Philippa Kaur

1.
Bell, D. The coming of the post-industrial society. Educ. Forum 40, 574–579 (1976).
Google Scholar 
2.
Veenhoven, R. Life is getting better: societal evolution and fit with human nature. Soc. Indic. Res. 97, 105–122 (2010).
Google Scholar 

3.
Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).
Google Scholar 

4.
Ripple, W. J. et al. World scientists’ warning to humanity: a second notice. BioScience 67, 1026–1028 (2017).
Google Scholar 

5.
Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752 (2014).
CAS  Google Scholar 

6.
Ceballos, G. et al. Accelerated modern human-induced species losses: entering the sixth mass extinction. Sci. Adv. 1, e1400253 (2015).
Google Scholar 

7.
Ehrlich, P. R. & Kennedy, D. Millennium assessment of human behavior. Science 309, 562–563 (2005).
CAS  Google Scholar 

8.
Crompton, T. Common Cause: The Case for Working with Our Cultural Values (World Wildlife Fund, 2010).

9.
What is the Great Transition? Great Transition Initiative https://greattransition.org/about/what-is-the-great-transition (2020).

10.
Wilson, D. S. Intentional cultural change. Curr. Opin. Psychol. 8, 190–193 (2016).
Google Scholar 

11.
Gelfand, M. J. et al. Differences between tight and loose cultures: a 33-nation study. Science 332, 1100–1104 (2011).
CAS  Google Scholar 

12.
Oishi, S. & Graham, J. Social ecology: lost and found in psychological science. Perspect. Psychol. Sci. 5, 356–377 (2010).
Google Scholar 

13.
Mace, G. M. Whose conservation? Science 345, 1558–1560 (2014).
CAS  Google Scholar 

14.
Teel, T. L. & Manfredo, M. J. Understanding the diversity of public interests in wildlife conservation. Conserv. Biol. 24, 128–139 (2009).
Google Scholar 

15.
Manfredo, M. J., Teel, T. L. & Dietsch, A. M. Implications of human value shift and persistence for biodiversity conservation. Conserv. Biol. 30, 287–296 (2016).
Google Scholar 

16.
Manfredo, M. J., Berl, R. E. W., Teel, T. L., & Brusktotter, J. T. Bringing social values to wildlife conservation decisions. Front. Ecol. Environ. (in the press).

17.
Simberloff, D. Flagships, umbrellas, and keystones: is single-species management passé in the landscape era? Biol. Conserv. 83, 247–257 (1998).
Google Scholar 

18.
Meine, C., Soulé, M. & Noss, R. F. “A mission‐driven discipline”: the growth of conservation biology. Conserv. Biol. 20, 631–651 (2006).
Google Scholar 

19.
Ives, C. D. & Kendal, D. The role of social values in the management of ecological systems. J. Environ. Manag. 144, 67–72 (2014).
Google Scholar 

20.
Manfredo, M. J. et al. Why social values cannot be changed for the sake of conservation. Conserv. Biol. 31, 772–780 (2017).
Google Scholar 

21.
Schwartz, S. H. A theory of cultural value orientations: explication and applications. Int. J. Comp. Sociol. 5, 136–182 (2006).
Google Scholar 

22.
Bruskotter, J. T. et al. Conservationists’ moral obligations toward wildlife: values and identity promote conservation conflict. Biol. Conserv. 240, 108296 (2019).
Google Scholar 

23.
Smolicz, J. Core values and cultural identity. Ethn. Racial Stud. 4, 75–90 (1981).
Google Scholar 

24.
Geels, F. W. Technological transitions as evolutionary reconfiguration processes: a multi-level perspective and a case-study. Res. Policy 31, 1257–1274 (2002).
Google Scholar 

25.
Kitayama, S., Conway, L. G. III, Pietromonaco, P. R., Park, H. & Plaut, V. C. Ethos of independence across regions in the United States: the production-adoption model of cultural change. Am. Psychol. 65, 559–574 (2010).
Google Scholar 

26.
Inglehart, R. Culture Shift in Advanced Industrial Society (Princeton Univ. Press, 2018).

27.
Tormos, R. The Rhythm of Modernization: How Values Change Over Time (Brill, 2019).

28.
Bardi, A., Lee, J. A., Hofmann-Towfigh, N. & Soutar, G. The structure of intraindividual value change. J. Pers. Soc. Psychol. 97, 913–929 (2009).
Google Scholar 

29.
Gouveia, V. V., Vione, K. C., Milfont, T. L. & Fischer, R. Patterns of value change during the life span: some evidence from a functional approach to values. Pers. Soc. Psychol. Bull. 41, 1276–1290 (2015).
Google Scholar 

30.
Bardi, A. & Goodwin, R. The dual route to value change: individual processes and cultural moderators. J. Cross Cult. Psychol. 42, 271–287 (2011).
Google Scholar 

31.
Hitlin, S. & Piliavin, J. A. Values: reviving a dormant concept. Annu. Rev. Sociol. 30, 359–393 (2004).
Google Scholar 

32.
Inglehart, R. Aggregate stability and individual-level flux in mass belief systems: the level of analysis paradox. Am. Political Sci. Rev. 79, 97–116 (1985).
Google Scholar 

33.
Molloy, R., Smith, C. L. & Wozniak, A. Internal migration in the United States. J. Econ. Perspect. 25, 173–196 (2011).
Google Scholar 

34.
Motyl, M., Iyer, R., Oishi, S., Trawalter, S. & Nosek, B. A. How ideological migration geographically segregates groups. J. Exp. Soc. Psychol. 51, 1–14 (2014).
Google Scholar 

35.
Kluckholn, C. in Toward a General Theory of Action (eds T. Parsons. T. & Shils, E. A.) 388–433 (Harvard Univ. Press, 1951).

