Ecology

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.
García-Roger, E. M., Carmona, M. J. & Serra, M. Facing adversity: Dormant embryos in rotifers. Biol. Bull. 237, 119–144 (2019).
PubMed  Article  CAS  PubMed Central  Google Scholar 
2.
Denlinger, D. L. Regulation of diapause. Annu. Rev. Entomol. 47, 93–122 (2002).
CAS  PubMed  Article  PubMed Central  Google Scholar 

3.
Reynolds, J. A. & Hand, S. C. Embryonic diapause highlighted by differential expression of mRNAs for ecdysteroidogenesis, transcription and lipid sparing in the cricket Allonemobius socius. J. Exp. Biol. 212, 2075–2084 (2009).
CAS  PubMed  PubMed Central  Article  Google Scholar 

4.
Ricci, C. Dormancy patterns in rotifers. Hydrobiologia 446(447), 1–11 (2001).
Article  Google Scholar 

5.
Poelchau, M. F., Reynolds, J. A., Elsik, C. G., Denlinger, D. L. & Armbruster, P. A. Deep sequencing reveals complex mechanisms of diapause preparation in the invasive mosquito, Aedes albopictus. Proc. R. Soc. B. 280, 20130143 (2013).
PubMed  Article  PubMed Central  Google Scholar 

6.
Alekseev, V. R., De Stasio, B. T., Gilbert, J. J. & Ravera, O. Preface. In Diapause in Aquatic Invertebrates, Theory and Human Use (eds Alekseev, V. R. et al.) xiii–xvi (Springer, New York, 2007).
Google Scholar 

7.
Hand, S. C. & Podrabsky, J. E. Bioenergetics of diapause and quiescence in aquatic animals. Thermochim. Acta 349, 31–42 (2000).
CAS  Article  Google Scholar 

8.
Ślusarczyk, M., Chlebicki, W., Pijanowska, J. & Radzikowski, J. The role of the refractory period in diapause length determination in a freshwater crustacean. Sci. Rep. 9, 11905 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

9.
Tauber, M. J., Tauber, C. A. & Masaki, S. Seasonal Adaptations of Insects (Oxford University Press, Oxford, 1986).
Google Scholar 

10.
Alekseev, V. R., De Stasio, B. T. & Gilbert, J. J. Diapause in Aquatic Invertebrates, Theory and Human Use (Springer, New York, 2012).
Google Scholar 

11.
García-Roger, E. M., Carmona, M. J. & Serra, M. Modes, mechanisms and evidence of bet hedging in rotifer diapause traits. Hydrobiologia 796, 223–233 (2017).
Article  Google Scholar 

12.
Cohen, D. Optimizing reproduction in a randomly varying environment. J. Theor. Biol. 12, 119–129 (1966).
CAS  PubMed  Article  Google Scholar 

13.
Seger, J. & Brockmann, H. J. What is bet-hedging? In Oxford Surveys in Evolutionary Biology Vol. 4 (eds Harvey, P. H. & Partridge, L.) 182–211 (Oxford University Press, Oxford, 1987).
Google Scholar 

14.
Philippi, T. & Seger, J. Hedging one’s evolutionary bets, revisited. Trends Ecol. Evol. 4, 41–44 (1989).
CAS  PubMed  Article  Google Scholar 

15.
Simons, A. M. Modes of response to environmental change and the elusive empirical evidence for bet hedging. Proc. R. Soc. B Biol. Sci. 278, 1601–1609 (2011).
Article  Google Scholar 

16.
Menu, F. & Desouhant, E. Bet-hedging for variability in life cycle duration: bigger and later-emerging chestnut weevils have increased probability of a prolonged diapause. Oecologia 132, 167–174 (2002).
ADS  PubMed  Article  Google Scholar 

17.
Franch-Gras, L., García-Roger, E. M., Serra, M. & Carmona, M. J. Adaptation in response to environmental unpredictability. Proc. R. Soc. B Biol. Sci. 284, 20170427 (2017).
Article  CAS  Google Scholar 

18.
Tarazona, E., García-Roger, E. M. & Carmona, M. J. Experimental evolution of bet hedging in rotifer diapause traits as a response to environmental unpredictability. Oikos 126, 1162–1172 (2017).
Article  Google Scholar 

19.
Koštál, V. Eco-physiological phases of insect diapause. J. Insect Physiol. 52, 113–127 (2006).
PubMed  Article  CAS  Google Scholar 

20.
Tammariello, S. P. & Denlinger, D. L. G0/G1 cell cycle arrest in the brain of Sarcophaga crassipalpis during pupal diapause and the expression pattern of the cell cycle regulator, proliferating cell nuclear antigen. Insect. Biochem. Mol. Biol. 28, 83–89 (1998).
CAS  PubMed  Article  Google Scholar 

21.
Denekamp, N. Y., Reinhardt, R., Kube, M. & Lubzens, E. Late embryogenesis abundant (LEA) proteins in nondesiccated, encysted, and diapausing embryos of rotifers. Biol. Repr. 82, 714–724 (2010).
CAS  Article  Google Scholar 

22.
Qiu, Z. & MacRae, T. H. A molecular overview of diapause in embryos of the crustacean, Artemia franciscana. In Dormancy and Resistance in Harsh Environments (eds Lubzens, E. et al.) 165–188 (Springer, New York, 2010).
Google Scholar 

23.
Ziv, T. et al. Dormancy in embryos: Insight from hydrated encysted embryos of an aquatic invertebrate. Mol. Cell. Proteomics 16, 1746–1769 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

24.
Roncalli, V. et al. Physiological characterization of the emergence from diapause: A transcriptomics approach. Sci. Rep. 8, 12577 (2018).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

25.
Rozema, E. et al. Metabolomics reveals novel insight on dormancy of aquatic invertebrate encysted embryos. Sci. Rep. 9, 8878 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

26.
Vanvlasselaer, E. & De Meester, L. An exploratory review on the molecular mechanisms of diapause termination in the waterflea. In Daphnia in Dormancy and Resistance in Harsh Environments (eds Lubzens, E. et al.) 189–202 (Springer, New York, 2010).
Google Scholar 

27.
Declerck, S. A. J. & Papakostas, S. Monogonont rotifers as model systems for the study of micro-evolutionary adaptation and its eco-evolutionary implications. Hydrobiologia 796, 131–144 (2017).
Article  Google Scholar 

28.
Serra, M., García-Roger, E. M., Ortells, R. & Carmona, M. J. Cyclically parthenogenetic rotifers and the theories of population and evolutionary ecology. Limnetica 38, 67–93 (2019).
Google Scholar 

29.
García-Roger, E. M., Serra, M. & Carmona, M. J. Bet-hedging in diapausing egg hatching of temporary rotifer populations—A review of models and new insights. Int. Rev. Hydrobiol. 99, 96–106 (2014).
Article  Google Scholar 

30.
Ricci, C. & Pagani, M. Desiccation of Panagrolaimus rigidus (Nematoda): Survival, reproduction and the influence on the internal clock. Hydrobiologia 347, 1–13 (1997).
Article  Google Scholar 

31.
Gordon, G. & Headrick, D. H. A Dictionary of Entomology (Oxford CABI Publ Series, Oxford, 2001).
Google Scholar 

32.
Fan, L., Lin, J., Zhong, Y. & Liu, J. Shotgun proteomic analysis on the diapause and nondiapause eggs of domesticated silkworm Bombyx mori. PLoS ONE 8, e60386 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
Schröder, T. Diapause in monogonont rotifers. Hydrobiologia 546, 291–306 (2005).
Article  Google Scholar 

34.
Denekamp, N. Y. et al. Discovering genes associated with dormancy in the monogonont rotifer Brachionus plicatilis. BMC Genomics 10, 108 (2009).
PubMed  PubMed Central  Article  CAS  Google Scholar 

35.
Denekamp, N. Y. et al. The expression pattern of dormancy-associated genes in multiple life-history stages in the rotifer Brachionus plicatilis. Hydrobiologia 662, 51–63 (2011).
CAS  Article  Google Scholar 

36.
Clark, M. S. et al. Long-term survival of hydrated resting eggs from Brachionus plicatilis. PLoS ONE 7, e29365 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

37.
Waterworth, W. M., Bray, C. M. & West, C. E. The importance of safeguarding genome integrity in germination and seed longevity. J. Exp. Bot. 66, 3549–3558 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

38.
Sim, C. & Denlinger, D. L. Catalase and superoxide dismutase-2 enhance survival and protect ovaries during overwintering diapause in the mosquito Culex pipiens. J. Insect Physiol. 57, 628–634 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

39.
Ragland, G. J., Denlinger, D. L. & Hahn, D. A. Mechanisms of suspended animation are revealed by transcript profiling of diapause in the flesh fly. Proc. Natl. Acad. Sci. USA 107, 14909–14914 (2010).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
Duceppe, M. O. et al. Analysis of survival and hatching transcriptomes from potato cyst nematodes, Globodera rostochiensis and G. pallida. Sci. Rep. 7, 3882 (2017).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

41.
Wise, M. J. & Tunnacliffe, A. POPP the question: What do LEA proteins do?. Trends Plant. Sci. 9, 13–17 (2004).
CAS  PubMed  Article  PubMed Central  Google Scholar 

42.
García-Roger, E. M. & Ortells, R. Trade-offs in rotifer diapausing egg traits: Survival, hatching, and lipid content. Hydrobiologia 805, 339–350 (2018).
Article  CAS  Google Scholar 

43.
Hand, S. C., Menze, M. A., Toner, M., Boswell, L. & Moore, D. LEA proteins during water stress: Not just for plants anymore. Annu. Rev. Physiol. 73, 115–134 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

44.
Crowe, J. H. et al. The trehalose myth revisited: Introduction to a symposium on stabilization of cells in the dry state. Cryobiology 43, 89–105 (2001).
CAS  PubMed  Article  PubMed Central  Google Scholar 

45.
Moore, D. S. & Hand, S. C. Cryopreservation of lipid bilayers by LEA proteins from Artemia franciscana and trehalose. Cryobiology 73, 240–247 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

46.
Clegg, J. S. Origin of trehalose and its significance during formation of encysted dormant embryos of Artemia Salina. Comp. Biochem. Physiol. 14, 135–143 (1965).
CAS  PubMed  Article  PubMed Central  Google Scholar 

47.
Caprioli, M. et al. Trehalose in desiccated rotifers: A comparison between a bdelloid and a monogonont species. Comp. Biochem. Physiol. 139, 527–532 (2004).
Article  CAS  Google Scholar 

48.
Li, T., Liu, L., Zhang, L. & Liu, N. Role of G-protein-coupled receptor-related genes in insecticide resistance of the mosquito, Culex quinquefasciatus. Sci. Rep. 4, 6474 (2015).
Article  CAS  Google Scholar 

49.
Hommaa, T. et al. G protein-coupled receptor for diapause hormone, an inducer of Bombyx embryonic diapause. Biochem. Biophys. Res. Comm. 344, 386–393 (2006).
Article  CAS  Google Scholar 

50.
Jones, S. J. et al. Changes in gene expression associated with developmental arrest and longevity in Caenorhabditis elegans. Genome Res. 11, 1346–1352 (2001).
CAS  PubMed  Article  Google Scholar 

51.
Fielenbach, N. & Antebi, A. C. elegans dauer formation and the molecular basis of plasticity. Genes Dev. 15, 2149–2165 (2008).
Article  CAS  Google Scholar 

52.
Hand, S. C., Denlinger, D. L., Podrabsky, J. E. & Roy, R. Mechanisms of animal diapause: recent developments from nematodes, crustaceans, insects, and fish. Am. J. Physiol. Regul. Integr. Comp. Physiol. 310, R1193–R1211 (2016).
PubMed  PubMed Central  Article  Google Scholar 

53.
Woll, S. C. & Podrabsky, J. E. Insulin-like growth factor signaling regulates developmental trajectory associated with diapause in embryos of the annual killifish Austrofundulus limnaeus. J. Exp. Biol. 220, 2777–2786 (2017).
PubMed  Article  Google Scholar 

54.
Yu, C. T. & Hirsh, D. The stimulatory effect of ammonium or potassium ions on the activity of leucyl-tRNA synthetase from Escherichia coli. Biochim. Biophys. Acta 142, 149–154 (1967).
CAS  PubMed  Article  Google Scholar 

55.
Beck, S. D., Shane, J. L. & Garland, J. A. Ammonium-induced termination of diapause in the European corn borer, Ostrinia nubilalis. J. Insect. Physiol. 15, 945–951 (1969).
CAS  Article  Google Scholar 

