Philippa Kaur

1.
Mills, K. E. et al. Fisheries management in a changing climate: lessons from the 2012 ocean heat wave in the Northwest Atlantic. Oceanography 26, 191–195 (2013).
Google Scholar 
2.
Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).
Google Scholar 

3.
Smale, D. A. et al. Marine heatwaves threaten global biodiversity and the provision of ecosystem services. Nat. Clim. Change 9, 306–312 (2019).
Google Scholar 

4.
Babcock, R. C. et al. Severe continental-scale impacts of climate change are happening now: Extreme climate events impact marine habitat forming communities along 45% of Australia’s coast. Front. Mar. Sci. 6, 411 (2019).
Google Scholar 

5.
Garrabou, J. et al. Mass mortality in Northwestern Mediterranean rocky benthic communities: effects of the 2003 heat wave. Glob. Change Biol. 15, 1090–1103 (2009).
Google Scholar 

6.
Benthuysen, J. A., Oliver, E. C. J., Chen, K. & Wernberg, T. Advances in understanding marine heatwaves and their impacts. Front. Mar. Sci. 7, 147 (2020).
Google Scholar 

7.
Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. Science 353, 169–172 (2016).
Google Scholar 

8.
Wernberg, T. Marine heatwave drives collapse of kelp forests in Western Australia. In Ecosystem Collapse and Climate Change. Ecological Studies (eds. Canadell, J. G. & Jackson, R. B.) (Springer-Nature, 2020).

9.
Pearce, A. et al. The “Marine Heat Wave” Off Western Australia During the Summer of 2010/11. Fisheries Research Report No. 222 (40pp) (Department of Fisheries, Western Australia, 2011)

10.
Olita, A. et al. Effects of the 2003 European heatwave on the Central Mediterranean Sea: surface fluxes and the dynamical response. Ocean Sci. 3, 273–289 (2007).
Google Scholar 

11.
Pearce, A. F. & Feng, M. The rise and fall of the ‘marine heat wave’ off Western Australia during the summer of 2010/2011. J. Mar. Syst. 111–112, 139–156 (2013).
Google Scholar 

12.
Chen, K., Gawarkiewicz, G. G., Lentz, S. J. & Bane, J. M. Diagnosing the warming of the Northeastern U.S. Coastal Ocean in 2012: A linkage between the atmospheric jet stream variability and ocean response. J. Geophys. Res. Oceans 119, 218–227 (2014).
Google Scholar 

13.
Oliver, E. C. J. et al. The unprecedented 2015/16 Tasman Sea marine heatwave. Nat. Commun. 8, 16101 (2017).
Google Scholar 

14.
Holbrook, N. J. et al. A global assessment of marine heatwaves and their drivers. Nat. Commun. 10, 2624 (2019).
Google Scholar 

15.
Di Lorenzo, E. & Mantua, N. Multi-year persistence of the 2014/15 North Pacific marine heatwave. Nat. Clim. Change 6, 1042–1047 (2016).
Google Scholar 

16.
Jackson, J. M., Johnson, G. C., Dosser, H. V. & Ross, T. Warming from recent marine heatwave lingers in deep British Columbia fjord. Geophys. Res. Lett. 45, 9757–9764 (2018).
Google Scholar 

17.
Reed, D. et al. Extreme warming challenges sentinel status of kelp forests as indicators of climate change. Nat. Commun. 7, 13757 (2016).
Google Scholar 

18.
Jacox, M., Tommasi, D., Alexander, M., Hervieux, G. & Stock, C. Predicting the evolution of the 2014-16 California Current System marine heatwave from an ensemble of coupled global climate forecasts. Front. Mar. Sci. 6, 497 (2019).
Google Scholar 

19.
Lee, T. et al. Record warming in the South Pacific and western Antarctica associated with the strong central-Pacific El Niño in 2009–10. Geophys. Res. Lett. 37, L19704 (2010).
Google Scholar 

20.
Benthuysen, J. A., Oliver, E. C. J., Feng, M. & Marshall, A. G. Extreme marine warming across tropical Australia during austral summer 2015–2016. J. Geophys. Res. Oceans 123, 1301–1326 (2018).
Google Scholar 

21.
Eakin, C. M., Sweatman, H. P. A. & Brainard, R. E. The 2014–2017 global-scale coral bleaching event: insights and impacts. Coral Reefs 38, 539–545 (2019).
Google Scholar 