36.
Manfredo, M. J., Teel, T. L. & Henry, K. L. Linking society and environment: a multilevel model of shifting wildlife value orientations in the western United States. Soc. Sci. Quart. 90, 407–427 (2009).
Google Scholar 

37.
Bruskotter, J. T. et al. Modernization, risk, and conservation of the world’s largest carnivores. BioScience 67, 646–655 (2017).
Google Scholar 

38.
Manfredo, M. J., Urquiza-Haas, E. G., Carlos, A. W. D., Bruskotter, J. T. & Dietsch, A. M. How anthropomorphism is changing the social context of modern wildlife conservation. Biol. Conserv. 241, 108297 (2019).
Google Scholar 

39.
Inglehart, R. & Welzel, C. Modernization, Cultural Change, and Democracy: The Human Development Sequence (Cambridge Univ. Press, 2005).

40.
Jacobson, C. A. & Decker, D. J. Governance of state wildlife management: reform and revive or resist and retrench? Soc. Nat. Resour. 21, 441–448 (2008).
Google Scholar 

41.
Cohen, J. Statistical Power Analysis for the Behavioural Sciences 2nd edn (Routledge, 1988).

42.
Fisher, P. A political outlier: the distinct politics of the millennial generation. Society 55, 35–40 (2018).
Google Scholar 

43.
Tormos, R., Vauclair, C. M. & Dobewall, H. Does contextual change affect basic human values? A dynamic comparative multilevel analysis across 32 European countries. J. Cross Cult. Psychol. 48, 490–510 (2017).
Google Scholar 

44.
Schuster, C., Pinkowski, L. & Fischer, D. Intra-individual value change in adulthood: a systematic literature review of longitudinal studies assessing Schwartz’s value orientations. Z. Psychol. 227, 42–52 (2019).
Google Scholar 

45.
Bruskotter, J. T., Vucetich, J. A. & Nelson, M. P. Animal rights and wildlife conservation: conflicting or compatible. Wildl. Prof. 11, 40–43 (2017).
Google Scholar 

46.
Wildes, F. T. Recent themes in conservation philosophy and policy in the United States. Environ. Conserv. 22, 143–150 (1995).
Google Scholar 

47.
Manfredo, M. J. et al. America’s Wildlife Values: The Social Context of Wildlife Management in the U.S. (Colorado State Univ., 2018).

48.
Duffin, E. Forecast on Urbanization in the U.S., 2000-2050 (Statistica, 2019).

49.
Oishi, S. et al. Does a major earthquake change job preferences and human values? Eur. J. Pers. 31, 258–265 (2017).
Google Scholar 

50.
Diamond, J. Collapse: How Societies Choose to Fail or Succeed (Penguin, 2011).

51.
Kenter, J. O. et al. Loving the mess: navigating diversity and conflict in social values for sustainability. Sustain. Sci. 14, 1439–1461 (2019).
Google Scholar 

52.
Dillman, D. A., Smyth, J. D. & Christian L. M. Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method (John Wiley & Sons, 2014).

53.
Scheaffer, R. L., Mendenhall, W. & Ott, L. Elementary Survey Sampling 5th edn (Duxbury, 1996).

54.
Keeter, S. et al. What Low Response Rates Mean for Telephone Surveys (Pew Research Center, 2017).

55.
Stedman, R. C. et al. The end of the (research) world as we know it? Understanding and coping with declining response rates to mail surveys. Soc. Nat. Resour. 32, 1139–1154 (2019).
Google Scholar 

56.
American Community Survey 2016 5-Year Estimates (US Census Bureau, 2017).

57.
National Survey of Fishing, Hunting, and Wildlife-Associated Recreation (US Census Bureau, 2011).

58.
Urban and Rural (US Census Bureau, 2018); https://www.census.gov/programs-surveys/geography/guidance/geo-areas/urban-rural.html

59.
SAGDP2S: Annual Gross Domestic Product by State (US Bureau of Economic Analysis, 2010); https://apps.bea.gov/iTable/index_regional.cfm

60.
Kerlinger, F. N. & Lee, H. B. Foundations of Behavioral Research 4th edn (Wadsworth, 2000).

61.
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
Google Scholar  More

1.
Steffen, W., Broadgate, W., Deutsch, L., Gaffney, O. & Ludwig, C. The trajectory of the Anthropocene: The Great Acceleration. Anthr. Rev. 2, 81–98 (2015).
Google Scholar 
2.
Steffen, W., Grinevald, J., Crutzen, P. & McNeill, J. The Anthropocene: conceptual and historical perspectives. Phil. Trans. R. Soc. A 369, 842–867 (2011).
PubMed  Google Scholar 

3.
Steinbauer, M. J. et al. Accelerated increase in plant species richness on mountain summits is linked to warming. Nature 556, 231–234 (2018).
CAS  PubMed  Google Scholar 

4.
McInerney, F. A. & Wing, S. L. The Paleocene-Eocene Thermal Maximum: a perturbation of carbon cycle, climate, and biosphere with implications for the future. Annu. Rev. Earth Planet. Sci. 39, 489–516 (2011).
CAS  Google Scholar 

5.
Herrero, C., García-Olivares, A. & Pelegrí, J. L. Impact of anthropogenic CO2 on the next glacial cycle. Clim. Change 122, 283–298 (2014).
CAS  Google Scholar 

6.
Clark, P. U. et al. Consequences of twenty-first-century policy for multi-millennial climate and sea-level change. Nat. Clim. Change 6, 360–369 (2016).
Google Scholar 

7.
Berger, A. & Loutre, M. F. An exceptionally long interglacial ahead? Science 297, 1287–1288 (2002).
CAS  PubMed  Google Scholar 

8.
Burke, K. D. et al. Pliocene and Eocene provide best analogues for near-future climates. Proc. Natl Acad. Sci. USA 115, 13288–13293 (2018).
CAS  PubMed  Google Scholar 

9.
Fisichelli, N. A., Schuurman, G. W. & Hoffman, C. H. Is ‘resilience’ maladaptive? Towards an accurate lexicon for climate change adaptation. Environ. Manag. 57, 753–758 (2016).
Google Scholar 

10.
Prober, S. M., Doerr, V. A. J., Broadhurst, L. M., Williams, K. J. & Dickson, F. Shifting the conservation paradigm: a synthesis of options for renovating nature under climate change. Ecol. Monogr. 89, e01333 (2019).
Google Scholar 

11.
Scheffers, B. R. & Pecl, G. Persecuting, protecting or ignoring biodiversity under climate change. Nat. Clim. Change 9, 581–586 (2019).
Google Scholar 

12.
Barnosky, A. D. et al. Merging paleobiology with conservation biology to guide the future of terrestrial ecosystems. Science 355, eaah4787 (2017).
PubMed  Google Scholar 

13.
Hughes, F. M. R., Adams, W. M. & Stroh, P. A. When is open-endedness desirable in restoration projects? Restor. Ecol. 20, 291–295 (2012).
Google Scholar 

14.
Williams, J. W. & Burke, K. in Climate Change and Biodiversity: Transforming the Biosphere (eds Lovejoy, T & Hannah, L.) 128–141 (Yale Univ. Press, 2019).