56.
Birnbaumer, L. Expansion of signal transduction by G proteins. The second 15 years or so: From 3 to 16 alpha subunits plus betagamma dimers. Biochim. Biophys. Acta 1768, 772–793 (2007).
CAS  PubMed  Article  Google Scholar 

57.
Dumont, H., Casier, P., Munuswamy, N. & De Wasche, C. Cyst hatching in Anostraca accelerated by retinoic acid, amplified by calcium ionosphore A23187, and inhibited by calcium-channel blockers. Hydrobiologia 230, 1–7 (1992).
CAS  Article  Google Scholar 

58.
Kim, H. J. et al. Light-dependent transcriptional events during resting egg hatching of the rotifer Brachionus manjavacas. Mar. Genomics 20, 25–31 (2015).
PubMed  Article  Google Scholar 

59.
Boschetti, C., Ricci, C., Sotgia, C. & Fascio, U. The development of a bdelloid egg: A contribution after 100 years. Hydrobiologia 546, 323–331 (2005).
Article  Google Scholar 

60.
Bonneau, B., Popgeorgiev, N., Prudent, J. & Gillet, G. Cytoskeleton dynamics in early zebrafish development. A matter of phosphorylation?. Bioarchitecture 1, 216–220 (2011).
PubMed  PubMed Central  Article  Google Scholar 

61.
Eno, C., Solanki, B. & Pelegri, F. Aura (mid1ip1l) regulates the cytoskeleton at the zebrafish egg-to-embryo transition. Development 143, 1585–1599 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

62.
Cáceres, C. E. & Schwalbach, M. S. How well do laboratory experiments explain field patterns of zooplankton emergence?. Freshw. Biol. 46, 1179–1189 (2001).
Article  Google Scholar 

63.
De Stasio, B. T. Diapause in calanoid copepods: Within-clutch hatching patterns. J. Limnol. 63, 26–31 (2004).
Article  Google Scholar 

64.
García-Roger, E. M., Carmona, M. J. & Serra, M. Patterns in rotifer diapausing egg banks: Density and viability. J. Exp. Mar. Biol. Ecol. 336, 198–210 (2006).
Article  Google Scholar 

65.
Helland, S., Nejstgaard, C., Fyhn, J. J., Egge, J. K. & Båmstedt, U. Effects of starvation, season, and diet on the free amino acid and protein content of Calanus finmarchicus females. Mar. Biol. 143, 297–306 (2003).
CAS  Article  Google Scholar 

66.
Skottene, E. et al. The β-oxidation pathway is downregulated during diapause termination in Calanus copepods. Sci. Rep. 9, 16686 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

67.
Tan, Q., Liu, W., Zhu, F., Lei, C. & Wang, X. Fatty acid synthase 2 contributes to diapause preparation in a beetle by regulating lipid accumulation and stress tolerance genes expression. Sci. Rep. 7, 40509 (2016).
ADS  Article  CAS  Google Scholar 

68.
Gilbert, J. J. & Schröder, T. Rotifers from diapausing, fertilized eggs: Unique features and emergence. Limnol. Oceanogr. 49, 1341–1354 (2004).
ADS  Article  Google Scholar 

69.
Alekseev, V. R., Hwang, J.-S. & Tseng, M.-H. Diapause in aquatic invertebrates: What’s known and what’s next in research and medical application?. J. Mar. Sci. Tech. 14, 269–286 (2006).
Google Scholar 

70.
Gilbert, J. J. Timing of diapause in monogonont rotifers. In Mechanisms and Strategies in Diapause in Aquatic Invertebrates. Theory and Human Use (eds Alekseev, V. R. et al.) 11–27 (Springer, New York, 2012).
Google Scholar 

71.
Koštál, V., Štětina, T., Poupardin, R., Korbelová, J. & Bruce, A. W. Conceptual framework of the eco-physiological phases of insect diapause development justified by transcriptomic profiling. Proc. Natl. Acad. Sci. USA 114, 8532–8537 (2017).
PubMed  Article  CAS  PubMed Central  Google Scholar 

72.
Podrabsky, J. E. & Hand, S. C. Physiological strategies during animal diapause: Lessons from brine shrimp and annual killifish. J. Exp. Biol. 218, 1897–1906 (2015).
PubMed  PubMed Central  Article  Google Scholar 

73.
Zahradka, K. et al. Reassembly of shattered chromosomes in Deinococcus radiodurans. Nature 443, 569–573 (2006).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

74.
Gladyshev, E. & Meselson, M. Extreme resistance of bdelloid rotifers to ionizing radiation. Proc. Natl. Acad. Sci. USA 105, 5139–5144 (2008).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

75.
Kim, R. O. et al. Ultraviolet B retards growth, induces oxidative stress, and modulates DNA repair-related gene and heat shock protein gene expression in the monogonont rotifer, Brachionus sp. Aquat. Toxicol. 101, 529–539 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

76.
Han, J. et al. Sublethal gamma irradiation affects reproductive impairment and elevates antioxidant enzyme and DNA repair activities in the monogonont rotifer Brachionus koreanus. Aquat Toxicol. 155, 101–109 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

77.
Hagiwara, A., Hoshi, N., Kawahara, F., Tominaga, K. & Hirayama, K. Resting eggs of the marine rotifer Brachionus plicatilis Müller: Development and effect of irradiation on hatching. Hydrobiologia 313(314), 223–229 (1995).
Article  Google Scholar 

78.
IPCC. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, Cambridge, 2013).
Google Scholar 

79.
Pourriot, R. & Snell, T. W. Resting eggs of rotifers. Hydrobiologia 104, 213–224 (1983).
Article  Google Scholar 

80.
Altman, N. & Krzywinski, M. Split plot design. Nat. Meth. 12, 165–166 (2015).
CAS  Article  Google Scholar 

81.
Nelder, J. A. & Wedderburn, R. W. M. Generalized linear models. J. Roy. Stat. Soc. Ser. A 135, 370–384 (1972).
Article  Google Scholar 

82.
Cox, D. R. Regression models and life-tables (with discussion). J. R. Statist. Soc. B 34, 187–220 (1972).
MATH  Google Scholar 

83.
R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/ (2017).

84.
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Article  Google Scholar 

85.
Therneau, T. M. & Grambsch, P. M. Modeling Survival Data: Extending the Cox Model (Springer, New York, 2020).
Google Scholar 

86.
Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

87.
Franch-Gras, L. et al. Genomic signatures of local adaptation to the degree of environmental unpredictability in rotifers. Sci. Rep. 8, 16051 (2018).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

88.
Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotech. 28, 511–515 (2010).
CAS  Article  Google Scholar 

89.
Li, B. & Dewey, C. N. RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

90.
Hoffman, G. E. & Schadt, E. E. VariancePartition: Interpreting drivers of variation in complex gene expression studies. BMC Bioinformatics 17, 483 (2016).
PubMed  PubMed Central  Article  Google Scholar 

91.
Ritchie, M. E. et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

92.
Benjamini, Y. & Hochberg, Y. On the adaptive control of the false discovery rate in multiple testing with independent statistics. J. Educ. Behav. Stat. 25, 60–83 (2000).
Article  Google Scholar 

93.
Gianetto, G. Q. et al. Calibration plot for proteomics: A graphical tool to visually check the assumptions underlying FDR control in quantitative experiments. Proteomics 16, 29–32 (2016).
Article  CAS  Google Scholar 

94.
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
PubMed  PubMed Central  Article  CAS  Google Scholar 

95.
McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nuc. Acids Res. 10, 4288–4297 (2012).
Article  CAS  Google Scholar 

96.
Witten, D. Classification and clustering of sequencing data using a Poisson model. Ann. Appl. Stat. 5, 2493–2518 (2011).
MathSciNet  MATH  Article  Google Scholar 

97.
Anderson, M. J. Distance-based tests for homogeneity of multivariate dispersions. Biometrics 62, 245–253 (2006).
MathSciNet  PubMed  MATH  Article  Google Scholar 

98.
Sims, D. et al. CGAT: Computational genomics analysis toolkit. Bioinformatics 30, 1290–1291 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

99.
Jones, P. et al. InterProScan 5: Genome-scale protein function classification. Bioinformatics 30, 1236–1240 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

100.
Alexa, A. & Rahnenführer, J. TopGO: Enrichment analysis for gene ontology. R package version 2.40.0. Bioconductor https://doi.org/10.18129/B9.bioc.topGO (2020).
Article  Google Scholar 

101.
Hanson, S. J., Stelzer, C.-P., Welch, D. B. & Logsdon, J. Comparative transcriptome analysis of obligately asexual and cyclically sexual rotifers reveals genes with putative functions in sexual reproduction, dormancy, and asexual egg production. BMC Genomics 14, 412 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar  More

1.
Osborn, D., Cutter, A. & Ullah, F. in Stakeholder Forum, Commissioned by the UN Development Program. Geneva, Switzerland.
2.
Cain, M., Bowman, W. & Hacker, S. Ecology 3rd edn. (Sinauer Associates Inc., Sunderland, 2014).
Google Scholar 

3.
Donohue, I. et al. On the dimensionality of ecological stability. Ecol. Lett. 16, 421–429 (2013).
PubMed  PubMed Central  Article  Google Scholar 

4.
Hillebrand, H. et al. Decomposing multiple dimensions of stability in global change experiments. Ecol. Lett. 21, 21–30 (2018).
PubMed  PubMed Central  Article  Google Scholar 

5.
Briones, A. & Raskin, L. Diversity and dynamics of microbial communities in engineered environments and their implications for process stability. Curr. Opin. Biotechnol. 14, 270–276 (2003).
CAS  PubMed  Article  PubMed Central  Google Scholar 

6.
Wang, Q., Ding, C., Tao, G. & He, J. Analysis of enhanced nitrogen removal mechanisms in a validation wastewater treatment plant containing anammox bacteria. Appl. Microbiol. Biotechnol. 103, 1255–1265 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

7.
Zhou, J. & Ning, D. Stochastic community assembly: does it matter in microbial ecology?. Microbiol. Mol. Biol. Rev. 81, 1–32 (2017).
Article  Google Scholar 

8.
Santillan, E., Seshan, H., Constancias, F., Drautz-Moses, D. I. & Wuertz, S. Frequency of disturbance alters diversity, function, and underlying assembly mechanisms of complex bacterial communities. NPJ Biofilms Microbiomes 5, 1–8 (2019).
Article  Google Scholar 

9.
Prosser, J. I. Replicate or lie. Environ. Microbiol. 12, 1806–1810 (2010).
CAS  PubMed  Article  Google Scholar 

10.
Bender, E. A., Case, T. J. & Gilpin, M. E. Perturbation experiments in community ecology: theory and practice. Ecology 65, 1–13 (1984).
Article  Google Scholar 

11.
Shade, A. et al. Fundamentals of microbial community resistance and resilience. Front. Microbiol. 3, 1–19 (2012).
ADS  Article  Google Scholar 

12.
Botton, S., van Heusden, M., Parsons, J. R., Smidt, H. & van Straalen, N. Resilience of microbial systems towards disturbances. Crit. Rev. Microbiol. 32, 101–112 (2006).
CAS  PubMed  Article  Google Scholar 

13.
Rykiel, E. J. Towards a definition of ecological disturbance. Aust. J. Ecol. 10, 361–365 (1985).
Article  Google Scholar 

14.
Hu, B., Wheatley, A., Ishtchenko, V. & Huddersman, K. The effect of shock loads on SAF bioreactors for sewage treatment works. Chem. Eng. J. 166, 73–80 (2011).
CAS  Article  Google Scholar 

15.
Bassin, J. P. et al. Effect of increasing organic loading rates on the performance of moving-bed biofilm reactors filled with different support media: assessing the activity of suspended and attached biomass fractions. Process Saf. Environ. Prot. 100, 131–141 (2016).
CAS  Article  Google Scholar 

16.
Seetha, N., Bhargava, R. & Kumar, P. Effect of organic shock loads on a two-stage activated sludge-biofilm reactor. Bioresour. Technol. 101, 3060–3066 (2010).
CAS  PubMed  Article  Google Scholar 

17.
Ketheesan, B. & Stuckey, D. C. Effects of hydraulic/organic shock/transient loads in anaerobic wastewater treatment: a review. Crit. Rev. Environ. Sci. Technol. 45, 2693–2727 (2015).
CAS  Article  Google Scholar 

18.
Senturk, E., Ince, M. & Onkal Engin, G. The effect of shock loading on the performance of a thermophilic anaerobic contact reactor at constant organic loading rate. J. Environ. Health Sci. Eng. 12, 1–6 (2014).
Article  CAS  Google Scholar 

