney, K. L. & Côté, I. M. Do ectoparasites determine cleaner fish abundance? Evidence on two spatial scales. Mar. Ecol. Prog. Ser. 263, 189–196 (2003).
ADS  Article  Google Scholar 

40.
Lo, C. M., Morand, S. & Galzin, R. Parasite diversityhost age and size relationship in three coral-reef fishes from French Polynesia. Int. J. Parasitol. 28, 1695–1708 (1998).
CAS  Article  Google Scholar 

41.
Palmer, T. M. et al. Breakdown of an ant-plant mutualism follows the loss of large herbivores from an African savanna. Science 319, 192–195. https://doi.org/10.1126/science.1151579 (2008).
ADS  CAS  Article  PubMed  Google Scholar 

42.
Toby, K. E., Palmer, T. M., Ives, A. R., Bruno, J. F. & Bronstein, J. L. Mutualisms in a changing world: an evolutionary perspective. Ecol. Lett. 13, 1459–1474. https://doi.org/10.1111/j.1461-0248.2010.01538.x (2010).
Article  Google Scholar 

43.
Chomicki, G. & Renner, S. S. Partner abundance controls mutualism stability and the pace of morphological change over geologic time. Proc. Natl. Acad. Sci. USA 114, 3951–3956. https://doi.org/10.1073/pnas.1616837114 (2017).
CAS  Article  PubMed  Google Scholar 

44.
Werner, E. E. Individual behavior and higher-order species interactions. Am. Nat. 140, S5–S32. https://doi.org/10.1086/285395 (1992).
Article  Google Scholar 

45.
Chamberlain, S. A. & Holland, J. N. Quantitative synthesis of context dependency in ant–plant protection mutualisms. Ecology 90, 2384–2392 (2009).
Article  Google Scholar 

46.
Bartomeus, I. Understanding linkage rules in plant-pollinator networks by using hierarchical models that incorporate pollinator detectability and plant traits. PLoS ONE 8, e69200 (2013).
ADS  CAS  Article  Google Scholar 

47.
Heath, K. D. & Stinchcombe, J. R. Explaining mutualism variation: a new evolutionary paradox?. Evolution 68, 309–317 (2014).
Article  Google Scholar 

48.
Lester, R. J. & McVinish, R. Does moving up a food chain increase aggregation in parasites?. J. R. Soc. Interface 13, 20160102. https://doi.org/10.1098/rsif.2016.0102 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

49.
Blanchet, S., Rey, O. & Loot, G. Evidence for host variation in parasite tolerance in a wild fish population. Evol. Ecol. 24, 1129–1139 (2010).
Article  Google Scholar 

50.
Rohde, K., Hayward, C. & Heap, M. Aspects of the ecology of metazoan ectoparasites of marine fishes. Int. J. Parasitol. 25, 945–970 (1995).
CAS  Article  Google Scholar 

51.
Toscano, B. J., Gownaris, N. J., Heerhartz, S. M. & Monaco, C. J. Personality, foraging behavior and specialization: integrating behavioral and food web ecology at the individual level. Oecologia 182, 55–69. https://doi.org/10.1007/s00442-016-3648-8 (2016).
ADS  Article  PubMed  Google Scholar 

52.
Batstone, R. T., Carscadden, K. A., Afkhami, M. E. & Frederickson, M. E. Using niche breadth theory to explain generalization in mutualisms. Ecology 99, 1039–1050 (2018).
Article  Google Scholar 

53.
White, J. W., Grigsby, C. J. & Warner, R. R. Cleaning behavior is riskier and less profitable than an alternative strategy for a facultative cleaner fish. Coral Reefs 26, 87–94. https://doi.org/10.1007/s00338-006-0161-2 (2007).
Article  Google Scholar 

54.
Barker, J. L. & Bronstein, J. L. Temporal structure in cooperative interactions: what does the timing of exploitation tell us about its cost?. PLoS Biol. 14, e1002371 (2016).
Article  Google Scholar 

55.
Vannette, R. L., Gauthier, M.-P.L. & Fukami, T. Nectar bacteria, but not yeast, weaken a plant–pollinator mutualism. Proc. R. Soc. B 280, 20122601 (2013).
Article  Google Scholar 

56.
Strauss, S. Y. Floral characters link herbivores, pollinators, and plant fitness. Ecology 78, 1640–1645 (1997).
Article  Google Scholar 

57.
Heath, K. D. & Lau, J. A. Herbivores alter the fitness benefits of a plant–rhizobium mutualism. Acta Oecol. 37, 87–92 (2011).
ADS  Article  Google Scholar 

58.
Ferrari, R. et al. Habitat structural complexity metrics improve predictions of fish abundance and distribution. Ecography 41, 1077–1091 (2018).
Article  Google Scholar 

59.
Ferreira, C. E., Goncçalves, J. E. & Coutinho, R. Community structure of fishes and habitat complexity on a tropical rocky shore. Environ. Biol. Fish 61, 353–369 (2001).
Article  Google Scholar 

60.
Humann, P. & Deloach, N. Reef Fish Identification (New World Publications, Jacksonville, 2014).
Google Scholar 

61.
Froese, R. & Pauly, D. FishBase. www.fishbase.org (2018).

62.
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.8637/jss.v067.i01 (2015).
Article  Google Scholar 

63.
R Core Team. R: A Language and Environment for Statistical Computing. http://www.R-project.org (2017).

64.
Harrison, X. A. et al. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ 6, e4794 (2018).
Article  Google Scholar 

65.
Bolker, B. M. et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends. Ecol. Evol. 24, 127–135. https://doi.org/10.1016/j.tree.2008.10.008 (2009).
Article  PubMed  Google Scholar 

66.
Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, Berlin, 2009).
Google Scholar  More