15.
Webb, T. III. Is vegetation in equilibrium with climate? How to interpret late-Quaternary pollen data. Vegetatio 67, 75–91 (1986).
Google Scholar 

16.
Blonder, B. et al. Predictability in community dynamics. Ecol. Lett. 20, 293–306 (2017).
PubMed  Google Scholar 

17.
Svenning, J.-C. & Sandel, B. Disequilibrium vegetation dynamics under future climate change. Am. J. Bot. 100, 1266–1286 (2013).
PubMed  Google Scholar 

18.
Huntley, B. et al. Climatic disequilibrium threatens conservation priority forests. Conserv. Lett. 11, e12349 (2018).
Google Scholar 

19.
Bertrand, R. et al. Ecological constraints increase the climatic debt in forests. Nat. Commun. 7, 12643 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

20.
Zimova, M., Mills, L. S. & Nowak, J. J. High fitness costs of climate change-induced camouflage mismatch. Ecol. Lett. 19, 299–307 (2016).
PubMed  Google Scholar 

21.
Visser, M. E. & Gienapp, P. Evolutionary and demographic consequences of phenological mismatches. Nat. Ecol. Evol. 3, 879–885 (2019).
PubMed  PubMed Central  Google Scholar 

22.
Davis, M. B. & Shaw, R. G. Range shifts and adaptive responses to Quaternary climate change. Science 292, 673–679 (2001).
CAS  PubMed  Google Scholar 

23.
Ratajczak, Z. et al. Abrupt change in ecological systems: inference and diagnosis. Trends Ecol. Evol. 33, 513–526 (2018).
PubMed  Google Scholar 

24.
Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377 (2017).
CAS  PubMed  Google Scholar 

25.
Williams, J. W., Blois, J. L. & Shuman, B. N. Extrinsic and intrinsic forcing of abrupt ecological change: case studies from the late Quaternary. J. Ecol. 99, 664–677 (2011).
Google Scholar 

26.
Lenton, T. M. et al. Tipping elements in the Earth’s climate system. Proc. Natl Acad. Sci. USA 105, 1786–1793 (2008).
CAS  PubMed  Google Scholar 

27.
Scheffer, M. et al. Anticipating critical transitions. Science 338, 344–348 (2012).
CAS  PubMed  Google Scholar 

28.
Scheffer, M., Hirota, M., Holmgren, M., Van Nes, E. H. & Chapin, F. S. Thresholds for boreal biome transitions. Proc. Natl Acad. Sci. USA 109, 21384–21389 (2012).
CAS  PubMed  Google Scholar 

29.
Boettiger, C. & Hastings, A. Tipping points: from patterns to predictions. Nature 493, 157–158 (2013).
CAS  PubMed  Google Scholar 

30.
Boettiger, C., Ross, N. & Hastings, A. Early warning signals: the charted and uncharted territories. Theor. Ecol. 6, 255–264 (2013).
Google Scholar 

31.
Lenton, T. M. et al. Climate tipping points — too risky to bet against. Nature 575, 593–595 (2019).
Google Scholar 

32.
Devictor, V., Julliard, R., Couvet, D. & Jiguet, F. Birds are tracking climate warming, but not fast enough. Proc. R. Soc. B 275, 2743–2748 (2008).
PubMed  Google Scholar 

33.
Dakos, V., Carpenter, S. R., van Nes, E. H. & Scheffer, M. Resilience indicators: prospects and limitations for early warnings of regime shifts. Phil. Trans. R. Soc. B 370, 20130263 (2014).
Google Scholar 

34.
IPCC in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Summary for Policymakers (Cambridge Univ. Press, 2013).

35.
Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).
PubMed  Google Scholar 

36.
Kudo, G. & Ida, T. Y. Early onset of spring increases the phenological mismatch between plants and pollinators. Ecology 94, 2311–2320 (2013).
Google Scholar 

37.
Körner, C. & Basler, D. Phenology under global warming. Science 327, 1461–1462 (2010).
PubMed  Google Scholar 

38.
Seddon, A. W. R., Macias-Fauria, M., Long, P. R., Benz, D. & Willis, K. J. Sensitivity of global terrestrial ecosystems to climate variability. Nature 531, 229–232 (2016).
CAS  PubMed  Google Scholar 

39.
Chen, I. C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).
CAS  PubMed  Google Scholar 

40.
Lenoir, J., Gégout, J. C., Marquet, P. A., de Ruffray, P. & Brisse, H. A significant upward shift in plant species optimum elevation during the 20th Century. Science 320, 1768–1771 (2008).
CAS  PubMed  PubMed Central  Google Scholar 

41.
Bellemare, J. & Deeg, C. Horticultural escape and naturalization of Magnolia tripetala in western Massachusetts: biogeographic context and possible relationship to recent climate change. Rhodora 117, 371–383 (2015).
Google Scholar 

42.
Blowes, S. A. et al. The geography of biodiversity change in marine and terrestrial assemblages. Science 366, 339–345 (2019).
CAS  PubMed  Google Scholar 

43.
Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).
CAS  PubMed  Google Scholar 

44.
Parmesan, C. Influences of species, latitudes and methodologies on estimates of phenological response to global warming. Glob. Change Biol. 13, 1860–1872 (2007).
Google Scholar 