19.
Gray, N. F. Biology of Wastewater Treatment 2nd edn, Vol. 4 (Imperial College Press, London, 2004).
Google Scholar 

20.
Laureni, M. et al. Mainstream partial nitritation and anammox: long-term process stability and effluent quality at low temperatures. Water Res. 101, 628–639 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

21.
Wang, Q. & He, J. Newly designed high-coverage degenerate primers for nitrogen removal mechanism analysis in a partial nitrification-anammox (PN/A) process. FEMS Microbiol. Ecol. 96, fiz202 (2019).
Article  Google Scholar 

22.
Ma, B. et al. Suppressing nitrite-oxidizing bacteria growth to achieve nitrogen removal from domestic wastewater via anammox using intermittent aeration with low dissolved oxygen. Sci. Rep. 5, 1–9 (2015).
Google Scholar 

23.
Sinha, B. & Annachhatre, A. P. Partial nitrification—operational parameters and microorganisms involved. Rev. Environ. Sci. Bio. Technol. 6, 285–313 (2007).
CAS  Article  Google Scholar 

24.
Okabe, S., Oozawa, Y., Hirata, K. & Watanabe, Y. Relationship between population dynamics of nitrifiers in biofilms and reactor performance at various C:N ratios. Water Res. 30, 1563–1572 (1996).
CAS  Article  Google Scholar 

25.
Ge, S. et al. Detection of nitrifiers and evaluation of partial nitrification for wastewater treatment: a review. Chemosphere 140, 85–98 (2015).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

26.
Ma, J. et al. Analysis of nitrification efficiency and microbial community in a membrane bioreactor fed with low COD/N-ratio wastewater. PLoS ONE 8, 1–10 (2013).
Article  Google Scholar 

27.
Tan, C., Ma, F. & Qiu, S. Impact of carbon to nitrogen ratio on nitrogen removal at a low oxygen concentration in a sequencing batch biofilm reactor. Water Sci. Technol. 67, 612–618 (2012).
Article  CAS  Google Scholar 

28.
Zhang, T. et al. Achieving partial nitrification in a continuous post-denitrification reactor treating low C/N sewage. Chem. Eng. J. 335, 330–337 (2018).
CAS  Article  Google Scholar 

29.
She, Z. et al. Partial nitrification and denitrification in a sequencing batch reactor treating high-salinity wastewater. Chem. Eng. J. 288, 207–215 (2016).
CAS  Article  Google Scholar 

30.
Regmi, P. et al. Control of aeration, aerobic SRT and COD input for mainstream nitritation/denitritation. Water Res. 57, 162–171 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

31.
Ge, S., Peng, Y., Qiu, S., Zhu, A. & Ren, N. Complete nitrogen removal from municipal wastewater via partial nitrification by appropriately alternating anoxic/aerobic conditions in a continuous plug-flow step feed process. Water Res. 55, 95–105 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

32.
Jiang, H. et al. A pilot-scale study on start-up and stable operation of mainstream partial nitrification-anammox biofilter process based on online pH-DO linkage control. Chem. Eng. J. 350, 1035–1042 (2018).
CAS  Article  Google Scholar 

33.
Pimm, S. L. The complexity and stability of ecosystems. Nature 307, 321 (1984).
ADS  Article  Google Scholar 

34.
Santillan, E., Constancias, F. & Wuertz, S. Press disturbance alters community structure and assembly mechanisms of bacterial taxa and functional genes in mesocosm-scale bioreactors. mSystems 5, e00471–e00420 (2020).
PubMed  PubMed Central  Article  Google Scholar 

35.
Nowka, B., Daims, H. & Spieck, E. Comparison of oxidation kinetics of nitrite-oxidizing bacteria: nitrite availability as a key factor in niche differentiation. Appl. Environ. Microbiol. 81, 745–753 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

36.
Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639–2643 (2017).
PubMed  PubMed Central  Article  Google Scholar 

37.
Okabe, S., Aoi, Y., Satoh, H. & Suwa, Y. Nitrification. In Nitrification in Wastewater Treatment (eds Ward, B. B. et al.) 405–418 (ASM Press, Washington, DC, 2011).
Google Scholar 

38.
Blackburne, R., Yuan, Z. & Keller, J. Partial nitrification to nitrite using low dissolved oxygen concentration as the main selection factor. Biodegradation 19, 303–312 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

39.
Garrido, J. M., van Benthum, W. A. J., van Loosdrecht, M. C. M. & Heijnen, J. J. Influence of dissolved oxygen concentration on nitrite accumulation in a biofilm airlift suspension reactor. Biotechnol. Bioeng. 53, 168–178 (1997).
CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
Almstrand, R., Daims, H., Persson, F., Sörensson, F. & Hermansson, M. New methods for analysis of spatial distribution and coaggregation of microbial populations in complex biofilms. Appl. Environ. Microbiol. 79, 5978–5987 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

41.
Law, Y. et al. High dissolved oxygen selection against nitrospira sublineage I in full-scale activated sludge. Environ. Sci. Technol. 53, 8157–8166 (2019).
ADS  CAS  PubMed  Article  Google Scholar 

42.
Gonzalez, C., Garcia, P. A. & Munoz, R. Effect of feed characteristics on the organic matter, nitrogen and phosphorus removal in an activated sludge system treating piggery slurry. Water Sci. Technol. 60, 2145–2152 (2009).
CAS  PubMed  Article  Google Scholar 

43.
Lydmark, P., Lind, M., Sörensson, F. & Hermansson, M. Vertical distribution of nitrifying populations in bacterial biofilms from a full-scale nitrifying trickling filter. Environ. Microbiol. 8, 2036–2049 (2006).
CAS  PubMed  Article  Google Scholar 

44.
Okabe, S., Satoh, H. & Watanabe, Y. In situ analysis of nitrifying biofilms as determined by in situ hybridization and the use of microelectrodes. Appl. Environ. Microbiol. 65, 3182–3191 (1999).
CAS  PubMed  PubMed Central  Article  Google Scholar 

45.
Anthonisen, A., Loehr, R., Prakasam, T. & Srinath, E. Inhibition of nitrification by ammonia and nitrous acid. Journal (Water Pollut. Control Fed.), 835–852 (1976).

46.
Lackner, S. et al. Full-scale partial nitritation/anammox experiences: an application survey. Water Res. 55, 292–303 (2014).
CAS  PubMed  Article  Google Scholar 

47.
Wu, J., He, C., van Loosdrecht, M. C. M. & Pérez, J. Selection of ammonium oxidizing bacteria (AOB) over nitrite oxidizing bacteria (NOB) based on conversion rates. Chem. Eng. J. 304, 953–961 (2016).
CAS  Article  Google Scholar 

48.
Tchobanoglous, G. B., Franklin, L. & Stensel, H. D. Wastewater engineering: treatment and reuse 4th edn. (McGraw Hill, New York, 2003).
Google Scholar 

49.
Smith, R. C., Elger, S. O. & Mleziva, S. Implementation of solids retention time (SRT) control in wastewater treatment. Xylem Anal. 20, 1–6 (2015).
Google Scholar 

50.
Simsek, H., Kasi, M., Ohm, J.-B., Murthy, S. & Khan, E. Impact of solids retention time on dissolved organic nitrogen and its biodegradability in treated wastewater. Water Res. 92, 44–51 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

51.
Wu, Y.-J. et al. Impact of food to microorganism (F/M) ratio and colloidal chemical oxygen demand on nitrification performance of a full-scale membrane bioreactor treating thin film transistor liquid crystal display wastewater. Bioresour. Technol. 141, 35–40 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

52.
Meerburg, F. A. et al. High-rate activated sludge communities have a distinctly different structure compared to low-rate sludge communities, and are less sensitive towards environmental and operational variables. Water Res. 100, 137–145 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

53.
Vuono, D. C. et al. Disturbance and temporal partitioning of the activated sludge metacommunity. ISME J. 9, 425–435 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

54.
Ballinger, S. J., Head, I. M., Curtis, T. P. & Godley, A. R. The effect of C/N ratio on ammonia oxidising bacteria community structure in a laboratory nitrification-denitrification reactor. Water Sci. Technol. 46, 543–550 (2002).
CAS  PubMed  Article  PubMed Central  Google Scholar 

55.
Cabrol, L. et al. Management of microbial communities through transient disturbances enhances the functional resilience of nitrifying gas-biofilters to future disturbances. Environ. Sci. Technol. 50, 338–348 (2016).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

56.
Wells, G. F. et al. Comparing the resistance, resilience, and stability of replicate moving bed biofilm and suspended growth combined nitritation–anammox reactors. Environ. Sci. Technol. 51, 5108–5117 (2017).
ADS  CAS  PubMed  Article  Google Scholar 

57.
Pianka, E. R. R-selection and K-selection. Am. Nat. 104, 592–579 (1970).
Article  Google Scholar 

58.
Santillan, E., Seshan, H., Constancias, F. & Wuertz, S. Trait-based life-history strategies explain succession scenario for complex bacterial communities under varying disturbance. Environ. Microbiol. 21, 3751–3764 (2019).
CAS  PubMed  Article  Google Scholar 

59.
Macarthur, R. H. & Wilson, E. O. The Theory of Island Biogeography 224 (Princeton, Princeton University Press, 1967).
Google Scholar 

60.
Blackburne, R., Vadivelu, V. M., Yuan, Z. & Keller, J. Kinetic characterisation of an enriched Nitrospira culture with comparison to Nitrobacter. Water Res. 41, 3033–3042 (2007).
CAS  PubMed  Article  PubMed Central  Google Scholar 

61.
Dytczak, M. A., Londry, K. L. & Oleszkiewicz, J. A. Activated sludge operational regime has significant impact on the type of nitrifying community and its nitrification rates. Water Res. 42, 2320–2328 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

62.
Huang, Z., Gedalanga, P. B., Asvapathanagul, P. & Olson, B. H. Influence of physicochemical and operational parameters on Nitrobacter and Nitrospira communities in an aerobic activated sludge bioreactor. Water Res. 44, 4351–4358 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

63.
Vuono, D. C., Munakata-Marr, J., Spear, J. R. & Drewes, J. E. Disturbance opens recruitment sites for bacterial colonization in activated sludge. Environ. Microbiol. 18, 87–99 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

64.
Jauffur, S., Isazadeh, S. & Frigon, D. Should activated sludge models consider influent seeding of nitrifiers? Field characterization of nitrifying bacteria. Water Sci. Technol. 70, 1526–1532 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

65.
Yu, L. et al. Natural continuous influent nitrifier immigration effects on nitrification and the microbial community of activated sludge systems. J. Environ. Sci. 74, 159–167 (2018).
Article  Google Scholar 

66.
Allison, S. D. & Martiny, J. B. H. Resistance, resilience, and redundancy in microbial communities. Proc. Natl. Acad. Sci. USA 105, 11512–11519 (2008).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

67.
Shade, A. et al. Lake microbial communities are resilient after a whole-ecosystem disturbance. ISME J. 6, 2153–2167 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

68.
Santillan, E. Disturbance-Performance-Diversity Relationships and Microbial Ecology in Bioreactors for Wastewater Treatment. Ph.D. thesis, University of California, Davis (2018).

69.
Hesselmann, R. P. X., Werlen, C., Hahn, D., van der Meer, J. R. & Zehnder, A. J. B. Enrichment, phylogenetic analysis and detection of a bacterium that performs enhanced biological phosphate removal in activated sludge. Syst. Appl. Microbiol. 22, 454–465 (1999).
CAS  PubMed  Article  PubMed Central  Google Scholar 

70.
APHA-AWWA-WEF. Standard Methods for the Examination of Water and Wastewater 22nd edn. (AWWA, Mumbai, 2005).
Google Scholar 

71.
Thijs, S. et al. Comparative evaluation of four bacteria-specific primer pairs for 16S rRNA gene surveys. Front. Microbiol. 8, 1–15 (2017).
Article  Google Scholar 

72.
Callahan, B. J. et al. DADA2: High resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

73.
Glöckner, F. O. et al. 25 years of serving the community with ribosomal RNA gene reference databases and tools. J. Biotechnol. 261, 169–176 (2017).
PubMed  Article  CAS  PubMed Central  Google Scholar 

74.
Chen, C., Khaleel, S. S., Huang, H. & Wu, C. H. Software for pre-processing Illumina next-generation sequencing short read sequences. Sour. Code Biol. Med. 9, 8–8 (2014).
Article  Google Scholar 

75.
Ilott, N. E. et al. Defining the microbial transcriptional response to colitis through integrated host and microbiome profiling. ISME J. 10, 2389–2404 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

76.
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

77.
Huson, D. H. et al. MEGAN community edition: interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Comp. Biol. 12, 1–12 (2016).
Article  CAS  Google Scholar 

78.
Tamames, J. & Puente-Sánchez, F. SqueezeMeta, a highly portable, fully automatic metagenomic analysis pipeline. Front. Microbiol. 9 (2019).