1.
Myhre, G. et al. Anthropogenic and natural radiative forcing. In Climate change 2013: The physical science basis; Working Group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change (ed. Stocker, T.) 659–740 (Cambridge University Press, Cambridge, 2014).
Google Scholar 
2.
Ravishankara, A. R., Daniel, J. S. & Portmann, R. W. Nitrous oxide (N2O): The dominant ozone-depleting substance emitted in the 21st century. Science 326, 123–125 (2009).
ADS  CAS  PubMed  Article  Google Scholar 

3.
Thomson, A. J., Giannopoulos, G., Pretty, J., Baggs, E. M. & Richardson, D. J. Biological sources and sinks of nitrous oxide and strategies to mitigate emissions. Philos. Trans. R. Soc. B Biol. Sci. 367(1593), 1157–1168 (2012).
CAS  Article  Google Scholar 

4.
NoAA, ESLR (2019). https://www.esrl.noaa.gov/gmd/hats/insitu/cats/conc.php?site=brw&gas=n2o. Accessed on 01 May 2019.

5.
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  Article  Google Scholar 

6.
Davidson, E. A. The contribution of manure and fertilizer nitrogen to atmospheric nitrous oxide since 1860. Nat. Geosci. 2, 659–662 (2009).
ADS  CAS  Article  Google Scholar 

7.
Hu, H., Chen, D. & He, J. Microbial regulation of terrestrial nitrous oxide formation: Understanding the biological pathways for prediction of emission rates. FEMS Microbiol. Rev. 39(5), 729–749 (2015).
CAS  PubMed  Article  Google Scholar 

8.
Zhou, M., Butterbach-Bahl, K., Vereecken, H. & Brüggemann, N. A meta-analysis of soil salinization effects on nitrogen pools, cycles and fluxes in coastal ecosystems. Glob. Chang. Biol. 23, 1338–1352 (2017).
ADS  PubMed  Article  Google Scholar 

9.
Inubushi, K., Barahona, M. A. & Yamakawa, K. Effects of salts and moisture content on N2O emission and nitrogen dynamics in Yellow soil and Andosol in model experiments. Biol. Fertil. Soils 29, 401–407 (1999).
CAS  Article  Google Scholar 

10.
Yang, W., Yang, M., Wen, H. & Jiao, Y. Global warming potential of CH4 uptake and N2O emissions in saline–alkaline soils. Atmos. Environ. 191, 172–180 (2018).
ADS  CAS  Article  Google Scholar 

11.
Aliyu, G. et al. A meta-analysis of soil background N2O emissions from croplands in China shows variation among climatic zones. Agric. Ecosyst. Environ. 267, 63–73 (2018).
CAS  Article  Google Scholar 

12.
Wang, Y. et al. Soil pH as the chief modifier for regional nitrous oxide emissions: New evidence and implications for global estimates and mitigation. Glob. Chang. Biol. 24, 617–626 (2018).
Article  Google Scholar 

13.
Bouwman, A. F., Boumans, L. J. M. & Batjes, N. H. Estimation of global NH3 volatilization loss from synthetic fertilizers and animal manure applied to arable lands and grasslands. Glob. Biogeochem. Cycles 16, 81–814 (2002).
Google Scholar 

14.
Allen, D. E. et al. Nitrous oxide and methane emissions from soil are reduced following afforestation of pasture lands in three contrasting climatic zones. Soil Res. 47, 443 (2009).
ADS  CAS  Article  Google Scholar 

15.
Pendall, E. et al. Land use and season affect fluxes of CO2, CH4, CO, N2O, H2 and isotopic source signatures in Panama: Evidence from nocturnal boundary layer profiles. Glob. Chang. Biol. 16, 2721–2736 (2010).
ADS  Article  Google Scholar 

16.
Van Lent, J., Hergoualc, H. K. & Verchot, L. V. Reviews and syntheses: 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  Article  Google Scholar 

17.
Benanti, G., Saunders, M., Tobin, B. & Osborne, B. Contrasting impacts of afforestation on nitrous oxide and methane emissions. Agric. For. Meteorol. 198–199, 82–93 (2014).
ADS  Article  Google Scholar 

18.
de Godoi, S. G. et al. The conversion of grassland to acacia forest as an effective option for net reduction in greenhouse gas emissions. J. Environ. Manag. 169, 91–102 (2016).
Article  CAS  Google Scholar 

19.
Zona, D. et al. Fluxes of the greenhouse gases (CO2, CH4 and N2O) above a short-rotation poplar plantation after conversion from agricultural land. Agric. For. Meteorol. 169, 100–110 (2013).
ADS  Article  Google Scholar 

20.
Li, C., Di, H. J., Cameron, K. C., Podolyan, A. & Zhu, B. Effect of different land use and land use change on ammonia oxidiser abundance and N2O emissions. Soil Biol. Biochem. 96, 169–175 (2016).
CAS  Article  Google Scholar 

21.
Ussiri, D. & Lal, R. Soil Emission of Nitrous Oxide and Its Mitigation (Springer, Netherlands, 2013).
Google Scholar 

22.
Butterbach-Bahl, K. et al. Nitrous oxide emissions from soils: How well do we understand the processes and their controls?. Philos Trans. R. Soc. B Biol. Sci. 368, 1621. https://doi.org/10.1098/rstb.2013.0122 (2013).
CAS  Article  Google Scholar 

23.
Sarauer, J. L. & Coleman, M. D. Converting conventional agriculture to poplar bioenergy crops: Soil greenhouse gas flux. Scand. J. For. Res. 33, 781–792 (2018).
Article  Google Scholar 

24.
Denk, T. R. A. et al. The nitrogen cycle: A review of isotope effects and isotope modeling approaches. Soil Biol. Biochem. 105, 121–137 (2017).
CAS  Article  Google Scholar 

25.
Park, S. et al. Can N2O stable isotopes and isotopomers be useful tools to characterize sources and microbial pathways of N2O production and consumption in tropical soils ?. Glob. Biogeochem. Cycles 25, 1–16 (2011).
CAS  Article  Google Scholar 

26.
Koba, K. et al. The 15N natural abundance of the N lost from an N-saturated subtropical forest in southern China. J. Geophys. Res. Biogeosci. 117, G2. https://doi.org/10.1029/2010JG001615 (2012).
CAS  Article  Google Scholar 