22.
Gurgel, C. F. D., Camacho, O., Minne, A. J. P., Wernberg, T. & Coleman, M. A. Marine heatwave drives cryptic loss of genetic diversity in underwater forests. Curr. Biol. 30, 1199–1206 (2020).
Google Scholar 

23.
Caputi, N. et al. Management adaptation of invertebrate fisheries to an extreme marine heat wave event at a global warming hot spot. Ecol. Evol. 6, 3583–3593 (2016).
Google Scholar 

24.
Caputi, N. et al. Factors affecting the recovery of invertebrate stocks from the 2011 Western Australian extreme marine heatwave. Front. Mar. Sci. 6, 484 (2019).
Google Scholar 

25.
Caputi, N. et al. Management Implications of Climate Change Effect on Fisheries in Western Australia, Part 2: Case Studies. Fisheries Research Report No. 261 (156pp) (Department of Fisheries, Western Australia, 2015).

26.
Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377 (2017).
Google Scholar 

27.
Wernberg, T. et al. An extreme climatic event alters marine ecosystem structure in a global biodiversity hotspot. Nat. Clim. Change 3, 78–82 (2013).
Google Scholar 

28.
Thomsen, M. S. et al. Local extinction of bull kelp (Durvillaea spp.) due to a marine heatwave. Front. Mar. Sci. 6, 84 (2019).
Google Scholar 

29.
Arafeh-Dalmau, N. et al. Extreme marine heatwaves alter kelp forest community near its equatorward distribution limit. Front. Mar. Sci. 6, 499 (2019).
Google Scholar 

30.
Arias-Ortiz, A. et al. A marine heatwave drives massive losses from the world’s largest seagrass carbon stocks. Nat. Clim. Change 8, 338–344 (2018).
Google Scholar 

31.
Zisserson, B. & Cook, A. Impact of bottom water temperature change on the southernmost snow crab fishery in the Atlantic Ocean. Fish. Res. 195, 12–18 (2017).
Google Scholar 

32.
Caputi, N., Jackson, G. & Pearce, A. The Marine Heat Wave Off Western Australia During the Summer of 2010/11 – 2 Years On. Fisheries Research Report No. 250 (40pp) (Department of Fisheries, Western Australia, 2014).

33.
Cavole, L. M. et al. Biological Impacts of the 2013–2015 warm-water anomaly in the northeast Pacific: winners, losers, and the future. Oceanography 29, 273–285 (2016).
Google Scholar 

34.
Santora, J. A. et al. Habitat compression and ecosystem shifts as potential links between marine heatwave and record whale entanglements. Nat. Commun. 11, 536 (2020).
Google Scholar 

35.
Oliver, E. C. J. et al. Longer and more frequent marine heatwaves over the past century. Nat. Commun. 9, 1324 (2018).
Google Scholar 

36.
Frölicher, T. L., Fischer, E. M. & Gruber, N. Marine heatwaves under global warming. Nature 560, 360–364 (2018).
Google Scholar 

37.
Oliver, E. C. J. et al. Projected marine heatwaves in the 21st century and the potential for ecological impact. Front. Mar. Sci. 6, 734 (2019).
Google Scholar 

38.
Bond, N. A., Cronin, M. F., Freeland, H. & Mantua, N. Causes and impacts of the 2014 warm anomaly in the NE Pacific. Geophys. Res. Lett. 42, 3414–3420 (2015).
Google Scholar 

39.
Rodrigues, R. R., Taschetto, A. S., Sen Gupta, A. & Foltz, G. R. Common cause for severe droughts in South America and marine heatwaves in the South Atlantic. Nat. Geosci. 12, 620–626 (2019).
Google Scholar 

40.
Black, E., Blackburn, M., Harrison, R. G., Hoskins, B. J. & Methven, J. Factors contributing to the summer 2003 European heatwave. Weather 59, 217–223 (2004).
Google Scholar 

41.
Chen, K., Gawarkiewicz, G., Kwon, Y.-O. & Zhang, W. The role of atmospheric forcing versus ocean advection during the extreme warming of the Northeast U.S. continental shelf in 2012. J. Geophys. Res. Oceans 120, 4324–4339 (2015).
Google Scholar 