79.
Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

80.
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 119 (2010).
Article  CAS  Google Scholar 

81.
Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016).
CAS  PubMed  Article  Google Scholar 

82.
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

83.
Puente-Sánchez, F., García-García, N. & Tamames, J. SQMtools: automated processing and visual analysis of ’omics data with R and anvi’o. BMC Bioinform. 21, 358 (2020).
Article  Google Scholar 

84.
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc. Ser. B. (Method.) 57, 289–300 (1995).
MathSciNet  MATH  Google Scholar  More

1.
Bergman, K.-O., Dániel-Ferreira, J., Milberg, P., Öckinger, E. & Westerberg, L. Butterflies in Swedish grasslands benefit from forest and respond to landscape composition at different spatial scales. Landsc. Ecol. 33, 2189–2204. https://doi.org/10.1007/s10980-018-0732-y (2018).
Article  Google Scholar 
2.
Cousins, S. A. O., Auffret, A. G., Lindgren, J. & Tränk, L. Regional-scale land-cover change during the 20th century and its consequences for biodiversity. Ambio 44, 17–27. https://doi.org/10.1007/s13280-014-0585-9 (2015).
Article  PubMed Central  Google Scholar 

3.
Eriksson, O., Cousins, S. A. O. & Bruun, H. H. Land-use history and fragmentation of traditionally managed grasslands in Scandinavia. J. Veg. Sci. 13, 743–748. https://doi.org/10.1111/j.1654-1103.2002.tb02102.x (2002).
Article  Google Scholar 

4.
Tyler, T. et al. Recent changes in the frequency of plant species and vegetation types in Scania, S Sweden, compared to changes during the twentieth century. Biodivers. Conserv. 29, 709–728. https://doi.org/10.1007/s10531-019-01906-5 (2020).
Article  Google Scholar 

5.
Thomas, J. A. Butterfly communities under threat. Science 353, 216–218. https://doi.org/10.1126/science.aaf8838 (2016).
ADS  CAS  Article  PubMed  Google Scholar 

6.
Bommarco, R., Lundin, O., Smith, H. G. & Rundlöf, M. Drastic historic shifts in bumble-bee community composition in Sweden. Proc. R. Soc. B 279, 309–315. https://doi.org/10.1098/rspb.2011.0647 (2012).
Article  Google Scholar 

7.
Marini, L. et al. Contrasting effects of habitat area and connectivity on evenness of pollinator communities. Ecography 37, 544–551. https://doi.org/10.1111/j.1600-0587.2013.00369.x (2014).
Article  Google Scholar 

8.
Ferreira, P. A., Boscolo, D. & Viana, B. F. What do we know about the effects of landscape changes on plant–pollinator interaction networks?. Ecol. Indic. 31, 35–40. https://doi.org/10.1016/j.ecolind.2012.07.025 (2013).
Article  Google Scholar 

9.
Larsen, T. H., Williams, N. M. & Kremen, C. Extinction order and altered community structure rapidly disrupt ecosystem functioning: altered community structure disrupts functioning. Ecol. Lett. 8, 538–547. https://doi.org/10.1111/j.1461-0248.2005.00749.x (2005).
Article  PubMed  Google Scholar 

10.
Vanneste, T. et al. Plant diversity in hedgerows and road verges across Europe. J. Appl. Ecol. 57, 1244–1257. https://doi.org/10.1111/1365-2664.13620 (2020).
Article  Google Scholar 

11.
Phillips, B. B. et al. Enhancing road verges to aid pollinator conservation: a review. Biol. Conserv. 250, 108687. https://doi.org/10.1016/j.biocon.2020.108687 (2020).

12.
Berg, Å., Bergman, K.-O., Wissman, J., Żmihorski, M. & Öckinger, E. Power-line corridors as source habitat for butterflies in forest landscapes. Biol. Conserv. 201, 320–326. https://doi.org/10.1016/j.biocon.2016.07.034 (2016).
Article  Google Scholar 

13.
Lundholm, J. T. & Richardson, P. J. MINI-REVIEW: Habitat analogues for reconciliation ecology in urban and industrial environments. J. Appl. Ecol. 47, 966–975. https://doi.org/10.1111/j.1365-2664.2010.01857.x (2010).
Article  Google Scholar 

14.
Cranmer, L., McCollin, D. & Ollerton, J. Landscape structure influences pollinator movements and directly affects plant reproductive success. Oikos 121, 562–568. https://doi.org/10.1111/j.1600-0706.2011.19704.x (2012).
Article  Google Scholar 

15.
Van Geert, A., Van Rossum, F. & Triest, L. Do linear landscape elements in farmland act as biological corridors for pollen dispersal? J. Ecol. 98, 178–187. https://doi.org/10.1111/j.1365-2745.2009.01600.x (2010).
Article  Google Scholar 

16.
Lázaro-Lobo, A. & Ervin, G. N. A global examination on the differential impacts of roadsides on native vs. exotic and weedy plant species. Glob. Ecol. Conserv. 17, e00555. https://doi.org/10.1016/j.gecco.2019.e00555 (2019).
Article  Google Scholar 

17.
Dubé, C., Pellerin, S. & Poulin, M. Do power line rights-of-way facilitate the spread of non-peatland and invasive plants in bogs and fens?. Botany 89, 91–103. https://doi.org/10.1139/B10-089 (2011).
Article  Google Scholar 

18.
Fahrig, L. & Rytwinski, T. Effects of Roads on Animal Abundance: an Empirical Review and Synthesis. Ecol. Soc. 14(1): 21. http://www.ecologyandsociety.org/vol14/iss1/art21/ (2009).

19.
Benítez-López, A., Alkemade, R. & Verweij, P. A. The impacts of roads and other infrastructure on mammal and bird populations: a meta-analysis. Biol. Conserv. 143, 1307–1316. https://doi.org/10.1016/j.biocon.2010.02.009 (2010).
Article  Google Scholar 

20.
Keilsohn, W., Narango, D. L. & Tallamy, D. W. Roadside habitat impacts insect traffic mortality. J. Insect Conserv. 22, 183–188. https://doi.org/10.1007/s10841-018-0051-2 (2018).
Article  Google Scholar 

21.
Gardiner, M. M., Riley, C. B., Bommarco, R. & Öckinger, E. Rights-of-way: a potential conservation resource. Front. Ecol. Environ. 16, 149–158. https://doi.org/10.1002/fee.1778 (2018).
Article  Google Scholar 

22.
Phillips, B. B., Gaston, K. J., Bullock, J. M. & Osborne, J. L. Road verges support pollinators in agricultural landscapes, but are diminished by heavy traffic and summer cutting. J. Appl. Ecol. 56, 2316–2327. https://doi.org/10.1111/1365-2664.13470 (2019).
Article  Google Scholar 

23.
Wagner, D. L., Metzler, K. J. & Frye, H. Importance of transmission line corridors for conservation of native bees and other wildlife. Biol. Conserv. 235, 147–156. https://doi.org/10.1016/j.biocon.2019.03.042 (2019).
Article  Google Scholar 

24.
Wojcik, V. A. & Buchmann, S. Pollinator conservation and management on electrical transmission and roadside rights-of-way: a review. J. Pollinat. Ecol. 7, 16–26 (2012).
Article  Google Scholar 

25.
Stenmark, M. Infrastrukturens gräs-och buskmarker. Hur stora arealer gräs och buskmarker finns i anslutning till transportinfrastruktur och bidrar dessa till miljömålsarbetet? Infrastrukturens gräs-och buskmarker. Jordbruksverket Rapport 2012:36 (2012).

26.
Jeusset, A. et al. Can linear transportation infrastructure verges constitute a habitat and/or a corridor for biodiversity in temperate landscapes? A systematic review protocol. Environ. Evid. 7, 5. https://doi.org/10.1186/s13750-016-0056-9 (2016).
Article  Google Scholar 

27.
Crist, T. O., Veech, J. A., Gering, J. C. & Summerville, K. S. Partitioning species diversity across landscapes and regions: a hierarchical analysis of α, β, and γ diversity. Am. Nat. 162, 734–743. https://doi.org/10.1086/378901 (2003).
Article  PubMed  Google Scholar 

28.
With, K. A. Are landscapes more than the sum of their patches?. Landsc. Ecol. 31, 969–980. https://doi.org/10.1007/s10980-015-0328-8 (2016).
Article  Google Scholar 

29.
Cornell, H. V. & Harrison, S. P. What are species pools and when are they important?. Annu. Rev. Ecol. Evol. Syst. 45, 45–67. https://doi.org/10.1146/annurev-ecolsys-120213-091759 (2014).
Article  Google Scholar 

30.
Cornell, H. V. & Lawton, J. H. Species interactions, local and regional processes, and limits to the richness of ecological communities: a theoretical perspective. J. Anim. Ecol. 61, 1. https://doi.org/10.2307/5503 (1992).
Article  Google Scholar 

31.
Steinert, M., Moe, S. R., Sydenham, M. A. K. & Eldegard, K. Different cutting regimes improve species and functional diversity of insect-pollinated plants in power-line clearings. Ecosphere 9, e02509. https://doi.org/10.1002/ecs2.2509 (2018).
Article  Google Scholar 

32.
Gagic, V. et al. Functional identity and diversity of animals predict ecosystem functioning better than species-based indices. Proc. R. Soc. B Biol. Sci. 282, 20142620. https://doi.org/10.1098/rspb.2014.2620 (2015).
Article  Google Scholar 

33.
Chao, A., Chiu, C.-H. & Jost, L. Unifying species diversity, phylogenetic diversity, functional diversity, and related similarity and differentiation measures through hill numbers. Annu. Rev. Ecol. Evol. Syst. 45, 297–324. https://doi.org/10.1146/annurev-ecolsys-120213-091540 (2014).
Article  Google Scholar 

34.
Vellend, M., Cornwell, W. K., Magnuson-Ford, K. & Mooers, A. O. Measuring phylogenetic biodiversity. In Biological Diversity: Frontiers in Measurement and Assessment 194–207 (Oxford University Press, 2011).

35.
Rosenzweig, M. L. Species Diversity in Space and Time (Cambridge University Press, Cambridge, 1995).
Google Scholar 

36.
Fahrig, L. Rethinking patch size and isolation effects: the habitat amount hypothesis. J. Biogeogr. 40, 1649–1663. https://doi.org/10.1111/jbi.12130 (2013).
Article  Google Scholar 

37.
Hill, B. & Bartomeus, I. The potential of electricity transmission corridors in forested areas as bumblebee habitat. R. Soc. Open Sci. 3, 160525. https://doi.org/10.1098/rsos.160525 (2016).
ADS  Article  PubMed  PubMed Central  Google Scholar 

38.
Kuussaari, M. et al. Extinction debt: a challenge for biodiversity conservation. Trends Ecol. Evol. 24, 564–571. https://doi.org/10.1016/j.tree.2009.04.011 (2009).
Article  PubMed  Google Scholar 

39.
Krauss, J. et al. Habitat fragmentation causes immediate and time-delayed biodiversity loss at different trophic levels. Ecol. Lett. 13, 597–605. https://doi.org/10.1111/j.1461-0248.2010.01457.x (2010).
Article  PubMed  PubMed Central  Google Scholar 

40.
Grusell, E. & Miliander, S. Fältmanual för skötsel av kraftledningsgatans biotoper. https://www.svk.se/contentassets/2f77f2d04b7b451495013f4de5fa7409/bilaga-5-faltmanual-for-skotsel-av-kraftledningsgatans-biotoper.pdf (2011).