45.
Albright, T. P. et al. Heat waves measured with MODIS land surface temperature data predict changes in avian community structure. Remote Sens. Environ. 115, 245–254 (2011).
Google Scholar 

46.
Cazelles, K. et al. Homogenization of freshwater lakes: recent compositional shifts in fish communities are explained by gamefish movement and not climate change. Glob. Change Biol. 25, 4222–4233 (2019).
Google Scholar 

47.
Guo, F., Lenoir, J. & Bonebrake, T. C. Land-use change interacts with climate to determine elevational species redistribution. Nat. Commun. 9, 1315 (2018).
PubMed  PubMed Central  Google Scholar 

48.
Abatzoglou, J. T., Dobrowski, S. Z. & Parks, S. A. Multivariate climate departures have outpaced univariate changes across global lands. Sci. Rep. 10, 3891 (2020).
CAS  PubMed  PubMed Central  Google Scholar 

49.
VanDerWal, J. et al. Focus on poleward shifts in species’ distribution underestimates the fingerprint of climate change. Nat. Clim. Change 3, 239–243 (2013).
Google Scholar 

50.
Ordonez, A., Williams, J. W. & Svenning, J. C. Mapping climatic mechanisms likely to favour the emergence of novel communities. Nat. Clim. Change 6, 1104–1109 (2016).
Google Scholar 

51.
Hof, C., Levinsky, I., Araújo, M. B. & Rahbek, C. Rethinking species’ ability to cope with rapid climate change. Glob. Change Biol. 17, 2987–2990 (2011).
Google Scholar 

52.
Brown, S. C., Wigley, T. M. L., Otto-Bliesner, B. L., Rahbek, C. & Fordham, D. A. Persistent Quaternary climate refugia are hospices for biodiversity in the Anthropocene. Nat. Clim. Change 10, 244–248 (2020).
Google Scholar 

53.
Buizert, C. et al. Greenland temperature response to climate forcing during the last deglaciation. Science 345, 1177–1180 (2014).
CAS  PubMed  Google Scholar 

54.
Steffensen, J. P. et al. High-resolution Greenland ice core data show abrupt climate change happens in few years. Science 321, 680–684 (2008).
CAS  PubMed  Google Scholar 

55.
Jackson, S. T. & Overpeck, J. T. Responses of plant populations and communities to environmental changes of the late Quaternary. Paleobiology 26 (Suppl.), 194–220 (2000).
Google Scholar 

56.
Prentice, I. C., Bartlein, P. J. & Webb, T. III. Vegetation and climate change in eastern North America since the last glacial maximum. Ecology 72, 2038–2056 (1991).
Google Scholar 

57.
Giesecke, T., Brewer, S., Finsinger, W., Leydet, M. & Bradshaw, R. H. W. Patterns and dynamics of European vegetation change over the last 15,000 years. J. Biogeogr. 44, 1441–1456 (2017).
Google Scholar 

58.
Ordonez, A. & Williams, J. W. Climatic and biotic velocities for woody taxa distributions over the last 16 000 years in eastern North America. Ecol. Lett. 16, 773–781 (2013).
PubMed  Google Scholar 

59.
Williams, J. W., Post, D. M., Cwynar, L. C., Lotter, A. F. & Levesque, A. J. Rapid and widespread vegetation responses to past climate change in the North Atlantic region. Geology 30, 971–974 (2002).
CAS  Google Scholar 

60.
Tinner, W. & Lotter, A. F. Central European vegetation response to abrupt climate change at 8.2 ka. Geology 29, 551–554 (2001).
Google Scholar 

61.
Juggins, S. Quantitative reconstructions in paleolimnology: new paradigm or sick science? Quat. Sci. Rev. 64, 20–32 (2013).
Google Scholar 

62.
Ammann, B. et al. Responses to rapid warming at Termination 1a at Gerzensee (Central Europe): primary succession, albedo, soils, lake development, and ecological interactions. Palaeogeogr. Palaeoclimatol. Palaeoecol. 391, 111–131 (2013).
Google Scholar 

63.
Ammann, B., von Grafenstein, U. & van Raden, U. J. Biotic responses to rapid warming about 14,685 yr BP: introduction to a case study at Gerzensee (Switzerland). Palaeogeogr. Palaeoclimatol. Palaeoecol. 391, 3–12 (2013).
Google Scholar 

64.
Cotto, O. et al. A dynamic eco-evolutionary model predicts slow response of alpine plants to climate warming. Nat. Commun. 8, 15399 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

65.
Petitpierre, B. et al. Climatic niche shifts are rare among terrestrial plant invaders. Science 335, 1344–1348 (2012).
CAS  PubMed  Google Scholar 

66.
Hui, C., Roura-Pascual, N., Brotons, L., Robinson, R. A. & Evans, K. L. Flexible dispersal strategies in native and non-native ranges: environmental quality and the ‘good–stay, bad–disperse’ rule. Ecography 35, 1024–1032 (2012).
Google Scholar 

67.
Grubb, P. J. The maintenance of species-richness in plant communities: the importance of the regeneration niche. Biol. Rev. 52, 107–145 (1977).
Google Scholar 

68.
Jackson, S. T., Betancourt, J. L., Booth, R. K. & Gray, S. T. Ecology and the ratchet of events: climate variability, niche dimensions, and species distributions. Proc. Natl Acad. Sci. USA 106, 19685–19692 (2009).
CAS  PubMed  Google Scholar 

69.
Hughes, T. P. et al. Global warming impairs stock–recruitment dynamics of corals. Nature 568, 387–390 (2019).
CAS  PubMed  Google Scholar 

70.
Stevens-Rumann, C. S. et al. Evidence for declining forest resilience to wildfires under climate change. Ecol. Lett. 21, 243–252 (2018).
PubMed  Google Scholar 

71.
Keeley, J. E., van Mantgem, P. & Falk, D. A. Fire, climate and changing forests. Nat. Plants 5, 774–775 (2019).
PubMed  Google Scholar 