27.
Gil, J., Pérez, T., Boering, K., Martikainen, P. J. & Biasi, C. Mechanisms responsible for high N2O emissions from subarctic permafrost peatlands studied via stable isotope techniques. Glob. Biogeochem. Cycles 31, 172–189 (2017).
CAS  Article  Google Scholar 

28.
Pérez, T. et al. Identifying the agricultural imprint on the global N2O budget using stable isotopes. J. Geophys. Res. Atmos. 106, 9869–9878 (2001).
ADS  Article  Google Scholar 

29.
Conen, F. & Neftel, Æ. A. Do increasingly depleted d 15N values of atmospheric N2O indicate a decline in soil N2O reduction ?. Biogeochemistry 82(3), 321–326 (2007).
CAS  Article  Google Scholar 

30.
Wada, E. & Ueda, S. Carbon, nitrogen, and oxygen isotope ratios of CH4 and N2O in soil ecosystems. In Mass Spectrometry of Soils (eds Boutton, T. W. & Yamaski, S. I.) 177–203 (Marchel Dekker, New York, 1996).
Google Scholar 

31.
Lambin, E. F. & Meyfroidt, P. Global land use change, economic globalization, and the looming land scarcity. Proc. Natl. Acad. Sci. USA 108, 3465–3472 (2011).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

32.
Shi, Z., Wang, R., Huang, M. X. & Landgraf, D. Detection of coastal saline land uses with multi-temporal landsat images in Shangyu City, China. Environ. Manag. 30, 142–150 (2002).
Article  Google Scholar 

33.
Manning, M. et al. The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, Cambridge, 2007).
Google Scholar 

34.
Cui, B., Yang, Q., Zhang, K., Zhao, X. & You, Z. Responses of saltcedar (Tamarix chinensis) to water table depth and soil salinity in the Yellow River Delta, China. Plant Ecol. 209, 279–290 (2010).
Article  Google Scholar 

35.
Feng, X. et al. Spatiotemporal heterogeneity of soil water and salinity after establishment of dense-foliage Tamarix chinensis on coastal saline land. Ecol. Eng. 121, 104–113 (2018).
Article  Google Scholar 

36.
Zhang, L. L. et al. Seasonal dynamics in nitrous oxide emissions under different types of vegetation in saline–alkaline soils of the Yellow River Delta, China and implications for eco-restoring coastal wetland. Ecol. Eng. 61, 82–89 (2013).
ADS  Article  Google Scholar 

37.
Abalos, D., van Groenigen, J. W. & De Deyn, G. B. What plant functional traits can reduce nitrous oxide emissions from intensively managed grasslands?. Glob. Chang. Biol. 24, 248–258 (2018).
Article  Google Scholar 

38.
Ju, Z., Du, Z., Guo, K. & Liu, X. Irrigation with freezing saline water for 6 years alters salt ion distribution within soil aggregates. J. Soils Sedim. 19, 97–105 (2019).
CAS  Article  Google Scholar 

39.
Li, F. et al. Impact of rice-fish/shrimp co-culture on the N2O emission and NH3 volatilization in intensive aquaculture ponds. Sci. Total Environ. 655, 284–291 (2019).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
Snider, D., Thompson, K., Wagner-Riddle, C., Spoelstra, J. & Dunfield, K. Molecular techniques and stable isotope ratios at natural abundance give complementary inferences about N2O production pathways in an agricultural soil following a rainfall event. Soil Biol. Biochem. 88, 197–213 (2015).
CAS  Article  Google Scholar 

41.
Dong, W. H., Zhang, S., Rao, X. & Liu, C.-A. Newly-reclaimed alfalfa forage land improved soil properties comparison to farmland in wheat–maize cropping systems at the margins of oases. Ecol. Eng. 94, 57–64 (2016).
Article  Google Scholar 

42.
Livesley, S. J. et al. Soil–atmosphere exchange of greenhouse gases in a Eucalyptus marginata woodland, a clover-grass pasture, and Pinus radiata and Eucalyptus globulus plantations. Glob. Chang. Biol. 15, 425–440 (2009).
ADS  Article  Google Scholar 

43.
Lin, S. et al. Differences in nitrous oxide fluxes from red soil under different land uses in mid-subtropical China. Agric. Ecosyst. Environ. 146, 168–178 (2012).
ADS  CAS  Article  Google Scholar 

44.
Song, A. et al. Substrate-driven microbial response: A novel mechanism contributes significantly to temperature sensitivity of N2O emissions in upland arable soil. Soil Biol. Biochem. 118, 18–26 (2018).
CAS  Article  Google Scholar 

45.
Huang, X. et al. A flexible Bayesian model for describing temporal variability of N2O emissions from an Australian pasture. Sci. Total Environ. 454, 206–210 (2013).
ADS  PubMed  Article  CAS  Google Scholar 

46.
Nan, W. et al. Characteristics of N2O production and transport within soil profiles subjected to different nitrogen application rates in China. Sci. Total Environ. 542, 864–875 (2016).
ADS  CAS  PubMed  Article  Google Scholar 

47.
Chaddy, A., Melling, L., Ishikura, K. & Hatano, R. Soil N2O emissions under different N rates in an oil palm plantation on tropical peatland. Agriculture 9, 213. https://doi.org/10.3390/agriculture9100213 (2019).
CAS  Article  Google Scholar 

48.
Davidson, E. A. Sources of nitric oxide and nitrous oxide following wetting of dry soil. Soil Sci. Soc. Am. J. 56, 95–102 (1992).
ADS  CAS  Article  Google Scholar 

49.
Kazuya, N. et al. Evaluation of uncertinities in N2O and NO fluxes from agricultural soil using a hierarchical bayesian model. J Geophys Res. 117, G04008. https://doi.org/10.1029/2012JG002157 (2012).
ADS  CAS  Article  Google Scholar 