41.
Zeiter, M., Stampfli, A. & Newbery, D. M. Recruitment limitation constrains local species richness and productivity in dry grassland. Ecology 87, 942–951. https://doi.org/10.1890/0012-9658(2006)87[942:RLCLSR]2.0.CO;2 (2006).
CAS  Article  PubMed  Google Scholar 

42.
Chaudron, C., Chauvel, B. & Isselin-Nondedeu, F. Effects of late mowing on plant species richness and seed rain in road verges and adjacent arable fields. Agric. Ecosyst. Environ. 232, 218–226. https://doi.org/10.1016/j.agee.2016.03.047 (2016).
Article  Google Scholar 

43.
Angold, P. G. The impact of a road upon adjacent heathland vegetation: effects on plant species composition. J. Appl. Ecol. 34, 409–417 (1997).
Article  Google Scholar 

44.
Watmough, S. A., Rabinowitz, T. & Baker, S. The impact of pollutants from a major northern highway on an adjacent hardwood forest. Sci. Total Environ. 579, 409–419. https://doi.org/10.1016/j.scitotenv.2016.11.081 (2017).
ADS  CAS  Article  PubMed  Google Scholar 

45.
Andersson, P., Koffman, A., Sjödin, N. E. & Johansson, V. Roads may act as barriers to flying insects: species composition of bees and wasps differs on two sides of a large highway. Nat. Conserv. 18, 47–59. https://doi.org/10.3897/natureconservation.18.12314 (2017).
Article  Google Scholar 

46.
Öckinger, E. & Smith, H. G. Semi-natural grasslands as population sources for pollinating insects in agricultural landscapes. J. Appl. Ecol. 44, 50–59. https://doi.org/10.1111/j.1365-2664.2006.01250.x (2006).
Article  Google Scholar 

47.
Krauss, J., Klein, A.-M., Steffan-Dewenter, I. & Tscharntke, T. Effects of habitat area, isolation, and landscape diversity on plant species richness of calcareous grasslands. Biodivers. Conserv. 13, 1427–1439. https://doi.org/10.1023/B:BIOC.0000021323.18165.58 (2004).
Article  Google Scholar 

48.
Thiele, J., Kellner, S., Buchholz, S. & Schirmel, J. Connectivity or area: what drives plant species richness in habitat corridors?. Landsc. Ecol. 33, 173–181. https://doi.org/10.1007/s10980-017-0606-8 (2018).
Article  Google Scholar 

49.
Lampinen, J., Heikkinen, R. K., Manninen, P., Ryttäri, T. & Kuussaari, M. Importance of local habitat conditions and past and present habitat connectivity for the species richness of grassland plants and butterflies in power line clearings. Biodivers. Conserv. 27, 217–233. https://doi.org/10.1007/s10531-017-1430-9 (2018).
Article  Google Scholar 

50.
Pettersson, L. B., Arnberg, H. & Mellbrand, K. Svensk Dagfjärilsövervakning Årsrapport 2018. (2018).

51.
Orrock, J. L., Curler, G. R., Danielson, B. J. & Coyle, D. R. Large-scale experimental landscapes reveal distinctive effects of patch shape and connectivity on arthropod communities. Landsc. Ecol. 26, 1361–1372. https://doi.org/10.1007/s10980-011-9656-5 (2011).
Article  Google Scholar 

52.
Clough, Y. et al. Density of insect-pollinated grassland plants decreases with increasing surrounding land-use intensity. Ecol. Lett. 17, 1168–1177. https://doi.org/10.1111/ele.12325 (2014).
Article  Google Scholar 

53.
Grab, H. et al. Agriculturally dominated landscapes reduce bee phylogenetic diversity and pollination services. Science 363, 282–284. https://doi.org/10.1126/science.aat6016 (2019).
ADS  CAS  Article  PubMed  Google Scholar 

54.
Williams, N. M. et al. Ecological and life-history traits predict bee species responses to environmental disturbances. Biol. Conserv. 143, 2280–2291. https://doi.org/10.1016/j.biocon.2010.03.024 (2010).
Article  Google Scholar 

55.
Helmus, M. R. & Ives, A. R. Phylogenetic diversity—area curves. Ecology 93, S31–S43. https://doi.org/10.1890/11-0435.1 (2012).
Article  Google Scholar 

56.
Cameron, S. A., Hines, H. M. & Williams, P. H. A comprehensive phylogeny of the bumble bees (Bombus). Biol. J. Linn. Soc. 91, 161–188. https://doi.org/10.1111/j.1095-8312.2007.00784.x (2007).
Article  Google Scholar 

57.
Eneland, A. Ängs- och betesmarksinventeringen. Metodik för inventering från och med 2016. Jordbruksverket Rapport 2017:9 (2017).

58.
Pollard, E. A method for assessing changes in the abundance of butterflies. Biol. Conserv. 12, 115–134. https://doi.org/10.1016/0006-3207(77)90065-9 (1977).
Article  Google Scholar 

59.
ESRI. ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems Research Institute (2018). https://desktop.arcgis.com/en/arcmap/.

60.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. (2019). More

Study location and species
Field research and sample collection was conducted on the shallow reef habitat surrounding the Smithsonian’s Carrie Bow Cay Research Station, Belize (16°48’9.8316”N, 88°4’54.8148”W) between January-April 2018. Mysid swarms, identified primarily as Mysidium integrum50, are present on these reefs year-round, and this species was used in all experiments. Six damselfish species in the genus Stegastes were present at the study site. Three of these, the longfin damselfish (Stegastes diencaeus), the phylogenetically similar dusky damselfish (Stegastes adustus)31, and threespot damselfish (Stegastes planifrons), can be characterized as ‘intensive territorial grazers’ or ‘farmers’ that tend and aggressively defend the turf-algae communities on which they feed24. The others, the bicolor damselfish (Stegastes partitus), cocoa damselfish (Stegastes variabilis) and beaugregory (Stegastes leucostictus) tend turf-algae to a lesser degree and display limited territoriality. As the most common intensive-farming species, the longfin damselfish was used in all experiments.
Analytical software
Analyses were conducted using R51. Generalized linear mixed models (GLMMs) were fitted with the lme4 package52 and zero-inflated GLMMs were fitted with the glmmTMB package53. The multinomial logistic regression model used for algal analysis was performed with the packages nnet54 and effects55.
Associations between mysids and damselfish farms
To determine whether mysid swarms were associated with Stegastes farms, we conducted a series of transects. Thirty 30-m transects were laid haphazardly across the study site. For 1 m on each side of the transect we recorded: the total number of swarms, the total number of Stegastes and whether a swarm was associated with a Stegastes farm. Each Stegastes was recorded to species. We used a (chi)2 test to investigate whether the presence of intensive-farming Stegastes was associated with swarm presence.
Mysid swarm movement and site fidelity
Mysidium swarms will often leave the substrate at dusk to feed in the water column, and isotope tagging indicates that swarms regroup at the same location each morning where they remain during daylight hours29. To test whether swarms at our study site followed this pattern, and whether swarms remained associated with the same Stegastes farms, we tested for site fidelity across a 20-day period. Thirty locations within longfin damselfish farms that had mysid swarms were tagged with numbered flagging tape. Thirty longfin damselfish farms where swarms were absent were also tagged. All locations were tagged between 9 and 10 a.m. Each location was visited at day 1, 10, and 20 post-tagging, 1-h post-sunrise and 1-h post-sunset, with swarm presence or absence recorded. In addition to formal rechecking, sites were frequently reassessed throughout this period, both during the day and at night, to confirm the consistency of the patterns recorded. We used a Friedman test to investigate whether swarms exhibited site fidelity to particular farms during the day over the 20-day period. The full and final model included time as a predictor and farm identification as a blocking factor.
Mysid responses to habitat-related olfactory cues
Choice experiments were conducted to determine whether mysids used olfactory cues to actively seek out intensive-farming damselfish33. Experiments were conducted using a two-channel choice flume (13 cm length × 4 cm width)56. Header tanks contained two separate water sources, each gravity fed into separate sides of the flume at equivalent volumes (~100 mL min−1). The flume design ensures that once laminar flow is achieved each water mass remains separated on either side of the main chamber with no areas of turbulence or eddies, presenting an individual placed in the center with a choice between the two separate water sources/olfactory cues. Regular dye tests confirmed laminar flow and that the two water sources remained separated.
Five separate cue combinations were tested. Four cues were tested against a seawater control (seawater with no added cue): longfin damselfish (putative mutualism partner), farmed turf (putative mutualism partner’s environment), bicolor damselfish (non-intensive-farming damselfish that did not associate with mysids), and slippery dick wrasse (Halichoeres bivittatus, a diurnal predator of mysids). The fifth combination was a mysid-associated longfin damselfish versus a non-mysid-associated longfin damselfish. Cues were prepared by soaking an individual fish, or turf-covered rock from a longfin damselfish farm, in 10 L of seawater from the Carrie Bow Cay flow-through system for 1-h. All selected fishes and turf pieces had a similar biomass to minimize variation in cue concentration. For each trial set, both the cue and seawater control were produced concurrently, with both buckets sitting adjacent with constant aeration for the 1-h period. In this way, both cue and control had matching salinity, temperature, and O2 levels, minimizing any opportunity for behavioral bias due to the physical properties of the water and ensuring proper laminar flow. Each cue combination was split into three blocks: three separate fish or turf-covered rocks (one per block) were used to prepare treatments, with ten replicates obtained from each block (a total of n = 30 per combination). Individual mysids were only used in one trial.
All trials were conducted blind, with the tester having no knowledge of the cues tested or the side on which each cue was placed. A second observer was also present at all times. For each trial, a mysid was placed at the downstream center of the flume chamber. Following a 2-min habituation period, its position on either the left or right side of chamber was recorded at 5-s intervals for 2-min. Water sources were then switched to the opposite sides, and the chamber was allowed to flush for 1-min. The 2-min habituation period and 2-min test period were then repeated to exclude the possibility that mysids were exhibiting a side preference (i.e., spending 100% of time on one side of the chamber despite the water source being switched midway through the trial). Mysids that exhibited a side preference (n = 6 of 156) were excluded from analysis. Data were analyzed using paired t-tests, except for the longfin damselfish versus seawater comparison. Here, a Wilcoxon signed-rank test was used because these data did not meet the assumption of normality.
Effect of predation on the damselfish-mysid relationship
To first test whether mysids receive protection by residing within the boundaries of longfin damselfish farms we conducted a predation-risk experiment. Sixty trials were conducted (n = 30 inside, and n = 30 immediately outside of farms), which each consisted of three treatments: (1) live mysids, (2) an ‘imitation’ mysid control, and (3) a seawater control. Each treatment consisted of a weighted 3.5 L polyethylene bag filled with seawater. The live mysid treatment consisted of 150 mysids. The ‘imitation’ mysid control was included to account for the presence of objects within the bag and consisted of 150 1 mm long sections of 4 mm diameter silicon tubing. These lightweight sections were slightly negatively buoyant and moved within the bag due to external water movement. Finally, the seawater control consisted of an empty seawater-filled bag. For each trial, bags were sequentially placed on the substrate in random order.
Each trial was 1-min in duration, during which the focal bag was filmed using an HD video camera (GoPro). After 1-min, this bag was removed and a 1-min rest period was observed. A bag containing the next treatment was then placed in the same location, and the 1-min trial was repeated. This same procedure was then repeated for the third bag. Videos were analyzed to compare: the number of fishes that attacked each bag, the number of strikes taken, the species of the attacker(s), and the number and species of fishes that came within 1 m of the bag but did not attack.
We used a zero-inflated GLMM with a Poisson distribution to test whether the number of strikes directed by predators at bags differed according to treatment (empty bag, artificial mysids, and live mysids), and location (inside versus outside of farm). The full and final model included treatment, location, and the interaction between treatment and location as fixed effects and trial as a random effect. We used a GLMM with a Poisson distribution to test whether the number of species that directed strikes at bags differed according to treatment and location. The full model included treatment, location and the interaction between treatment and location as fixed effects and trial as a random effect; however, the interaction was removed from the final model as it was found to be non-significant. We used a GLMM with a negative-binomial distribution to test whether the number of individuals that directed strikes at bags differed according to treatment and location. The full model included treatment, location and the interaction between treatment and location as fixed effects and trial as a random effect; however, the interaction was removed from the final model as it was found to be non-significant. Finally, we used a zero-inflated GLMM with a Poisson distribution to test whether the number of chases by longfin damselfish directed towards mysid predators differed according to treatment, location and the interaction between treatment and location. The full model included treatment, location and the interaction between treatment and location as fixed effects and trial as a random effect; however, the interaction was removed from the final model after being found to be non-significant.
In addition, to test whether the persistence of naturally occurring swarms was dependent on the protection damselfish provide, we conducted a second field-based predation experiment. Thirty trials were conducted (n = 15 treatment, and n = 15 control) with replicates for both conducted in a random order. For treatment trials, a swarm within a damselfish farm was observed on SCUBA from a distance of 2 m for a 5-min period. During this time, all strikes on the swarm by predatory fishes were recorded with this number taken as the baseline predation rate. Immediately following this period, a second 5-min observation was conducted during which a second diver actively prevented damselfish from defending their territory, pressuring fish into reef structure by gesturing at them using a fiberglass pole. During this second period, all strikes on the swarm were again recorded with this number taken as the change in predation rate. Control trials accounted for the effect of the second diver’s actions on predator behavior. Control period 1 was as above; however, during the second period, the second diver made movements and noise using the pole but did not direct this at damselfish, allowing them to continue to defend their farm. All strikes on the swarm were recorded. During control trials, damselfish did not react to the second diver’s actions indicating that their territorial behavior was not affected. Differences in strikes between periods were determined using Wilcoxon signed-rank tests.
Effect of mysids on damselfish behavior
We conducted field observations to determine whether mysid-associated longfin damselfish behaved differently to those without mysids. Adult longfin damselfish that were (n = 30), or were not (n = 30), associated with swarms were observed for 30-min. For each observation, the focal fish was observed on SCUBA from a distance of at least 2-m. During each observation we recorded the number of bites on the farmed substrate, the number of strikes directed towards the swarms, the number of chases directed towards intruding fishes, the number of chases directed towards intruding fishes attempting to feed on farm-associated mysids and the number of non-aggressive interactions between the focal fish and the swarm. At the end of each observation, we recorded the total number of longfin damselfish associated with each farm with only one observation made per farm. An estimate of farm area was also made at this point by using a transect tape to measure the maximum length and width across the area that was actively defended and tended during the observation period.
We used a GLM with a Gaussian distribution to determine whether the number of chases by longfin damselfish was associated with the presence or absence of mysids. The full model included farm type (mysids present or absent), longfin damselfish group size, and the interaction between farm type and group size as fixed effects; however, the interaction between farm type and group size, and group size were removed from the final model as they were found to be non-significant. A GLM with a Gaussian distribution was used to test whether the number of bites on farmed substrate by focal longfin damselfish was associated with the presence or absence of mysids. The full model included farm type (mysids present or absent) longfin damselfish group size and the interaction between farm type and group size as fixed effects; however, the interaction between farm type and group size, and group size were removed from the final model because they were found to be non-significant. Whether farm area differed between farms with and without mysids was determined using a Wilcoxon rank sum test.
Effect of mysid swarms on longfin damselfish body condition
To determine the effect of mysid presence on longfin damselfish body condition, we compared the hepatosomatic index (HSI) of damselfish with and without mysids in their farms. This measure can reflect the amount of stored energy in the liver, and thus it can indicate of the relationship between diet and physical condition in damselfishes57,58,59,60. Thirty adult longfin damselfish (75–100 mm TL) were sampled from farms with or without associated mysids. Damselfish were collected on snorkel using hand nets and a 1:3:7 clove oil/ethanol/seawater mixture. Prior to euthanasia, fish were maintained in a 20 L flow-through aquaria for 24-h. Fish were not fed during this period. Fish were euthanized by immersion in a clove oil/ethanol/seawater solution to induce anesthesia followed by immersion in an ice slurry. Once euthanized, fish were measured (SL and TL) and weighed. The liver of each fish was removed and weighed, and the alimentary canal checked to confirm that all digested matter was evacuated. The HSI of each fish was calculated as the proportion of total weight contributed by the liver [(liver weight (g)/total weight (g)) × 100]. We used a GLM with a Gaussian distribution to test the effect of mysid presence on hepatosomatic index (HSI). The full model included damselfish length, farm type (mysids present or absent) and the interaction between damselfish length and farm type as fixed effects; however, the interaction and damselfish length were removed from the final model as they were found to be non-significant.
Effect of mysid swarms on algal composition within damselfish farms
Algal composition was assessed to determine the effect of mysid swarms on algae within longfin damselfish farms. Sixty farms were analyzed: 30 with and 30 without swarms. Three 20 cm × 20 cm quadrats were placed haphazardly within each farm, and a series of four photographs were taken, including one overhead shot encompassing the entire quadrat and three macro shots of the algae within the quadrat. Within each quadrat, algal composition and coverage was determined to phylum, including Chlorophyta, Rhodophyta, and Ochrophyta. Percent cover of these groups was assigned based on a categorical classification scheme: low (30% coverage). To obtain a single assessment of algal composition for each phylum within each farm, the categorical classification was averaged across the three quadrats photographed in each farm. We used a multinomial logistic regression model to assess how the percent cover of Ochrophyta within farms was affected by swarm presence. The response within each model was multinomial: low, medium, or high percent coverage of Ochrophyta. The model included the fixed effect of mysid presence or absence.
Estimates of mysid swarm density
Surveys were conducted to determine the average size and density of farm-associated swarms. The size and area of 30 focal swarms were determined by measuring the length, width and height across the widest points when first observed. Each swarm was then collected using hand nets and returned to the laboratory where the total number of mysids within each swarm was counted. Finally, swarm density was estimated by calculating the maximum ellipsoid volume based on the measured axes and dividing this volume by the total number of mysids, giving an estimate of mysids mL−1.
Mysid waste excretion and nutrient availability
We used an aquarium experiment to determine if mysid swarms produce key nutrients at concentrations that could enhance benthic algal growth. Artificial seawater was produced at sunrise by mixing deionized fresh water with a phosphate and nitrogen-free aquarium salt (Instant Ocean® Sea Salt) to 35ppt salinity. Once mixed, the absence of phosphate and ammonia was confirmed through color comparison using laboratory-grade test kits (Hach PO-19A test kit, Hach NI-SA test kit), and the temperature and pH of the water were also measured. Seawater was then distributed into a series of 1 L plastic containers, along with one air stone per container. Containers were covered to prevent water loss through evaporation.
Mysids were collected using hand nets and returned to the lab where they were allowed to habituate for 30-min in a bucket containing 3 L of artificial seawater. For sorting, mysids were removed from the habituation bucket using a hand net and placed into a petri dish containing artificial seawater. Mysids were individually selected using a sterile plastic pipette and moved into a second petri dish containing artificial seawater. The water in the second petri dish was then removed using the pipette, and mysids were placed into the appropriate container corresponding to one of three treatments: 0 mysids L−1 (n = 30), which served as a control, 100 mysids L−1 (n = 30) and 200 mysids L−1 (n = 30). These densities were selected as they are representative of the range found during the swarm density surveys. Each day, containers were haphazardly assigned a treatment prior to sorting, with an equal number of replicates for each treatment (n = 5) conducted each day. Trials were conducted from 08:00 to 16:00. This period was selected to represent the daylight hours when mysids are present, but was also short enough to prevent stress due to nutrient accumulation. Following this 8-h period, mysids were removed, and the concentration of phosphorous (P) and nitrogen-ammonia (NH3–N) in each container were recorded to the nearest 0.1 mgL−1 through color comparison. The temperature, salinity, and pH of each container were also recorded at this point.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article. More