72.
Raffa, K. F., Powell, E. N. & Townsend, P. A. Temperature-driven range expansion of an irruptive insect heightened by weakly coevolved plant defenses. Proc. Natl Acad. Sci. USA 110, 2193–2198 (2013).
CAS  PubMed  Google Scholar 

73.
Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).
CAS  PubMed  Google Scholar 

74.
Feeley, K. J. et al. Upslope migration of Andean trees. J. Biogeogr. 38, 783–791 (2011).
Google Scholar 

75.
Ricklefs, R. E. & Latham, R. E. Intercontinental correlation of geographical ranges suggests stasis in ecological traits of relict genera of temperature perennial herbs. Am. Nat. 139, 1305–1321 (1992).
Google Scholar 

76.
McCain, C. M. & King, S. R. B. Body size and activity times mediate mammalian responses to climate change. Glob. Change Biol. 20, 1760–1769 (2014).
Google Scholar 

77.
Pither, J., Pickles, B. J., Simard, S. W., Ordonez, A. & Williams, J. W. Below-ground biotic interactions moderated the postglacial range dynamics of trees. New Phytol. 220, 1148–1160 (2018).
PubMed  Google Scholar 

78.
Lawler, J. J. & Olden, J. D. Reframing the debate over assisted colonization. Front. Ecol. Environ. 9, 569–574 (2011).
Google Scholar 

79.
Schwartz, M. W. et al. Managed relocation: integrating the scientific, regulatory, and ethical challenges. BioScience 62, 732–743 (2012).
Google Scholar 

80.
Van der Veken, S., Hermy, M., Vellend, M., Knapen, A. & Verheyen, K. Garden plants get a head start on climate change. Front. Ecol. Environ. 6, 212–216 (2008).
Google Scholar 

81.
Svenning, J.-C. & Skov, F. Limited filling of the potential range in European tree species. Ecol. Lett. 7, 565–573 (2004).
Google Scholar 

82.
Sax, D. F., Early, R. & Bellemare, J. Niche syndromes, species extinction risks, and management under climate change. Trends Ecol. Evol. 28, 517–523 (2013).
PubMed  Google Scholar 

83.
Kuussaari, M. et al. Extinction debt: a challenge for biodiversity conservation. Trends Ecol. Evol. 24, 564–571 (2009).
PubMed  Google Scholar 

84.
Norberg, J., Urban, M. C., Vellend, M., Klausmeier, C. A. & Loeuille, N. Eco-evolutionary responses of biodiversity to climate change. Nat. Clim. Change 2, 747–751 (2012).
Google Scholar 

85.
Wheeler, H. C., Høye, T. T., Schmidt, N. M., Svenning, J.-C. & Forchhammer, M. C. Phenological mismatch with abiotic conditions—implications for flowering in Arctic plants. Ecology 96, 775–787 (2015).
PubMed  Google Scholar 

86.
Beard, K. H., Kelsey, K. C., Leffler, A. J. & Welker, J. M. The missing angle: ecosystem consequences of phenological mismatch. Trends Ecol. Evol. 34, 885–888 (2019).
PubMed  Google Scholar 

87.
Chamberlain, C. J., Cook, B. I., de Cortazar Atauri, I. G. & Wolkovich, E. M. Rethinking false spring risk. Glob. Change Biol. 25, 2209–2220 (2019).
Google Scholar 

88.
Wolkovich, E. M., Cook, B. I., McLauchlan, K. K. & Davies, T. J. Temporal ecology in the Anthropocene. Ecol. Lett. 17, 1365–1379 (2014).
CAS  PubMed  Google Scholar 

89.
Pagel, J. et al. Mismatches between demographic niches and geographic distributions are strongest in poorly dispersed and highly persistent plant species. Proc. Natl Acad. Sci. USA 117, 3663–3669 (2020).
CAS  PubMed  Google Scholar 

90.
Komatsu, K. J. et al. Global change effects on plant communities are magnified by time and the number of global change factors imposed. Proc. Natl Acad. Sci. USA 116, 17867–17873 (2019).
CAS  PubMed  Google Scholar 

91.
Fadrique, B. et al. Widespread but heterogeneous responses of Andean forests to climate change. Nature 564, 207–212 (2018).
CAS  PubMed  Google Scholar 

92.
Talluto, M. V., Boulangeat, I., Vissault, S., Thuiller, W. & Gravel, D. Extinction debt and colonization credit delay range shifts of eastern North American trees. Nat. Ecol. Evol. 1, 0182 (2017).
Google Scholar 

93.
Zhu, K., Woodall, C. W. & Clark, J. S. Failure to migrate: lack of tree range expansion in response to climate change. Glob. Change Biol. 18, 1042–1052 (2012).
Google Scholar 

94.
Bertrand, R. et al. Changes in plant community composition lag behind climate warming in lowland forests. Nature 479, 517–520 (2011).
CAS  PubMed  PubMed Central  Google Scholar 

95.
Bocsi, T. et al. Plants’ native distributions do not reflect climatic tolerance. Divers. Distrib. 22, 615–624 (2016).
Google Scholar 

96.
Early, R. & Sax, D. F. Climatic niche shifts between species’ native and naturalized ranges raise concern for ecological forecasts during invasions and climate change. Glob. Ecol. Biogeogr. 23, 1356–1365 (2014).
Google Scholar 

97.
Perret, D. L., Leslie, A. B. & Sax, D. F. Naturalized distributions show that climatic disequilibrium is structured by niche size in pines (Pinus L.). Glob. Ecol. Biogeogr. 28, 429–441 (2019).
Google Scholar 

98.
Blonder, B. et al. Linking environmental filtering and disequilibrium to biogeography with a community climate framework. Ecology 96, 972–985 (2015).
PubMed  Google Scholar 

99.
Knight, C. A. et al. Community assembly and climate mismatch in Late-Quaternary eastern North American pollen assemblages. Am. Nat. 195, 166–180 (2020).
PubMed  Google Scholar 