42.
Salinger, M. J. et al. The unprecedented coupled ocean-atmosphere summer heatwave in the New Zealand region 2017/18: Drivers, mechanisms and impacts. Environ. Res. Lett. 14, 044023 (2019).
Google Scholar 

43.
Perkins-Kirkpatrick, S. E. et al. The role of natural variability and anthropogenic climate change in the 2017/18 Tasman Sea marine heatwave. Bull. Am. Meteorol. Soc. 100, S105–S110 (2019).
Google Scholar 

44.
Sparnocchia, S., Schiano, M. E., Picco, P., Bozzano, R. & Cappelletti, A. The anomalous warming of summer 2003 in the surface layer of the Central Ligurian Sea (Western Mediterranean). Ann. Geophys. 24, 443–452 (2006).
Google Scholar 

45.
Swain, D. L. et al. The extraordinary California drought of 2013/2014: Character, context, and the role of climate change. Bull. Am. Meteorol. Soc. 95, S3–S7 (2014).
Google Scholar 

46.
Alexander, M. A., Deser, C. & Timlin, M. S. The reemergence of SST anomalies in the North Pacific Ocean. J. Clim. 12, 2419–2433 (1999).
Google Scholar 

47.
Benthuysen, J., Feng, M. & Zhong, L. Spatial patterns of warming off Western Australia during the 2011 Ningaloo Niño: Quantifying impacts of remote and local forcing. Cont. Shelf Res. 91, 232–246 (2014).
Google Scholar 

48.
Kataoka, T., Tozuka, T. & Yamagata, T. Generation and decay mechanisms of Ningaloo Niño/Niña. J. Geophys. Res. Oceans 122, 8913–8932 (2017).
Google Scholar 

49.
Behrens, E., Fernandez, D. & Sutton, P. Meridional oceanic heat transport influences marine heatwaves in the Tasman Sea on interannual to decadal timescales. Front. Mar. Sci. 6, 228 (2019).
Google Scholar 

50.
Scannell, H. A., Pershing, A. J., Alexander, M. A., Thomas, A. C. & Mills, K. E. Frequency of marine heatwaves in the North Atlantic and North Pacific since 1950. Geophys. Res. Lett. 43, 2069–2076 (2016).
Google Scholar 

51.
Hartmann, D. L. Pacific sea surface temperature and the winter of 2014. Geophys. Res. Lett. 42, 1894–1902 (2015).
Google Scholar 

52.
Marshall, A. G. & Hendon, H. H. Impacts of the MJO in the Indian Ocean and on the Western Australian coast. Clim. Dyn. 42, 579–595 (2014).
Google Scholar 

53.
Zhang, N., Feng, M., Hendon, H. H., Hobday, A. J. & Zinke, J. Opposite polarities of ENSO drive distinct patterns of coral bleaching potentials in the southeast Indian Ocean. Sci. Rep. 7, 2443 (2017).
Google Scholar 

54.
Kataoka, T., Tozuka, T., Behera, S. & Yamagata, T. On the Ningaloo Niño/Niña. Clim. Dyn. 43, 1463–1482 (2013).
Google Scholar 

55.
Holbrook, N. J., Goodwin, I. D., McGregor, S., Molina, E. & Power, S. B. ENSO to multi-decadal time scale changes in East Australian Current transports and Fort Denison sea level: Oceanic Rossby waves as the connecting mechanism. Deep Sea Res. Part II Top. Stud. Oceanogr. 58, 547–558 (2011).
Google Scholar 

56.
Li, Z., Holbrook, N. J., Zhang, X., Oliver, E. C. J. & Cougnon, E. A. Remote forcing of Tasman Sea marine heatwaves. J. Clim. 33, 5337–5354 (2020).
Google Scholar 

57.
Schaeffer, A. & Roughan, M. Subsurface intensification of marine heatwaves off southeastern Australia: The role of stratification and local winds. Geophys. Res. Lett. 44, 5025–5033 (2017).
Google Scholar 

58.
Elzahaby, Y. & Schaeffer, A. Observational Insight Into the subsurface anomalies of marine heatwaves. Front. Mar. Sci. 6, 745 (2019).
Google Scholar 

59.
Ridgway, K. R., Dunn, J. R. & Wilkin, J. L. Ocean interpolation by four-dimensional weighted least squares — Application to the waters around Australasia. J. Atmos. Ocean. Technol. 19, 1357–1375 (2002).
Google Scholar 