50.
Sakata, R. et al. Effect of soil types and nitrogen fertilizer on nitrous oxide and carbon dioxide emissions in oil palm plantations. Soil Sci. Plant Nutr. 61, 48–60 (2015).
CAS  Article  Google Scholar 

51.
Smith, K. A. Changing views of nitrous oxide emissions from agricultural soil: Key controlling processes and assessment at different spatial scales. Eur. J. Soil Sci. 68, 137–155 (2017).
CAS  Article  Google Scholar 

52.
Bateman, E. J. & Baggs, E. M. Contributions of nitrification and denitrification to N2O emissions from soils at different water-filled pore space. Biol. Fertil. Soils 41, 379–388 (2005).
CAS  Article  Google Scholar 

53.
Chapuis-lardy, L., Wrage, N., Metay, A., Chotte, J. L. & Bernoux, M. Soils, a sink for N2O? A review. Glob. Chang. Biol. 13, 1–17 (2007).
ADS  Article  Google Scholar 

54.
Glatzel, S. & Stahr, K. Methane and nitrous oxide exchange in differently fertilised grassland in southern Germany. Plant Soil 231, 21–35 (2001).
CAS  Article  Google Scholar 

55.
Saggar, S. et al. Denitrification and N2O:N2 production in temperate grasslands: Processes, measurements, modelling and mitigating negative impacts. Sci. Total Environ. 465, 173–195 (2013).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

56.
Warneke, S., Schipper, L. A., Bruesewitz, D. A., Mcdonald, I. & Cameron, S. Rates, controls and potential adverse effects of nitrate removal in a denitrification bed. Ecol. Eng. 37, 511–522 (2011).
Article  Google Scholar 

57.
Wang, Y. et al. Depth-dependent greenhouse gas production and consumption in an upland cropping system in northern China. Geoderma 319, 100–112 (2018).
ADS  CAS  Article  Google Scholar 

58.
Wu, D. et al. N2O consumption by low-nitrogen soil and its regulation by water and oxygen. Soil Biol. Biochem. 60, 165–172 (2013).
CAS  Article  Google Scholar 

59.
Dijkstra, F. A., Morgan, J. A., Follett, R. F. & LeCain, D. R. Climate change reduces the net sink of CH4 and N2O in a semiarid grassland. Glob. Chang. Biol. 19, 1816–1826 (2013).
ADS  PubMed  Article  PubMed Central  Google Scholar 

60.
Ryden, J. C. Denitrification loss from a grassland soil in the field receiving different rates of nitrogen as ammonium nitrate. J. Soil Sci. 34, 355–365 (1983).
CAS  Article  Google Scholar 

61.
Rosenkranz, P. et al. N2O, NO and CH4 exchange, and microbial N turnover over a Mediterranean pine forest soil. Biogeosciences 3(2), 121–133 (2006).
ADS  CAS  Article  Google Scholar 

62.
Menyailo, O. V. & Hungate, B. A. Stable isotope discrimination during soil denitrification: Production and consumption of nitrous oxide. Glob. Biochem. Cycle. 20, 3. https://doi.org/10.1029/2005GB002527 (2006).
CAS  Article  Google Scholar 

63.
Roobroeck, D., Butterbach-Bahl, K., Brüggemann, N. & Boeckx, P. Dinitrogen and nitrous oxide exchanges from an undrained monolith fen: Short-term responses following nitrate addition. Eur. J. Soil Sci. 61, 662–670 (2010).
CAS  Article  Google Scholar 

64.
Kammann, C., Grünhage, L., Müller, C., Jacobi, S. & Jäger, H.-J. Seasonal variability and mitigation options for N2O emissions from differently managed grasslands. Environ. Pollut. 102, 179–186 (1998).
CAS  Article  Google Scholar 

65.
Yang, X. et al. Nitrous oxide emissions from an agro-pastoral ecotone of northern China depending on land uses. Agric. Ecosyst. Environ. 213, 241–251 (2015).
CAS  Article  Google Scholar 

66.
Peng, Q., Qi, Y., Dong, Y., Xiao, S. & He, Y. Soil nitrous oxide emissions from a typical semiarid temperate steppe in inner Mongolia: Effects of mineral nitrogen fertilizer levels and forms. Plant Soil 342, 345–357 (2011).
CAS  Article  Google Scholar 

67.
Eggleston, S. et al. (eds) IPCC Guidelines for National Greenhouse Gas Inventories (Institute for Global Environmental Strategies, Hayama, 2006).
Google Scholar 

68.
Qin, S. et al. Yield-scaled N2O emissions in a winter wheat-summer corn double-cropping system. Atmos. Environ. 55, 240–244 (2012).
ADS  CAS  Article  Google Scholar 

69.
Blackmer, A. M. & Bremner, J. M. Inhibitory effect of nitrate on reduction of N2O to N2 by soil microorganisms. Soil Biol. Biochem. 10, 187–191 (1978).
CAS  Article  Google Scholar 

70.
Ghosh, U., Thapa, R., Desutter, T., He, Y. & Chatterjee, A. Saline-sodic soils: Potential sources of nitrous oxide and carbon dioxide emissions?. Pedosphere 27, 65–75 (2017).
Article  Google Scholar 

71.
Townsend-Small, A. et al. Nitrous oxide emissions and isotopic composition in urban and agricultural systems in southern California. J. Geophys. Res. Biogeosci. 116, 1–11 (2011).
Article  CAS  Google Scholar 

72.
Mariotti, A. et al. Experimental determination of nitrogen kinetic isotope fractionation: Some principles; illustration for the denitrification and nitrification processes. Plant Soil 62, 413–430 (1981).
CAS  Article  Google Scholar 

73.
Ibraim, E. et al. Attribution of N2O sources in a grassland soil with laser spectroscopy based isotopocule analysis. Biogeosciences 16(16), 3247–3266 (2019).
ADS  CAS  Article  Google Scholar 

74.
Timilsina, A. et al. Potential pathway of nitrous oxide formation in plants. Front. Plant Sci. https://doi.org/10.3389/fpls.2020.01177 (2020).
Article  PubMed  PubMed Central  Google Scholar 