100.
Butterfield, B. J., Anderson, R. S., Holmgren, C. A. & Betancourt, J. L. Extinction debt and delayed colonization have had comparable but unique effects on plant community–climate lags since the Last Glacial Maximum. Glob. Ecol. Biogeogr. 28, 1067–1077 (2019).
Google Scholar 

101.
Graham, R. W. et al. Timing and causes of a middle Holocene mammoth extinction on St. Paul Island, Alaska. Proc. Natl Acad. Sci. USA 113, 9310–9314 (2016).
CAS  PubMed  Google Scholar 

102.
Woods, K. D. & Davis, M. B. Paleoecology of range limits: beech in the Upper Peninsula of Michigan. Ecology 70, 681–696 (1989).
Google Scholar 

103.
Jackson, S. T. et al. Inferring local to regional changes in forest composition from Holocene macrofossils and pollen of a small lake in central Upper Michigan. Quat. Sci. Rev. 98, 60–73 (2014).
Google Scholar 

104.
Seeley, M., Goring, S. & Williams, J. W. Testing hypotheses about environmental and dispersal controls on Fagus grandifolia distributions in the upper Midwest Great Lakes region. J. Biogeogr. 46, 405–419 (2019).
Google Scholar 

105.
Birks, H. J. B. & Birks, H. H. Biological responses to rapid climate change at the Younger Dryas—Holocene transition at Kråkenes, western Norway. Holocene 18, 19–30 (2008).
Google Scholar 

106.
Ammann, B. et al. Vegetation responses to rapid warming and to minor climatic fluctuations during the Late-Glacial Interstadial (GI-1) at Gerzensee (Switzerland). Palaeogeogr. Palaeoclimatol. Palaeoecol. 391, 40–59 (2013).
Google Scholar 

107.
Svenning, J.-C. & Skov, F. Could the tree diversity pattern in Europe be generated by postglacial dispersal limitation? Ecol. Lett. 10, 453–460 (2007).
PubMed  Google Scholar 

108.
Sandel, B. et al. The influence of late Quaternary climate-change velocity on species endemism. Science 334, 660–664 (2011).
CAS  PubMed  Google Scholar 

109.
Feng, G. et al. Species and phylogenetic endemism in angiosperm trees across the Northern Hemisphere are jointly shaped by modern climate and glacial–interglacial climate change. Glob. Ecol. Biogeogr. 28, 1393–1402 (2019).
Google Scholar 

110.
Richardson, D. M. et al. Naturalization and invasion of alien plants: concepts and definitions. Divers. Distrib. 6, 93–107 (2000).
Google Scholar 

111.
Sakai, A. K. et al. The population biology of invasive species. Annu. Rev. Ecol. Syst. 32, 305–332 (2001).
Google Scholar 

112.
Hui, C. & Richardson, D. M. Invasion Dynamics (Oxford Univ. Press, 2017).

113.
Kowarik, I. in Plant Invasions. General Aspects and Special Problems (eds Pysek, P. et al.) 15–39 (SPB Academic Publishing, 1995).

114.
Bruce, K. A., Cameron, G. N. & Harcombe, P. A. Initiation of a new woodland type on the Texas Coastal Prairie by the Chinese tallow tree (Sapium sebiferum (L.) Roxb.). Bull. Torrey Bot. Club 122, 215–225 (1995).
Google Scholar 

115.
Castro, S. A., Figueroa, J. A., Muñoz-Schick, M. & Jaksic, F. M. Minimum residence time, biogeographical origin, and life cycle as determinants of the geographical extent of naturalized plants in continental Chile. Divers. Distrib. 11, 183–191 (2005).
Google Scholar 

116.
Hoffmann, J. H. & Moran, V. C. The invasive weed Sesbania punicea in South Africa and prospects for its biological control. S. Afr. J. Sci. 84, 740–472 (1988).
Google Scholar 

117.
Byers, J. E. et al. Invasion expansion: time since introduction best predicts global ranges of marine invaders. Sci. Rep. 5, 12436 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

118.
Phillips, M. L., Murray, B. R., Leishman, M. R. & Ingram, R. The naturalization to invasion transition: are there introduction-history correlates of invasiveness in exotic plants of Australia? Austral Ecol. 35, 695–703 (2010).
Google Scholar 

119.
Scott, J. K. & Panetta, F. D. Predicting the Australian weed status of southern African plants. J. Biogeogr. 20, 87–93 (1993).
Google Scholar 

120.
Arroyo, M. T. K., Rozzi, R., Simonetti, J. A., Marquet, P. & Sallaberry, M. in Hotspots: Earth’s Biologically Richest and Most Endangered Terrestrial Ecosystems (eds Mittermeier, R. A. et al.) 161–171 (Cemex, Conservation International, 1999).

121.
Zarnetske, P. L., Skelly, D. K. & Urban, M. C. Biotic multipliers of climate change. Science 336, 1516–1518 (2012).
CAS  PubMed  Google Scholar 

122.
Ordonez, A. & Williams, J. W. Projected climate reshuffling based on multivariate climate-availability, climate-analog, and climate-velocity analyses: implications for community disaggregation. Clim. Change 119, 659–675 (2013).
Google Scholar 

123.
Zohner, C. M. et al. Late-spring frost risk between 1959 and 2017 decreased in North America but increased in Europe and Asia. Proc. Natl Acad. Sci. USA 117, 12192–12200 (2020).
CAS  PubMed  Google Scholar 

124.
Renner, S. S. & Zohner, C. M. Climate change and phenological mismatch in trophic interactions among plants, insects, and vertebrates. Annu. Rev. Ecol. Evol. Syst. 49, 165–182 (2018).
Google Scholar 

125.
Turner, M. G. et al. Climate change, ecosystems, and abrupt change: science priorities. Phil. Trans. R. Soc. B 375, 20190105 (2020).
PubMed  Google Scholar 

126.
Rietkerk, M., Dekker, S. C., de Ruiter, P. C. & van de Koppel, J. Self-organized patchiness and catastrophic shifts in ecosystems. Science 305, 1926–1929 (2004).
CAS  PubMed  Google Scholar 