60.
Roemmich, D. & Gilson, J. The 2004–2008 mean and annual cycle of temperature, salinity, and steric height in the global ocean from the Argo Program. Prog. Oceanogr. 82, 81–100 (2009).
Google Scholar 

61.
Moltmann, T. The “coastal data paradox”. J. Ocean Technol. 13, 148–149 (2018).
Google Scholar 

62.
Oliver, E. C. J. et al. Marine heatwaves off eastern Tasmania: Trends, interannual variability, and predictability. Prog. Oceanogr. 161, 116–130 (2018).
Google Scholar 

63.
Darmaraki, S. et al. Future evolution of marine heatwaves in the Mediterranean Sea. Clim. Dyn. 53, 1371–1392 (2019).
Google Scholar 

64.
Schlegel, R. W., Oliver, E. C. J., Hobday, A. J. & Smit, A. J. Detecting marine heatwaves with sub-optimal data. Front. Mar. Sci. 6, 737 (2019).
Google Scholar 

65.
Salinger, J. et al. Decadal-scale forecasting of climate drivers for marine applications. Adv. Mar. Biol. 74, 1–68 (2016).
Google Scholar 

66.
Dunstan, P. K. et al. How can climate predictions improve sustainability of coastal fisheries in Pacific Small-Island Developing States? Mar. Policy 88, 295–302 (2018).
Google Scholar 

67.
Smith, G. & Spillman, C. New high-resolution sea surface temperature forecasts for coral reef management on the Great Barrier Reef. Coral Reefs 38, 1039–1056 (2019).
Google Scholar 

68.
White, C. J. et al. Potential applications of subseasonal-to-seasonal (S2S) predictions. Meteorol. Appl. 24, 315–325 (2017).
Google Scholar 

69.
Hobday, A. J., Spillman, C. M., Paige Eveson, J. & Hartog, J. R. Seasonal forecasting for decision support in marine fisheries and aquaculture. Fish. Oceanogr. 25, 45–56 (2016).
Google Scholar 

70.
Game, E. T., Watts, M. E., Wooldridge, S. & Possingham, H. P. Planning for persistence in marine reserves: a question of catastrophic importance. Ecol. Appl. 18, 670–680 (2008).
Google Scholar 

71.
Johnson, C. R., Chabot, R. H., Marzloff, M. P. & Wotherspoon, S. Knowing when (not) to attempt ecological restoration. Restor. Ecol. 25, 140–147 (2017).
Google Scholar 

72.
Tommasi, D. et al. Managing living marine resources in a dynamic environment: the role of seasonal to decadal climate forecasts. Prog. Oceanogr. 152, 15–49 (2017).
Google Scholar 

73.
Marshall, A. G., Hendon, H. H., Feng, M. & Schiller, A. Initiation and amplification of the Ningaloo Niño. Clim. Dyn. 45, 2367–2385 (2015).
Google Scholar 

74.
D’Andrea, F. et al. Northern Hemisphere atmospheric blocking as simulated by 15 atmospheric general circulation models in the period 1979–1988. Clim. Dyn. 14, 385–407 (1998).
Google Scholar 

75.
Scaife, A. A., Woollings, T., Knight, J., Martin, G. & Hinton, T. Atmospheric blocking and mean biases in climate models. J. Clim. 23, 6143–6152 (2010).
Google Scholar 

76.
Davini, P. & D’Andrea, F. Northern Hemisphere atmospheric blocking representation in global climate models: Twenty years of improvements? J. Clim. 29, 8823–8840 (2016).
Google Scholar 

77.
Kwon, Y. O., Camacho, A., Martinez, C. & Seo, H. North Atlantic winter eddy-driven jet and atmospheric blocking variability in the Community Earth System Model version 1 Large Ensemble simulations. Clim. Dyn. 51, 3275–3289 (2018).
Google Scholar 

78.
Pilo, G. S., Mata, M. M. & Azevedo, J. L. L. Eddy surface properties and propagation at Southern Hemisphere western boundary current systems. Ocean Sci. 11, 629–641 (2015).
Google Scholar 

79.
Oliver, E. C. J., Wotherspoon, S. J., Chamberlain, M. A. & Holbrook, N. J. Projected Tasman Sea extremes in sea surface temperature through the twenty-first century. J. Clim. 27, 1980–1998 (2014).
Google Scholar 