75.
Webster, E. A. & Hopkins, D. W. Nitrogen and oxygen isotope ratios of nitrous oxide emitted from soil and produced by nitrifying and denitrifying bacteria. Biol. Fertil. Soils 22, 326–330 (1996).
CAS  Article  Google Scholar 

76.
Wrage, N. et al. Distinguishing sources of N2O in European grasslands by stable isotope analysis. Rapid Commun. Mass Spectrom. 18, 1201–1207 (2004).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

77.
Sutka, R. L., Ostrom, N. E., Ostrom, P. H., Gandhi, H. & Breznak, J. A. Nitrogen isotopomer site preference of N2O produced by Nitrosomonas europaea and Methylococcus capsulatus Bath. Rapid Commun. Mass Spectrom. 17, 738–745 (2003).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

78.
Röckmann, T., Kaiser, J. & Brenninkmeijer, C. A. M. The isotopic fingerprint of the pre-industrial and the anthropogenic N2O source. Atmos. Chem. Phys. 3, 315–323 (2003).
ADS  Article  Google Scholar  More

Mesocosm experiment
To study the effect of earthworms’ bioturbation activities on EPNs recruitment by maize roots, we performed an outdoor mesocosm experiment at the Botanical Garden of Neuchâtel, Switzerland. The mesocosms consisted of 50 × 50 × 30 cm wooden raised garden beds layered with a 1 mm diameter plastic mesh to prevent earthworm escape (Supplementary Fig. S1 in Supplementary material). Each mesocosm was filled with approximately 60 L of the A-layer of an Anthrosol (organo-mineral horizon) enriched with 10% compost and 10% sand. The initial soil was sieved once at 2 cm, and subsequently hand-sieved twice to remove all potential indigenous earthworms present. Before adding compost and sand, natural soil samples were collected, homogenized, dried at 40 °C for 48 h, sieved at 2 mm, and ground using agate mortars for subsequent physicochemical analyses. Specifically, we measured the particle size distribution (modified Robinson pipette method), the organic matter content through loss on ignition by weighing before and after burning 10 g of soil at 450 °C for 2 h, carbon to nitrogen ratio (CN) using an elemental analyser (FLASH2000, Thermo Fisher Scientific, Waltham, Massachusetts, United States); the pH in 1:2.5 soil to water ratio; the cation exchange capacity (CEC) following the cobaltihexamine chloride method; and the total phosphorous (using the Kjeldahl digestion method)25. The initial A-layer of the Anthrosol so was thus characterized as a silty-loamy soil (23% sand, 65% silt, 11% clay), with 7.06% organic matter content, 2.95% organic carbon, with a CN of 11.36, and with 19 ppm of total phosphorus content. The soil pH was 7.65 and the CEC was 5.0 cmolc/kg.
Four experimental treatments were tested (Supplementary Fig. S1): monocultures with and without the endogeic earthworm species Allolobophora icterica; and polycultures with and without the same earthworms. To reduce risks of interspecific variation in earthworms, we used a commercial strain of A. icterica supplied by the Ecotoxicology Department of National Institute for Agricultural Research (INRA Versailles, France).
The simulated monoculture consisted of three maize plants (Zea mays var. Delprim, UFA Delley Semences et Plantes, Delley-Portalban, Switzerland) per mesocosm, whereas one squash plant (Curcubita pepo, var. Rondini, Sativa Rheinau AG, Switzerland) and two bean plants (Phaseolus vulgaris, var. Neckargold, Sativa Rheinau AG) were growing with three maize plants in the simulated polycultures (Supplementary Fig. S1). In total we built 10 mesocosms per treatment (N = 40 mesocosms). All plants were sown early July and grown until mid-October 2018 before the onset of the experiment (Supplementary Fig. S2). Seven weeks after sowing, half of the mesocosms were inoculated with 15 earthworms each. The earthworms were standardized to 6 g of total fresh weight biomass per mesocosm.
In mid-October, the roots of one maize plant per mesocosm were mechanically damaged with a cork borer (punched three times near root area) and watered with 25 ml of a solution containing 500 μg (2.4 µmoles) of jasmonic acid (JA; ( ±)-Jasmonic acid, CAS Number: 77026-92-7, Sigma, St Louis, IL, USA) per plant to induce emission of volatile defence compounds26. JA is a growth phytohormone also called the “wound hormone” as it plays a central role in plant defence and has been shown to induce the release of Eβc in herbivore-attacked maize plants22. Mechanical damage was preferred over direct herbivory on roots to ensure damage reproducibility and to standardize the production of defence volatile compounds. Two days later, four Galleria mellonella (Lepidoptera: Pyralidae) larvae per mesocosm were placed in the soil as sentinel hosts to quantify EPN infection success. Specifically, in each mesocosm, the first two G. mellonella larvae were buried 5 cm deep in the soil and 5 cm away from the stem of a root-broken maize plant (damaged roots), while the second pair of larvae was placed in the same conditions, but close to the roots of an undamaged plant (control roots). Because late-instars G. mellonella larvae are immobile and highly susceptible to EPN infection, they have been extensively used for monitoring EPNs’ presence in soil27,28, as was done here. One day after G. mellonella addition, a solution of less than 2-week old 3000 infective juveniles H. megidis EPNs was inoculated at the centre of all mesocosms. The used H. megidis EPNs (Nematoda: Heterorhabditidae) were supplied by Andermatt Biocontrol AG, Switzerland, and reared on late-instar G. mellonella larvae in accordance with an in vivo rearing protocol described step by step29. Five days after EPN inoculation, all G. mellonella larvae were collected. Dead larvae were directly transferred into White traps to confirm infestation by EPNs, while living larvae were kept in soil-filled 5 × 6 × 4 cm plastic boxes for measuring potential EPN infection. Next, we collected plant traits related to biomass accumulation, including: total aboveground biomass, total vegetative height, and fitness, as the total biomass of all corncobs on each plant. Finally, a fraction of the soil was sampled in each mesocosm for fertility-related analyses; which included CEC and CN measures.
Olfactometer-based bioassays
To dissect the interactive effect of root herbivory and earthworm presence near the roots of maize plants on EPN recruitment, a first (four-arm) olfactometer bioassay was conducted in controlled conditions of temperature, light and humidity (22 ± 2 °C day/16 ± 2 °C night, 55% RH, daytime 08:00 a.m.–06:00 p.m., 230 μmol/m2 s). The belowground olfactometer device (Fig. 2A), modified from Rasmann et al.5, consisted of a central glass chamber filled with white sand (Spielsand classic, Hamann Mercatus GmbH, Germany) extending in side arms connected to terminal glass pots (10 cm high, 15 cm diameter). Pots were filled with 1.2 L of soil (1/4 sand, and 3/4 standard potting soil; Ricoter, Aarberg, Switzerland, 10% relative humidity) as a standard for growing maize in the non-soil substrate when tested in olfactometers30. Three A. icterica earthworms were inoculated in two terminal pots (Fig. 2A). Simultaneously, one maize seedling was sown in each of the four terminal pots, and left to grow for 20 days (two-leaf to three-leaf stage). After 20 days of growth, which is considered as a minimum period for significant bioturbation of the soils31, three second instars of the banded cucumber beetle Diabrotica balteata (Coleoptera: Chrysomelidae) were added to two opposite glass pots containing the plants for the herbivore treatment setup (Fig. 3A). We used D. balteata instead of D. v. virgifera because of quarantine restrictions impeding the use of this species in the climate chambers in Switzerland. D. balteata is a generalist beetle that can feed on maize roots, and has been previously shown to induce