127.
Calder, W. J. & Shuman, B. N. Extensive wildfires, climate change, and an abrupt state change in subalpine ribbon forests, Colorado. Ecology 98, 2585–2600 (2017).
PubMed  Google Scholar 

128.
Shuman, B. N., Marsicek, J., Oswald, W. W. & Foster, D. R. Predictable hydrological and ecological responses to Holocene North Atlantic variability. Proc. Natl Acad. Sci. USA 116, 5985–5990 (2019).
CAS  PubMed  Google Scholar 

129.
Allison, T. D., Moeller, R. E. & Davis, M. B. Pollen in laminated sediments provides evidence of mid-Holocene forest pathogen outbreak. Ecology 67, 1101–1105 (1986).
Google Scholar 

130.
Ramiadantsoa, T., Stegner, M. A., Williams, J. W. & Ives, A. R. The potential role of intrinsic processes in generating abrupt and quasi-synchronous tree declines during the Holocene. Ecology 100, e02579 (2019).
PubMed  Google Scholar 

131.
Seddon, A. W. R., Froyd, C. A., Witkowski, A. & Willis, K. J. A quantitative framework for analysis of regime shifts in a Galápagos coastal lagoon. Ecology 95, 3046–3055 (2014).
Google Scholar 

132.
Gray, S. T., Betancourt, J. L., Jackson, S. J. & Eddy, R. G. Role of multidecadal climatic variability in a range extension of pinyon pine. Ecology 87, 1124–1130 (2006).
PubMed  Google Scholar 

133.
Lyford, M. E., Jackson, S. T., Betancourt, J. L. & Gray, S. T. Influence of landscape structure and climate variability on a late Holocene plant migration. Ecol. Monogr. 73, 567–583 (2003).
Google Scholar 

134.
Tinner, W. & Lotter, A. F. Holocene expansions of Fagus silvatica and Abies alba in Central Europe: where are we after eight decades of debate? Quat. Sci. Rev. 25, 526–549 (2006).
Google Scholar 

135.
Saltré, F. A. et al. Climate or migration: what limited European beech post-glacial colonization? Glob. Ecol. Biogeogr. 22, 1217–1227 (2013).
Google Scholar 

136.
Ruosch, M. et al. Past and future evolution of Abies alba forests in Europe – comparison of a dynamic vegetation model with palaeo data and observations. Glob. Change Biol. 22, 727–740 (2016).
Google Scholar 

137.
Danz, N. P., Frelich, L. E., Reich, P. B. & Niemi, G. J. Do vegetation boundaries display smooth or abrupt spatial transitions along environmental gradients? Evidence from the prairie–forest biome boundary of historic Minnesota, USA. J. Veg. Sci. 24, 1129–1140 (2013).
Google Scholar 

138.
Grimm, E. C. Fire and other factors controlling the Big Woods vegetation of Minnesota in the mid-nineteenth century. Ecol. Monogr. 54, 291–311 (1984).
Google Scholar 

139.
Staver, A. C., Archibald, S. & Levin, S. A. The global extent and determinants of savanna and forest as alternative biome states. Science 334, 230–232 (2011).
CAS  PubMed  Google Scholar 

140.
Thomson, J. A. et al. Extreme temperatures, foundation species, and abrupt ecosystem change: an example from an iconic seagrass ecosystem. Glob. Change Biol. 21, 1463–1474 (2015).
Google Scholar 

141.
Teskey, R. et al. Responses of tree species to heat waves and extreme heat events. Plant Cell Environ. 38, 1699–1712 (2015).
PubMed  Google Scholar 

142.
Hansen, W. D. & Turner, M. G. Origins of abrupt change? Postfire subalpine conifer regeneration declines nonlinearly with warming and drying. Ecol. Monogr. 89, e01340 (2019).
Google Scholar 

143.
Bestelmeyer, B. T. et al. Analysis of abrupt transitions in ecological systems. Ecosphere 2, 129 (2011).
Google Scholar 

144.
Lenton, T. M. Early warning of climate tipping points. Nat. Clim. Change 1, 201–209 (2011).
Google Scholar 

145.
Hastings, A. & Wysham, D. B. Regime shifts in ecological systems can occur with no warning. Ecol. Lett. 13, 464–472 (2010).
PubMed  Google Scholar 

146.
Boettiger, C. & Hastings, A. Quantifying limits to detection of early warning for critical transitions. J. R. Soc. Interface 9, 2527–2539 (2012).
PubMed  PubMed Central  Google Scholar 

147.
Millar, C. I., Stephenson, N. L. & Stephens, S. L. Climate change and forests of the future: managing in the face of uncertainty. Ecol. Appl. 17, 2145–2151 (2007).
PubMed  Google Scholar 

148.
Choi, Y. D. Restoration ecology to the future: a call for new paradigm. Restor. Ecol. 15, 351–353 (2007).
Google Scholar 

149.
Corlett, R. T. Restoration, reintroduction, and rewilding in a changing world. Trends Ecol. Evol. 31, 453–462 (2016).
PubMed  Google Scholar 

150.
Sprugel, D. G. Disturbance, equilibrium, and environmental variability: what is ‘Natural’ vegetation in a changing environment? Biol. Conserv. 58, 1–18 (1991).
Google Scholar 

151.
Perino, A. et al. Rewilding complex ecosystems. Science 364, eaav5570 (2019).
CAS  PubMed  Google Scholar 

152.
Jackson, S. T. & Hobbs, R. J. Ecological restoration in the light of ecological history. Science 325, 567–569 (2009).
CAS  PubMed  Google Scholar 

153.
Radeloff, V. C. et al. The rise of novelty in ecosystems. Ecol. Appl. 25, 2051–2068 (2015).
PubMed  Google Scholar 

154.
Truitt, A. M. et al. What is novel about novel ecosystems: managing change in an ever-changing world. Environ. Manag. 55, 1217–1226 (2015).
Google Scholar 

155.
Murcia, C. et al. A critique of the ‘novel ecosystem’ concept. Trends Ecol. Evol. 29, 548–553 (2014).
PubMed  Google Scholar 