1.
Devai, I., Felföldy, L., Wittner, I. & Plósz, S. Detection of phosphine: new aspects of the phosphorus cycle in the hydrosphere. Nature 333, 343 (1988).
Article  Google Scholar 
2.
Cao, J. et al. Study on effects of electron donors on phosphine production from anaerobic activated sludge. Water 9, 563 (2017).
Article  Google Scholar 

3.
Zhang, C., Zhang, K., Wei, W., Rong, H. & Liu, T. Release rule of phosphine in anaerobic sequencing batch process. China Water Wastewater 26, 53–55 (2010).
Google Scholar 

4.
Hong, Y. et al. Distribution of phosphine in the offshore area of the Southwest Yellow Sea, East Asia. Mar. Chem. 118, 67–74 (2010).
Article  Google Scholar 

5.
Liu, Z., Jia, S., Wang, B. & Liu, S. Differences in phosphine contents of various environment samples and the effecting factors. Acta Scien. Circum. 5, 852–857 (2004).
Google Scholar 

6.
Zhu, R., Liu, Y., Sun, J., Sun, L. & Geng, J. Stimulation of gaseous phosphine production from Antarctic seabird guanos and ornithogenic soils. J. Environ. Sci. 21, 150–154 (2009).
Article  Google Scholar 

7.
Zhang, R., Wu, M., Wang, Q., Geng, J. & Yang, X. The determination of atmospheric phosphine in Ny-Ålesund. Sci. Bull. 55, 1662–1666 (2010).
Article  Google Scholar 

8.
Zhu, R., Ding, W., Hou, L. & Wang, Q. Matrix-bound phosphine and phosphorus fractions in surface sediments of Arctic Kongsfjorden, Svalbard: effects of glacial activity and environmental variables. Chemosphere 103, 240–249 (2014).
Article  Google Scholar 

9.
Wang, Q., Geng, J.-j., Jin, H.-m., Shi, H.-h. & Wang, X.-r. Temporal and spatial distributions of microbes and phosphine in Lake Taihu sediments. China Environ. Sci. 26, 350–354 (2006).
Google Scholar 

10.
Ding, L. et al. Sources of matrix-bound phosphine in advanced wastewater treatment system. Sci. Bull. 50, 1274–1276 (2005).
Article  Google Scholar 

11.
Li, J.-B. et al. Matrix bound phosphine in sediments of the Changjiang Estuary and its adjacent shelf areas. Estuar. Coast. Shelf Sci. 90, 206–211 (2010).
Article  Google Scholar 

12.
You, L. et al. Distribution of matrix-bound phosphine in surface sediments of Jinpu Bay. Environ. Sci. 34, 3804–3809 (2013).
Google Scholar 

13.
Niu, X. et al. Phosphine in paddy fields and the effects of environmental factors. Chemosphere 93, 1942–1947 (2013).
Article  Google Scholar 

14.
Glindemann, D., Edwards, M., Liu, J.-A. & Kuschk, P. Phosphine in soils, sludges, biogases and atmospheric implications—a review. Ecol. Eng. 24, 457–463 (2005).
Article  Google Scholar 

15.
Han, S.-H., Zhuang, Y.-H., Liu, J.-A. & Glindemann, D. Phosphorus cycling through phosphine in paddy fields. Sci. Total Environ. 258, 195–203 (2000).
Article  Google Scholar 

16.
Han, C., Geng, J., Zhang, R., Wang, X. & Gao, S. Matrix-bound phosphine and phosphorus fractions in paddy soils. J. Environ. Monit. 13, 844–849 (2011).
Article  Google Scholar 

17.
Han, C. et al. Production and emission of phosphine gas from wetland ecosystems. J. Environ. Sci. 22, 1309–1311 (2010).
Article  Google Scholar 

18.
Glindemann, D., Stottmeister, U. & Bergmann, A. Free phosphine from the anaerobic biosphere. Environ. Sci. Pollut. Res. 3, 17–19 (1996).
Article  Google Scholar 

19.
Wang, J., Niu, X., Ma, J. & Lu, M. Conversion of phosphorus to phosphine by microbial deoxidization under anaerobic conditions. Microbiol. China 1, 34–41 (2015).
Google Scholar 

20.
Ding, L. et al. Effect of pH on phosphine production and the fate of phosphorus during anaerobic process with granular sludge. Chemosphere 59, 49–54 (2005).
Article  Google Scholar 

21.
Zhang, R. et al. Effects of free-air CO2 enrichment on phosphine emission from rice field. Environ. Sci. 30, 2694–2700 (2009).
Google Scholar 

22.
Ma, J., Chen, W., Niu, X. & Fan, Y. The relationship between phosphine, methane, and ozone over paddy field in Guangzhou, China. Glob. Ecol. Conserv. 17, 1–7 (2019).
Google Scholar 

23.
Zhang, C., Zhang, K., Sun, L., Rong, H. & Liu, T. Effect of carbon sources on phoshpine production from anaerobic activated sludge. China Water Wastewater 29, 103–106 (2013).
Google Scholar 

24.
Liu, J.-A. et al. Phosphine in the urban air of Beijing and its possible sources. Water Air Soil Pollut. 116, 597–604 (1999).
Article  Google Scholar 

25.
Niu, X. et al. Temporal and spatial distributions of phosphine in Taihu Lake, China. Sci. Total Environ. 323, 169–178 (2004).
Article  Google Scholar 

26.
Zhu, R. et al. Tropospheric phosphine and its sources in coastal Antarctica. Environ. Sci. Technol. 40, 7656–7661 (2006).
Article  Google Scholar 

27.
Zhu, R., Kong, D., Sun, L., Geng, J. & Wang, X. The first determination of atmospheric phosphine in Antarctica. Sci. Bull. 52, 131–135 (2007).
Article  Google Scholar 

28.
Zhu, R. et al. Phosphine in the marine atmosphere along a hemispheric course from China to Antarctica. Atmos. Environ. 41, 1567–1573 (2007).
Article  Google Scholar 

29.
An, S. et al. Mechanism of matrix-bound phosphine production in response to atmospheric elevated CO2in paddy soils. Environ. Pollut. 239, 253–260 (2018).
Article  Google Scholar 

30.
Zhang, K., Zhang, C., Wei, W., Rong, H. & Liu, T. Phosphine release in aerobic sequencing reactor process and anaerobic/aerobic sequencing reactor process. Environ. Eng. 5, 127–129 (2011).
Google Scholar 