maize plants to produce Eβc and attract EPNs32. Therefore, while slight differences might exist in terms of defence induction between the two Diabrotica species, they should be negligible and the results generalizable across Diabroticine beetles. Eggs of D. balteata were supplied by Syngenta Crop Protection (Stein, Switzerland) and larvae reared on a corn-based diet. Overall, the treatments followed a two-by-two factorial design experiment with the presence or absence of earthworms and root herbivores in each four-arm olfactometer (Fig. 2A). After three days, a solution of 2000 H. megidis infective juveniles was inoculated 5 mm below the sand surface in the middle of the central arena. After an additional 24 h, EPNs were retrieved from each side arm using the Baermann decantation funnel method. Next, roots were harvested, carefully washed and flash frozen in liquid nitrogen. Roots were ground in liquid nitrogen and Eβc production from each root system was measured using solid-phase microextraction (SPME) coupled to gas chromatography-mass spectrometry (GCMS) as described in Rasmann et al.5. Finally, for each plant, we scored root and shoot fresh biomass and plant height, measured as the longest leaf length from the ground. The same experiment consisting of 5 olfactometers each time was repeated four times for a total of N = 20 replicates.
A second (two-arm) olfactometer bioassay was performed in order to explore whether earthworm-emitted exudates via the epidermal mucus or faeces would directly interfere with the production of Eβc from maize roots, and the subsequent EPN movement. For this, maize seeds were sown in olfactometer glass pots (10 cm high, 5 cm diameter) with commercial substrate in similar conditions as described above (see Supplementary Fig. S3). Half of the plants were watered with tap water (control) while the other plants were watered with mucus solution. The mucus solution was obtained by caging 10 earthworms (adults and juveniles) in two 1 mm plastic mesh sieves in contact with each other’s open edge. The cage was submerged in 3 mm of tap water and left in darkness at room temperature (25 °C) for one hour, allowing earthworms to move into water and rub their skin against the sieve. The mucus of 20 earthworms (two cages) was collected to water 10 plants (equivalent of two earthworms per plant). The mucus solution (200 mL) was freshly prepared an hour before direct application onto the plants. All plants received the same volume of liquid at the same interval in order to keep substrate between plants as homogenously moist as possible. After 20 days, three second-instar D. balteata larvae were placed in every pot (controls and treatments) and left to feed on maize roots for three days. Connection with the olfactometer system was made 1 day before EPN inoculation, after which, a solution of 2000 infective juvenile EPNs was inoculated in the olfactometer central arena. After 24 h, EPNs were extracted from the two side arms of each olfactometer with the Baermann funnel method and counted under the microscope. Finally, root biomass and Eβc emissions were recorded as described above. The experiment was replicated 10 times.
A third (two-arm) olfactometer bioassay was performed to the test whether earthworm bioturbation activity in bulk soil affects nematode mobility alone, independently of earthworms being in contact with the maize root system. For this, soil-filled side arms and central arena and sand-filled terminal pots (10 cm high, 5 cm diameter) were assembled into a two-arm olfactometer (Fig. 4A). The same natural soil that was used for the mesocosm experiment at the Botanical Garden was used and sieved to 2 mm to ensure effective bioturbation and burrowing by earthworms. Ten soil samples were randomly taken from the soil stock, put on filter paper, emerged in water and left for decantation for 48 h to test the presence of any indigenous nematodes. None were observed and the soil was consequently not sterilized. Based on the study of Chiriboga et al.20 on diffusion of Eβc in different soil textures, soil and sand moisture were set respectively at 20% and 10% to maximise diffusion of volatiles. On the first day, three A. icterica earthworms were added to one half of olfactometer, and none in the other half. Earthworm bioturbation was restricted to half of the central arena by a 0.5 mm-mesh screen dividing it and by an anti-EPN mesh screen at the end of the side arms. Earthworms were left to work the soil for 4 days in climatic chamber (18 ± 2 °C, continuous darkness). Four days were considered enough time for three earthworms to properly burrow 0.5 L of soil. On day 5, the terminal pots were connected to olfactometer central system, and custom-made dispensers containing CO2 generating material (300 mg and sodium hydrogencarbonate and citric acid 3:1) and synthetic Eβc (300 μL, β-Caryophyllene, CAS Number 87-44-5, Sigma, St Louis, IL, USA) were prepared as described in Turlings et al.22, and inserted into the terminal pots filled with sand to ensure that EPN attraction was equally stimulated by both sides of the olfactometer (Fig. 4A). Five hours after inserting the dispensers, a suspension of 2000 H. megidis infective juveniles was inoculated in the central arena and left for 24 h, after which EPN presence in each arm was retrieved using Baermann funnels. The experiment was replicated eight times.
Statistical analysis
All statistical analyses were performed on R33.
Mesocosm outdoor experiment
We scored the probability of infection by dividing the number of larvae infected by EPNs around each plant by two. We then assessed the full interactive effect of culture type (two levels), earthworms (two levels), and root induction (two levels) on the probability of infection with generalized linear model analysis (GLM) with quasi-binomial distribution. We next performed the same GLM, but by comparing the interactive effect of earthworms and root induction, by splitting the data into monoculture and polyculture systems. Probabilities of infection scores were visualized using the library popbio34. The interactive effect of culture type and earthworms on CEC, CN, plant biomass and corn earcobs biomass was assessed using two-ways ANOVAs, followed by TukeyHSD post-hoc tests. For plant traits, we included mesocosm as a blocking effect in the model.
Four-arm olfactometer bioassay
Analysis of variation in nematode recruitment across earthworms by root herbivory treatments was performed using generalized linear model analysis (GLM) with quasi-Poisson distribution to take data overdispersion into consideration, and by including the experiment date as a blocking factor in the model. Differences among treatments were assessed using analyses of deviance and F statistics. The analysis of the effect of herbivores, earthworms and their interactions on log + 1-transformed Eβc emissions were performed using two-way ANOVA, and by including experiment as blocking factor, and root biomass as covariate in the model. Differences among treatments were assessed using Tukey HSD tests. The effect of root herbivory and earthworms on plant biomass accumulation was assessed on the first principal component analysis (PCA) axis that included plant height, root and shoot fresh biomass (Supplementary Fig. S4).
Mucus experiment
Analysis of variation in nematode recruitment across treatments was performed using GLM with quasi-Poisson distribution. Analysis of the effect of earthworm mucus on log + 1-transformed Eβc emissions and root biomass were performed using one-way ANOVAs.
Bioturbation and synthetic Eβc olfactometer bioassay
The analysis of variation in nematode recruitment across earthworm presence/absence treatment was performed using GLM with quasi-Poisson distribution, and followed by analysis of deviance. More