156.
Ricciardi, A. & Simberloff, D. Assisted colonization is not a viable conservation strategy. Trends Ecol. Evol. 24, 248–253 (2009).
PubMed  Google Scholar 

157.
Svenning, J.-C. Proactive conservation and restoration of botanical diversity in the Anthropocene’s “rambunctious garden”. Am. J. Bot. 105, 963–966 (2018).
PubMed  Google Scholar 

158.
Jepson, P. Recoverable Earth: a twenty-first century environmental narrative. Ambio 48, 123–130 (2019).
PubMed  Google Scholar 

159.
Hoegh-Guldberg, O. et al. Assisted colonization and rapid climate change. Science 321, 345–346 (2008).
CAS  PubMed  Google Scholar 

160.
van Oppen, M. J. H., Oliver, J. K., Putnam, H. M. & Gates, R. D. Building coral reef resilience through assisted evolution. Proc. Natl Acad. Sci. USA 112, 2307–2313 (2015).
PubMed  Google Scholar 

161.
Willis, K. J. & MacDonald, G. M. Long-term ecological records and their relevance to climate change predictions for a warmer world. Annu. Rev. Ecol. Evol. Syst. 42, 267–287 (2011).
Google Scholar 

162.
Farley, S. S., Dawson, A., Goring, S. J. & Williams, J. W. Situating ecology as a big data science: Current advances, challenges, and solutions. BioScience 68, 563–576 (2018).
Google Scholar 

163.
Brown, T. B. et al. Using phenocams to monitor our changing Earth: toward a global phenocam network. Front. Ecol. Environ. 14, 84–93 (2016).
Google Scholar 

164.
Clark, J. S. et al. Ecological forecasts: an emerging imperative. Science 293, 657–660 (2001).
CAS  PubMed  Google Scholar 

165.
Dietze, M. C. et al. Iterative near-term ecological forecasting: needs, opportunities, and challenges. Proc. Natl Acad. Sci. USA 115, 1424–1432 (2018).
CAS  PubMed  Google Scholar 

166.
Dietze, M. C. Ecological Forecasting (Princeton Univ. Press, 2017).

167.
Bauer, P., Thorpe, A. & Brunet, G. The quiet revolution of numerical weather prediction. Nature 525, 47–55 (2015).
CAS  PubMed  Google Scholar 

168.
Thomas, S. M., Griffiths, S. W. & Ormerod, S. J. Adapting streams for climate change using riparian broadleaf trees and its consequences for stream salmonids. Freshw. Biol. 60, 64–77 (2015).
Google Scholar 

169.
Greenwood, O., Mossman, H. L., Suggitt, A. J., Curtis, R. J. & Maclean, I. M. D. Using in situ management to conserve biodiversity under climate change. J. Appl. Ecol. 53, 885–894 (2016).
PubMed  PubMed Central  Google Scholar 

170.
Carpenter, S. R. & Turner, M. G. Hares and tortoises: interactions of fast and slow variables in ecosystems. Ecosystems 3, 495–497 (2000).
Google Scholar 

171.
Harris, I., Jones, P., Osborn, T. & Lister, D. Updated high-resolution grids of monthly climatic observations–the CRU TS3.10 dataset. Int. J. Climatol. 34, 623–642 (2014).
Google Scholar 

172.
Bureau of Reclamation Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections: Release of Hydrology Projections, Comparison with Preceding Information, and Summary of User Needs (US Department of the Interior, Bureau of Reclamation, Technical Services Center, 2014).

173.
Delcourt, H. R. & Delcourt, P. A. Quaternary Ecology: A Paleoecological Perspective (Chapman & Hall, 1991).

174.
IPCC in Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) 1–32 (Cambridge Univ. Press, 2014).

175.
Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).
CAS  PubMed  Google Scholar 

176.
McDowell, P. F., Webb, T. III & Bartlein, P. J. in The Earth as Transformed by Human Action (eds Turner, B. L. II et al.) 143–162 (Cambridge Univ. Press, 1990).

177.
Delcourt, P. A. & Delcourt, H. R. Long-Term Forest Dynamics of the Temperate Zone: A Case Study of Late-Quaternary Forests in Eastern North America (Springer-Verlag, 1987).

178.
Turner, M. G., Dale, V. H. & Gardner, R. H. Predicting across scales: theory development and testing. Landsc. Ecol. 3, 245–252 (1989).
Google Scholar 

179.
Kidwell, S. M. Biology in the Anthropocene: challenges and insights from young fossil records. Proc. Natl Acad. Sci. USA 12, 4922–4929 (2015).
Google Scholar 

180.
National Research Council Abrupt Climate Change: Inevitable Surprises (National Academy Press, 2002).

181.
Rahmstorf, S. in Encyclopedia of Ocean Sciences (eds Steele, J. et al.) 1–6 (Academic Press, 2001).

182.
Scheffer, M., Carpenter, S., Foley, J. A., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413, 591–596 (2001).
CAS  PubMed  Google Scholar 

183.
Staver, A. C., Archibald, S. & Levin, S. Tree cover in sub-Saharan Africa: rainfall and fire constrain forest and savanna as alternative stable states. Ecology 92, 1063–1072 (2011).
PubMed  Google Scholar 

184.
Andersen, T., Carstensen, J., Hernández-Garcia, E. & Duarte, C. M. Ecological thresholds and regime shifts: approaches to identification. Trends Ecol. Evol. 24, 49–57 (2009).
PubMed  Google Scholar 

185.
Scheffer, M. & Carpenter, S. R. Catastrophic regime shifts in ecosystems: linking theory to observation. Trends Ecol. Evol. 18, 648–656 (2003).
Google Scholar 

186.
Claussen, M. Late Quaternary vegetation-climate feedbacks. Clim. Past 5, 203–216 (2009).
Google Scholar 

187.
Liu, Z., Notaro, M. & Gallimore, R. Indirect vegetation-soil moisture feedback with application to Holocene North Africa climate. Glob. Change Biol. 16, 1733–1743 (2010).
Google Scholar  More

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.
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