31.
Ding, L. et al. Distribution of phosphine and phosphorus balance in a full scale UASB system. J. Nanjing Uni. Nat. Sci. 41, 620–626 (2005).
Google Scholar 

32.
Hou, L. et al. Emission of phosphine in intertidal marshes of the Yangtze Estuary. Appl. Geochem. 26, 2260–2265 (2011).
Article  Google Scholar 

33.
Glindemann, D., Bergmann, A., Stottmeister, U. & Gassmann, G. Phosphine in the lower terrestrial troposphere. Naturwissenschaften 83, 131–133 (1996).
Article  Google Scholar 

34.
Feng, Y., Wang, Q., Yao, Z. & Geng, J. Research on distribution of phosphine in the natural environment and its environmental factors. Chin. High. Technol. Lett. 19, 650–655 (2009).
Google Scholar 

35.
Geng, J. et al. Simultaneous monitoring of phosphine and of phosphorus species in Taihu Lake sediments and phosphine emission from lake sediments. Biogeochemistry 76, 283–298 (2005).
Article  Google Scholar 

36.
Han, C. et al. Free atmospheric phosphine concentrations and fluxes in different wetland ecosystems, China. Environ. Pollut. 159, 630–635 (2011).
Article  Google Scholar 

37.
Glindemann, D., Edwards, M. & Kuschk, P. Phosphine gas in the upper troposphere. Atmos. Environ. 37, 2429–2433 (2003).
Article  Google Scholar 

38.
Gassmann, G., Van Beusekom, J. & Glindemann, D. Offshore atmospheric phosphine. Naturwissenschaften 83, 129–131 (1996).
Article  Google Scholar 

39.
Roels, J. & Verstraete, W. Occurrence and origin of phosphine in landfill gas. Sci. Total Environ. 327, 185–196 (2004).
Article  Google Scholar 

40.
Han, S.-h., Wang, Z.-j., Zhuang, Y.-h., Yu, Z.-m. & Glindemann, D. Phosphine in various matrixes. J. Environ. Sci. (China) 15, 339–341 (2003).
Google Scholar 

41.
Devai, I., DeLaune, R., Devai, G., Patrick, J. W. H. & Czegeny, I. Phosphine production potential of various wastewater and sewage sludge sources. Anal. Lett. 32, 1447–1457 (1999).
Article  Google Scholar 

42.
Zhu, R. et al. Matrix-bound phosphine in Antarctic biosphere. Chemosphere 64, 1429–1435 (2006).
Article  Google Scholar 

43.
Niu, X. et al. Matrix-bound phosphine in the paddy soils of South China and its relationship to environmental factors and bacterial composition. J. Soils Sediment. 16, 592–604 (2016).
Article  Google Scholar 

44.
Zhang, J., Geng, J., Zhang, R., Ren, H. & Wang, X. Matrix-bound phosphine in paddy fields under a simulated increase in global atmospheric CO2. Environ. Chem. 7, 287–291 (2010).
Article  Google Scholar 

45.
Eismann, F., Glindemann, D., Bergmannt, A. & Kuschk, P. Soils as source and sink of phosphine. Chemosphere 35, 523–533 (1997).
Article  Google Scholar 

46.
Devai, I. & Delaune, R. Evidence for phosphine production and emission from Louisiana and Florida marsh soils. Org. Geochem. 23, 277–279 (1995).
Article  Google Scholar 

47.
Gassmann, G. Phosphine in the fluvial and marine hydrosphere. Mar. Chem. 45, 197–205 (1994).
Article  Google Scholar 

48.
Gassmann, G. & Schorn, E. Phosphine from harbor surface sediments. Naturwissenschaften 80, 78–80 (1993).
Article  Google Scholar 

49.
Song, X. et al. Matrix-bound phosphine in sediments from Lake Illawarra, New South Wales, Australia. Mar. Pollut. Bull. 62, 1744–1750 (2011).
Article  Google Scholar 

50.
Feng, Z., Song, X. & Yu, Z. Seasonal and spatial distribution of matrix-bound phosphine and its relationship with the environment in the Changjiang River Estuary, China. Mar. Pollut. Bull. 56, 1630–1636 (2008).
Article  Google Scholar 

51.
Feng, Z., Song, X. & Yu, Z. Distribution characteristics of matrix-bound phosphine along the coast of China and possible environmental controls. Chemosphere 73, 519–525 (2008).
Article  Google Scholar 

52.
Yu, Z. & Song, X. Matrix-bound phosphine: a new form of phosphorus found in sediment of Jiaozhou Bay. Sci. Bull. 48, 31–35 (2003).
Article  Google Scholar 

53.
Mu, Q., Song, X. & Yu, Z. Matrix-bound phosphine (PH3) distribution characteristics in the sediments of Jiaozhou Bay. China Environ. Sci. 26, 135–138 (2005).
Google Scholar 

54.
Zhu, R. et al. Occurrence of matrix-bound phosphine in polar ornithogenic tundra ecosystems: effects of alkaline phosphatase activity and environmental variables. Sci. Total Environ. 409, 3789–3800 (2011).
Article  Google Scholar 

55.
Eismann, F., Glindemann, D., Bergmann, A. & Kuschk, P. Balancing phosphine in manure fermentation. J. Environ. Sci. Health B 32, 955–968 (1997).
Article  Google Scholar 

56.
Li, J.-B., Zhang, G.-L., Zhang, J., Liu, S.-M. & Ren, J.-L. Matrix bound phosphine in sediments of the yellow sea and its coastal areas. Cont. Shelf Res. 30, 743–751 (2010).
Article  Google Scholar 

57.
Han, C., Geng, J., Zhang, J., Wang, X. & Gao, S. Phosphine migration at the water–air interface in Lake Taihu, China. Chemosphere 82, 935–939 (2011).
Article  Google Scholar 

58.
Glindemann, D., Edwards, M. & Schrems, O. Phosphine and methylphosphine production by simulated lightning—a study for the volatile phosphorus cycle and cloud formation in the earth atmosphere. Atmos. Environ. 38, 6867–6874 (2004).
Article  Google Scholar 

59.
Bains, W., Petkowski, J. J., Sousa-Silva, C. & Seager, S. New environmental model for thermodynamic ecology of biological phosphine production. Sci. Total Environ. 658, 521–536 (2019).
Article  Google Scholar 

60.
Cao, H., Liu, J., Zhuang, Y. & Dietmar, G. Emission sources of atmospheric phosphine and simulation of phosphine formation. Sci. China, Ser. B: Chem. 43, 162 (2000).
Article  Google Scholar 

61.
Zhu, R. et al. Penguins significantly increased phosphine formation and phosphorus contribution in maritime Antarctic soils. Sci. Rep. 4, 1–9 (2014).
Google Scholar 

62.
Roels, J. & Verstraete, W. Biological formation of volatile phosphorus compounds. Bioresour. Technol. 79, 243–250 (2001).
Article  Google Scholar 

63.
Sun, L., Zhang, C., Zhang, K., Rong, H. & Liu, T. Effects of different phosphorus sources on phosphine production from anaerobic sludge. China Water Wastewater 28, 89–91 (2012).
Google Scholar 

64.
Jenkins, R., Morris, T.-A., Craig, P. J., Ritchie, A. & Ostah, N. Phosphine generation by mixed-and monoseptic-cultures of anaerobic bacteria. Sci. Total Environ. 250, 73–81 (2000).
Article  Google Scholar 

65.
Glindemann, D., Edwards, M. & Morgenstern, P. Phosphine from rocks: mechanically driven phosphate reduction? Environ. Sci. Technol. 39, 8295–8299 (2005).
Article  Google Scholar 

66.
Glindemann, D., Eismann, F., Bergmann, A., Kuschk, P. & Stottmeister, U. Phosphine by bio-corrosion of phosphide-rich iron. Environ. Sci. Pollut. Res. 5, 71 (1998).
Article  Google Scholar 

67.
Glindemann, D., De Graaf, R. & Schwartz, A. W. Chemical reduction of phosphate on the primitive Earth. Orig. Life Evol. Biosphere 29, 555–561 (1999).
Article  Google Scholar 

68.
Hammer, D. A. Constructed Wetlands for Wastewater Treatment: Municipal, Industrial and Agricultural (CRC Press, 1989).

69.
Whitmire, S. L. & Hamilton, S. K. Rates of anaerobic microbial metabolism in wetlands of divergent hydrology on a glacial landscape. Wetlands 28, 703–714 (2008).
Article  Google Scholar 

70.
Picek, T., Čížková, H. & Dušek, J. Greenhouse gas emissions from a constructed wetland—plants as important sources of carbon. Ecol. Eng. 31, 98–106 (2007).
Article  Google Scholar 

71.
Roels, J., Huyghe, G. & Verstraete, W. Microbially mediated phosphine emission. Sci. Total Environ. 338, 253–265 (2005).
Article  Google Scholar 

72.
Pasek, M. A role for phosphorus redox in emerging and modern biochemistry. Curr. Opin. Chem. Biol. 49, 53–58 (2019).
Article  Google Scholar 

73.
Pasek, M. A., Sampson, J. M. & Atlas, Z. Redox chemistry in the phosphorus biogeochemical cycle. Proc. Nat. Acad. Sci. USA 111, 15468–15473 (2014).
Article  Google Scholar 

74.
Han, C. et al. Phosphite in sedimentary interstitial water of Lake Taihu, a large eutrophic shallow lake in China. Environ. Sci. Technol. 47, 5679–5685 (2013).
Article  Google Scholar 

75.
Han, C. et al. Determination of phosphite in a eutrophic freshwater lake by suppressed conductivity ion chromatography. Environ. Sci. Technol. 46, 10667–10674 (2012).
Article  Google Scholar 

76.
Wang, J., Li, L., Niu, X. & Zou, D. Phosphine-induced phosphorus mobilization in the rhizosphere of rice seedlings. J. Soils Sediment. 16, 1735–1744 (2016).
Article  Google Scholar 

77.
Zhang, C.-B. et al. Responses of dissimilatory nitrate reduction to ammonium and denitrification to plant presence, plant species and species richness in simulated vertical flow constructed wetlands. Wetlands 37, 109–122 (2017).
Article  Google Scholar 

78.
Li, H., Chen, Z. & Chen, Z. Daily variation of the rhizosphere redox potential of six wetland plants. Acta Ecologica Sin. 34, 5766–5773 (2014).
Google Scholar 

79.
Fu, R., Zhu, Y. & Yang, H. DO and ORP conditions and their correlation with plant root distribution in a continuous-flow constructed wetland treating eutrophic water. Acta Scien. Circum. 28, 2036–2038 (2008).
Google Scholar 

80.
Colmer, T. Long-distance transport of gases in plants: a perspective on internal aeration and radial oxygen loss from roots. Plant Cell Environ. 26, 17–36 (2003).
Article  Google Scholar 

81.
Liu, S., Li, T., Ning, P., Wu, M. & Yu, S. Research progress of the release, distribution and transformation of phosphine in environment. Chem. Ind. Eng. Prog. 38, 1085–1096 (2019).
Google Scholar 

82.
Wang, D., Leng, B., An, X. & Lu, Q. Effects of UV light wave, temperature and humidity on phosphine concentration degrading. Plant Quar. 27, 45–49 (2013).
Google Scholar 

83.
Chen, L., Arimoto, R. & Duce, R. A. The sources and forms of phosphorus in marine aerosol particles and rain from northern New Zealand. Atmos. Environ. 19, 779–787 (1985).
Article  Google Scholar 

84.
Chen, H. Y. & Chen, L. D. Importance of anthropogenic inputs and continental‐derived dust for the distribution and flux of water‐soluble nitrogen and phosphorus species in aerosol within the atmosphere over the East China Sea. J. Geophys. Res. Atmos. 113, D11303 (2008).
Article  Google Scholar 

85.
Xu, Z. et al. Dry and wet atmospheric deposition of nitrogen and phosphorus in Taihu Lake. Environ. Monit. Forewarning 119, 37–42 (2019).
Google Scholar 

86.
Ma, Z., Zhang, Q. & Qin, Y. Numerical simulation and analysis of the effect of Three Gorges reservoir project on the regional climate change. Res. Environ. Yangtze Basin 19, 1044–1052 (2010).
Google Scholar 

87.
Wang, M., Zhou, Y., Ren, Y. & Fang, S. Spatial-temporal change characteristics of precipitation over the key region of Three Gorges Reservoir. Meteorol. Environ. Sci. 40, 40–46 (2017).
Google Scholar 