Specimens examined
Seven Australian genetic strains (clonal genotypes) of phylloxera were assessed in this project: G1, G4, G7, G19, G20, G30 and G38; genotype numbers follow14. These strains were obtained from live insect colonies maintained by Agriculture Victoria Rutherglen, Victoria. Root aphids, mealybugs and other non-target invertebrates were collected from vineyards for assay specificity testing. Additional aphids, from grass and vegetable host plants, as well as Phylloxeroidea (Adelges spp. and Pineus sp.) from pine trees, were also collected to create a comprehensive Aphidomorpha (Phylloxeroidea/Aphidoidea) species panel to test the specificity of the new LAMP assay.
Phylloxera DNA extractions and molecular variation
DNA was extracted from 100% ethanol preserved whole phylloxera adult, crawler, and eggs (destructive extraction method) using a DNeasy Blood and Tissue extraction kit (Qiagen, Australia), following the manufacturers protocol. The DNA concentrations were quantified using a Qubit 2.0 Flourometer (Invitrogen, Life Technologies, Australia) following the manufacturers protocol and stored at − 20 °C. These samples provided “clean” DNA preparations to use for phylloxera DNA barcoding, to assess Cytochrome Oxidase I (COI) genetic variation between strains, and for development of the LAMP assay.
All specimens, both phylloxera and the non-target invertebrates, were identified morphologically, prior to DNA extraction, followed by molecular identification using standard DNA barcoding methods7,17. DNA sequences from the 5′ region of the COI locus were obtained using LCO1490 / HCO2198 primers19. The thermal cycling conditions consisted of a PCR amplification profile of: 2 min at 94 °C, 40 cycles of at 94 °C for 30 s, 50 °C for 45 s and 72 °C for 45 s, and a final extension of 2 min at 72 °C.
PCR amplicons were sanger sequenced by Macrogen Inc. (Seoul, Korea) and sequences were compared to public databases (NCBI and BOLD), to determine similarity (i.e.  > 99% matches) with previously identified reference species. DNA sequences from the laboratory strains of phylloxera were used to develop phylloxera specific LAMP primers in this research.
An additional in-field compatible non-destructive DNA extraction method for “crude” DNA extractions was tested. Intact specimens of phylloxera (egg, crawler, and adult) were processed using a modified HotSHOT protocol “HS6” from20. There are several variations of HotSHOT method which have been used previously to extract DNA from mice (HS4)16 and Zebrafish (HS3)21. The HotSHOT (HS6) protocol has been refined to further improve the efficiency of extracted DNA by combining NaOH and Tris–EDTA buffer in a single step20. In our study twenty microlitres of HotSHOT extraction buffer consisting of 25 mM NaOH + TE buffer, pH 8.0 (Invitrogen , Australia) (1:1 volume) was pipetted into each 8 well strip of LAMP PCR tubes (OptiGene, UK). Phylloxera samples were removed from ethanol and air dried on a paper towel for approx.1 min. Single whole adult, crawler, single and/or multiple eggs were transferred with a toothpick (single use to prevent cross contamination), into each well. Each sample was immersed in HotSHOT buffer with up to six samples processed simultaneously in the portable real-time fluorometer (Genie III, OptiGene, UK). The protocol used the Genie III as an incubator at 95 °C for 5 min followed by  > 1 min incubation on ice. The DNA was quantified using a NanoDrop ND-1000 Spectrophotometer (Thermo Fisher, Australia) and stored at − 20 °C.
Development of the LAMP assay
LAMP primer design
Phylloxera specific primers were designed from an alignment of publicly available COI (5′-region) DNA sequences (from BOLD, http://www.boldsystems.org), which included (i) target (phylloxera) and (ii) non-target species that were either closely related to phylloxera (i.e. Phylloxeroidea), or aphids (Aphididae) known to utilise grapevines or plant roots as hosts (Supplementary Table 1).
For all primers, the GC content (%), predicted melting temperature (Tm), and potential secondary structure (hairpins or dimers) were analysed using the Integrated DNA Technologies (IDT) online OligoAnalyzer tool (https://sg.idtdna.com/calc/analyzer), using the qPCR parameter sets. Complete sets of LAMP primers were analysed together to detect potential primer dimer interactions using the Thermo Fisher Multiple Primer Analyzer tool (www.thermofisher.com). Potential within-species genetic variation in phylloxera LAMP primers was examined using combined DNA sequences of Australian phylloxera and reference sequences available on BOLD (i.e. the seven laboratory strains, plus 230 × 5′-COI reference sequences on BOLD, accessed May 2018).
A synthetic 221 bp DNA fragment, designed from the complete fragment used for the assay (Fig. 1), was synthesized as a gBlock (Integrated DNA Technologies, Iowa, USA) to provide a reliable LAMP positive control. This fragment consisted of all eight LAMP primer regions (Fig. 1) concatenated together, with the non-primer DNA removed from between primers and replaced a run of “ccc” DNA bases between the primer regions and at the ends of the fragment, to increase the overall GC content of the synthetic fragment.
LAMP primer ratio optimisation
Primer master mix was prepared following published protocols11. Optimisation of the six primer (Table 1) ratio and concentration for the phylloxera LAMP assay was also conducted according to11.
LAMP assay conditions
The phylloxera LAMP assay was performed following the same method as published11. All LAMP assays were run in the Genie III at 65 °C for 25 min followed by an annealing curve analysis from 98 °C to 73 °C with ramping at 0.05 °C/s. The total run time being approximately 35 min.
Comparison of molecular methods for phylloxera identification
Analytical sensitivity of the LAMP assay compared with qPCR
Clean DNA was extracted from phylloxera (3 adult samples were pooled for one DNA extraction) using the destructive method. A fourfold serial dilution of two biological replicates was prepared using ultrapure water. Starting DNA concentrations were quantified using a Qubit 2.0 Flourometer (Invitrogen, Life Technologies, Australia) following the manufacturers protocol. Phylloxera DNA was serially diluted from 1.0 ng/µL to 0.000061 ng/µL (1:1 to 1:16,384). Sensitivity of the LAMP assay was tested using the serially diluted DNA in the Genie III, following the same assay conditions as mentioned above. The time of amplification and anneal derivative temperature was recorded for all samples.
The same serial dilution of DNA extracts was also used in real-time qPCR assay. The primers and probe set (manufactured by Sigma, Australia) and cycling conditions used were as published9. Real-time qPCR was performed in QuantStudio 3 Real time PCR system (Thermo Fisher Scientific, Australia) in a total volume of 25 µL with technical replicates for each dilution. Each reaction mixture included 12.5 µL Platinum Quantitative PCR SuperMix-UDG (Invitrogen, Australia), 0.5 µM of each forward and reverse primers, 0.2 µM Taqman probe, 4 µL of template DNA and made up to 25 µL with RNA-free water. An NTC with 4 µl of water instead of DNA was included in each run to check for reagent contamination. The thermal cycling conditions consisted of a two-step denaturation: 2 min at 50 °C and 10 min at 95 °C, followed by 40 cycles of amplification in a two-step procedure: 95 °C for 15 s and 60 °C for 1 min. The mean Cq value (cycling quantification value) of the 8 dilutions were recorded for comparison with the time of amplification obtained from the LAMP assay.
Performance of non-destructive DNA samples (HotSHOT HS6)
DNA was non-destructively extracted from whole phylloxera egg, crawler, and adult (n = 3) using the “crude” HotSHOT HS6 protocol, as mentioned above. The extracted DNA was tested across the full range of molecular techniques available for detection of phylloxera: real-time qPCR (using 5 µL and 2 µL DNA per reaction)9, microsatellite genotyping14, DNA barcoding7, as well as the new LAMP assay. Real-time qPCR and DNA barcoding were performed as described above. Microsatellite genotyping was performed following a laboratory protocol (Blacket et al. unpublished) that screens the six phylloxera microsatellite loci used to define Australian genotypes5,14, modified to utilise fluorescently labelled primer tails following22 and a multiplex PCR kit (Qiagen, Australia). Capillary separation of fluorescently labelled microsatellites was performed commercially by AGRF (Melbourne), using LIZ-500 size standards. Genotyping of fsa files was performed using the microsatellite plugin in Geneious R11 (https://www.geneious.com).
Phylloxera specimens (adults and crawlers) were visually examined pre-DNA extraction, with slides prepared from specimens’ post-DNA extraction to confirm retention of key morphological characters. Microscopic slides were prepared following the protocols as published22 and were deposited as reference specimens in the Victorian Agricultural Insect Collection (VAIC) (https://collections.ala.org.au).
Evaluation of gBlock DNA fragment for use as synthetic DNA positive in LAMP assay
To evaluate detection sensitivity a tenfold serial dilution of the gBlock DNA fragment was prepared using ultrapure water (Invitrogen, Australia). Synthetic DNA was serially diluted from ~ 100 million copies down to ~ 10 copies (108 copies to 10 copies). Sensitivity of the LAMP assay was tested using the serially diluted synthetic DNA in the Genie III, following the same assay conditions as mentioned above (run time increased from 25 to 35 min). Following this another LAMP run was conducted to determine the best dilution to be used as synthetic DNA for positive template in LAMP assay. The same fourfold serial dilution (1.0 ng/µL to 0.00098 ng/µL) of clean phylloxera DNA prepared previously was used as template to compare with one hundred thousand copies (105) of synthetic DNA. The amount of phylloxera DNA was then calculated from the amplification time of 105 copies of synthetic DNA. More