88.
Zhang, S., Lu, Z. & Zhang, N. Analysis of influence of Three Gorges Dam storage on reservoir region precipitation. Water Resour. Power 31, 21–24 (2013).
Google Scholar 

89.
Zhang, H. Potential pitfalls in “Hongqi River” water transfer proposal and drought management in Northwest China. Water Resource Prot. 2, 8–11 (2018).
Google Scholar 

90.
Prinn, R. G. The interactive atmosphere: global atmospheric-biospheric chemistry. Ambio 23, 50–61 (1994).
Google Scholar 

91.
Kleemann, R. et al. Evaluation of local and national effects of recovering phosphorus at wastewater treatment plants: Lessons learned from the UK. Resour. Conserv. Recycl. 105, 347–359 (2015).
Article  Google Scholar 

92.
Pradel, M. & Aissani, L. Environmental impacts of phosphorus recovery from a “product” Life Cycle Assessment perspective: allocating burdens of wastewater treatment in the production of sludge-based phosphate fertilizers. Sci. Total Environ. 656, 55–69 (2019).
Article  Google Scholar 

93.
Humphrey, C. P., Anderson-Evans, E., O’Driscoll, M., Manda, A. & Iverson, G. Comparison of phosphorus concentrations in coastal plain watersheds served by onsite wastewater treatment systems and a municipal sewer treatment system. Water Air Soil Pollut. 226, 2259 (2015).
Article  Google Scholar 

94.
Ding, L.-L. et al. Distribution of phosphine and phosphorus balance in a full-scale UASB system. J. Nanjing Uni. Nat. Sci. 41, 620–626 (2005).
Google Scholar 

95.
Yang, Z., Zhou, J., Li, J., Han, Y. & He, Q. Pre-processing of raw wastewater in a septic tank leads to phosphorus removal by phosphine production in a sequencing batch biofilm reactor (SBBR). Desalination Water Treat. 57, 810–818 (2016).
Article  Google Scholar 

96.
Liu, W., Niu, X., Chen, W., An, S. & Sheng, H. Effects of applied potential on phosphine formation in synthetic wastewater treatment by Microbial Electrolysis Cell (MEC). Chemosphere 173, 172–179 (2017).
Article  Google Scholar 

97.
Yang, S. & Yao, G. Simultaneous removal of concentrated organics, nitrogen and phosphorus nutrients by an oxygen-limited membrane bioreactor. PLoS ONE 13, e0202179 (2018).
Article  Google Scholar 

98.
Zhang, P., Rong, H., Zhang, K., Liu, T. & Cao, Y. Effect of diverse mud and phosphorus sources on total phosphorus removal efficiencies. Guangdong Chem. Ind. 56, 118–119 (2011).
Google Scholar 

99.
Han, S., Zhuang, Y., Zhang, H., Wang, Z. & Yang, J. Phosphine and methane generation by the addition of organic compounds containing carbon–phosphorus bonds into incubated soil. Chemosphere 49, 651–657 (2002).
Article  Google Scholar 

100.
Rutishauser, B. V. & Bachofen, R. Phosphine formation from sewage sludge cultures. Anaerobe 5, 525–531 (1999).
Article  Google Scholar 

101.
Wan, J., Deng, M., He, H. & Tang, A. Factors influencing release of phosphine in piggery wastewater. China Water Wastewater 23, 117–120 (2013).
Google Scholar 

102.
Fan, Y., Lv, M., Niu, X., Ma, J. & Song, Q. Evidence and mechanism of biological formation of phosphine from the perspective of the tricarboxylic acid cycle. Int. Biodeterior. Biodegrad. 146, 104791 (2020).
Article  Google Scholar 

103.
Fan, Y. et al. Analysis of the characteristics of phosphine production by anaerobic digestion based on microbial community dynamics, metabolic pathways, and isolation of the phosphate-reducing strain. Chemosphere 262, 128213 (2021).
Article  Google Scholar 

104.
Wang, R. et al. Significant contribution of combustion-related emissions to the atmospheric phosphorus budget. Nat. Geosci. 8, 48 (2015).
Article  Google Scholar  More

1.
Fenner, D. Field guide to the coral species of the Samoan archipelago: American Samoa and independent Samoa 1–422 (American Samoa Dept. Marine & Wildlife Resources, 2016).
2.
NOAA. US Coral Reef Monitoring Data Summary 2018. NOAA Coral Reef Conservation Program. NOAA Technical Memorandum CRCP 31, https://doi.org/10.25923/g0v0-nm61 (2018).

3.
Fenner, D., et al. (2008) The State of Coral Reef Ecosystems of American Samoa in The State of Coral Reef Ecosystems of the United States and Pacific Freely Associated States (ed. Waddell, J) 307–351 (NOAA Technical Memorandum NOS NCCOS 11. NOAA/NCCOS Center for Coastal Monitoring and Assessment’s Biogeography Team, 2008).

4.
Birkeland, C. et al. Geologic setting and ecological functioning of coral reefs in American Samoa. In Coral reefs of the USA, pp 741–765 (eds Riegl, B. & Dodge, R.) (Springer, Berlin, 2008).
Google Scholar 

5.
Ennis, R. S., Brandt, M. E., Grimes, K. R. W. & Smith, T. B. Coral reef health response to chronic and acute changes in water quality in St. Thomas, United States Virgin Islands. Mar. Poll. Bull. 111, 418–427 (2016).
CAS  Article  Google Scholar 

6.
Webster, P. J., Holland, G. J., Curry, J. A. & Chang, H. R. Changes in tropical cyclone number, duration, and intensity in a warming environment. Science 309, 1844–1846 (2005).
ADS  CAS  Article  Google Scholar 

7.
Knutson, T. R. et al. Tropical cyclones and climate change. Nat. Geosci. 3, 157–163 (2010).
ADS  CAS  Article  Google Scholar 

8.
Hughes, T. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).
ADS  CAS  Article  Google Scholar 

9.
NOAA Coral Reef Watch. NOAA Coral Reef Watch Version 3.1 Daily Global 5-km Satellite Virtual Station Time Series Data for Samoas, Jan 01, 2013-April 07, 2020. Data set accessed 08 Apr 2020. College Park, Maryland, https://coralreefwatch.noaa.gov/product/5km/index.php, (2018, updated daily).

10.
Fenner, D. The Samoan Archipelago. In World Seas: An Environmental Evaluation: Volume II: The Indian Ocean to the Pacific (ed. Sheppard, C.) 619–643 (Academic Press, Cambridge, 2018).
Google Scholar 

11.
NOAA Physical Science Laboratory. Oceanic Niño Index for SST 5N-5S, 170W-120W from 1950 to 2020. Data set accessed 29 May 2020. Boulder, Colorado. https://psl.noaa.gov/data/correlation/oni.data (2015, updated monthly).

12.
Koppers, A. A. et al. Samoa reinstated as a primary hotspot trail. Geology 36, 435–438 (2008).
ADS  CAS  Article  Google Scholar 

13.
Brown, D. P. et al. American Samoa’s island of giants: massive Porites colonies at Ta’u island. Coral Reefs 28, 735 (2009).
ADS  Article  Google Scholar 

14.
Forsman, Z. H., Barshis, D. J., Hunter, C. L. & Toonen, R. J. Shape-shifting corals: molecular markers show morphology is evolutionarily plastic in Porites. BMC Evol. Biol. 9, 45 (2009).
Article  Google Scholar 

15.
Tangri, N., Dunbar, R. B., Linsley, B. K. & Mucciarone, D. M. ENSO’s shrinking twentieth-century footprint revealed in a half-millennium coral core from the South Pacific Convergence Zone. Paleoceanogr. Paleoclimatol. 33, 1136–1150 (2018).
ADS  Article  Google Scholar 

16.
Done, T. J. & Potts, D. C. Influences of habitat and natural disturbances on contributions of massive Porites corals to reef communities. Mar. Biol. 114, 479–493 (1992).
Article  Google Scholar 

17.
Potts, D. C., Done, T. J., Isdale, P. J. & Fisk, D. A. Dominance of a coral community by the genus Porites (Scleractinia). Mar. Ecol. Prog. Ser. 23, 79–84 (1985).
ADS  Article  Google Scholar 

18.
Soong, K., Chen, C. A. & Chang, J. C. A very large poritid colony at Green Island, Taiwan. Coral Reefs 18, 42 (1999).
Article  Google Scholar 

19.
Takeuchi, I. & Yamashiro, H. Large Porites microatoll found by aerial survey at Sesoko Island, Okinawa, Japan. Coral Reefs 36, 1317 (2017).
ADS  Article  Google Scholar 

20.
Pacific Islands Fisheries Science Center (PIFSC). Coral reef ecosystems of American Samoa: a 2002–2010 overview. (NOAA Fisheries Pacific Islands Fisheries Center, PIFSC Special Publication, 2011).

21.
Barstow, S.F. & Haug, O. The wave climate of Western Samoa. SOPAC Technical Report 204. 34 pp. (1994).

22.
Pirhalla, D., Ransi, V., Kendall, M., & Fenner, D. Oceanography of the Samoan Archipelago in A biogeographic assessment of the Samoan Archipelago. 3–26. (eds. Kendall, M & Poti, M) 3–26 (NOAA Tech. Memo NOS NCCOS 132, Silver Springs, Maryland, 2011).

23.
U.S. Army Corps of Engineers Wave Information Studies WISWAVE Hindcast model. Average Wave Height, Maximum Wave Height, Wave Direction at 14.5°S, 170°W from 1980–2011. Data set accessed 10 Jan 2012. http://frf.usace.army.mil/wis/.

24.
Lough, J. M. & Barnes, D. J. Several centuries of variation in skeletal extension, density and calcification in massive Porites colonies from the Great Barrier Reef: a proxy for seawater temperature and a background of variability against which to identify unnatural change. J. Exp. Mar. Biol. Ecol. 211, 29–67 (1997).
Article  Google Scholar 

25.
Lough, J. M. Coral calcification from skeletal records revisited. Mar. Ecol. Prog. Ser. 373, 257–264 (2008).
ADS  Article  Google Scholar 

26.
Environmental Research Division’s Data Access Program. HadISST Average Sea Surface Temperature, 1°, Global, Monthly, 1870-present. Data set accessed 04 Apr 2020. https://coastwatch.pfeg.noaa.gov/erddap/griddap/erdHadISST.html (2020).

27.
Qiu, B. & Chen, S. Seasonal modulations in the eddy field of the South Pacific Ocean. J. Phys. Oceanogr. 34, 1515–1527 (2004).
ADS  Article  Google Scholar 

28.
Kendall, M.S., Poti, M., T. Wynne, B. Kinlan, L. Bauer. Ocean Currents and Larval Transport Among Islands and Shallow Seamounts of the Samoan Archipelago and Adjacent Island Nations. In A Biogeographic Assessment of the Samoan Archipelago (eds. Kendall, M & Poti, M) 3–26 (NOAA Tech. Memo NOS NCCOS 132, Silver Springs, Maryland, 2011).

29.
Wall, M. et al. Large-amplitude internal waves benefit corals during thermal stress. Proc. Biol. Sci 282, 20140650 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

30.
Kavousi, J. & Keppel, G. Clarifying the concept of climate change refugia for coral reefs. ICES J. Mar. Sci. 75, 43–49 (2018).
Article  Google Scholar 

31.
Langlais, C. E. et al. Coral bleaching pathways under the control of regional temperature variability. Nat. Clim. Change 7, 839–844 (2017).
ADS  Article  Google Scholar 

32.
Hendy, E. J., Lough, J. M. & Gagan, M. K. Historical mortality in massive Porites from the central Great Barrier Reef, Australia: evidence for past environmental stress?. Coral Reefs 22, 207–215 (2003).
Article  Google Scholar 

33.
Roff, G. et al. Porites and the Phoenix effect: unprecedented recovery after a mass coral bleaching event at Rangiroa Atoll, French Polynesia. Mar Biol. 161, 1385–1393 (2014).
Article  Google Scholar 

34.
Barkley, H. C. et al. Repeat bleaching of a central Pacific coral reef over the past six decades (1960–2016). Commun. Biol. 1, 1–10 (2018).
Article  Google Scholar 

35.
Loya, Y., Sakai, K., Nakano, Y., Sambali, H. & van Woesik, R. Coral bleaching: the winners and the losers. Ecol. Lett. 4, 122–131 (2001).
Article  Google Scholar 

36.
Edmunds, P. J., Putnam, H. M. & Gates, R. D. Photophysiological consequences of vertical stratification of Symbiodinium in tissue of the coral Porites lutea. Biol. Bull. 223, 226–235 (2012).
CAS  Article  Google Scholar  More