80.
Oliver, E. C. J., O’Kane, T. J. & Holbrook, N. J. Projected changes to Tasman Sea eddies in a future climate. J. Geophys. Res. Oceans 120, 7150–7165 (2015).
Google Scholar 

81.
Hu, Z-Z., Kumar, A., Jha, B., Zhu, J. & Huang, B. Persistence and predictions of the remarkable warm anomaly in the northeastern Pacific ocean during 2014–16. J. Clim. 30, 689–702 (2017).
Google Scholar 

82.
Spillman, C. M. Operational real-time seasonal forecasts for coral reef management. J. Oper. Oceanogr. 4, 13–22 (2011).
Google Scholar 

83.
Yang, Y. et al. A CFCC-LSTM model for sea surface temperature prediction. IEEE Geosci. Remote Sens. Lett. 15, 207–211 (2018).
Google Scholar 

84.
Hobday, A. J. et al. Ethical considerations and unanticipated consequences associated with ecological forecasting for marine resources. ICES J. Mar. Sci. 76, 1244–1256 (2019).
Google Scholar 

85.
Quinting, J. F. & Reeder, M. J. Southeastern Australian heat waves from a trajectory viewpoint. Mon. Wea. Rev. 145, 4109–4125 (2017).
Google Scholar 

86.
Quinting, J. F., Parker, T. J. & Reeder, M. J. Two synoptic routes to subtropical heat waves as illustrated in the Brisbane region of Australia. Geophys. Res. Lett. 45, 10,700–10,708 (2018).
Google Scholar 

87.
Doblin, M. A. & Van Sebille, E. Drift in ocean currents impacts intergenerational microbial exposure to temperature. Proc. Natl Acad. Sci. USA 113, 5700–5705 (2016).
Google Scholar 

88.
Zhang, X., Cornuelle, B. & Roemmich, D. Sensitivity of western boundary transport at the mean north equatorial current bifurcation latitude to wind forcing. J. Phys. Oceanogr. 42, 2056–2072 (2012).
Google Scholar 

89.
Pecl, G. T. et al. Autonomous adaptation to climate-driven change in marine biodiversity in a global marine hotspot. Ambio 48, 1498–1515 (2019).
Google Scholar 

90.
Serrao-Neumann, S. et al. Marine governance to avoid tipping points: can we adapt the adaptability envelope? Mar. Policy 65, 56–67 (2016).
Google Scholar 

91.
Hobday, A. J. et al. A framework for combining seasonal forecasts and climate projections to aid risk management for fisheries and aquaculture. Front. Mar. Sci. 5, 137 (2018).
Google Scholar 

92.
Wernberg, T. et al. Impacts of climate change in a global hotspot for temperate marine biodiversity and ocean warming. J. Exp. Mar. Biol. Ecol. 400, 7–16 (2011).
Google Scholar 

93.
Strain, E. M. A., Thomson, R. J., Micheli, F., Mancuso, F. P. & Airoldi, L. Identifying the interacting roles of stressors in driving the global loss of canopy-forming to mat-forming algae in marine ecosystems. Glob. Change Biol. 20, 3300–3312 (2014).
Google Scholar 

94.
Bates, A. E. et al. Resilience and signatures of tropicalization in protected reef fish communities. Nat. Clim. Change 4, 62–67 (2014).
Google Scholar 

95.
Connell, S. D. & Ghedini, G. Resisting regime-shifts: the stabilising effect of compensatory processes. Trends Ecol. Evol. 30, 513–515 (2015).
Google Scholar 

96.
Bruno, J. F., Côté, I. M. & Toth, L. T. Climate change, coral loss, and the curious case of the parrotfish paradigm: Why don’t marine protected areas improve reef resilience? Annu. Rev. Mar. Sci. 11, 307–334 (2019).
Google Scholar 

97.
Coleman, M. A. & Goold, H. D. Harnessing synthetic biology for kelp forest conservation1. J. Phycol. 55, 745–751 (2019).
Google Scholar 

98.
Vergés, A. et al. Tropicalisation of temperate reefs: Implications for ecosystem functions and management actions. Funct. Ecol. 33, 1000–1013 (2019).
Google Scholar 

99.
Wernberg, T., Krumhansl, K., Filbee-Dexter, K. & Pedersen, M. F. in World Seas: An Environmental Evaluation 2nd edn (ed. Sheppard, C.) 57–78 (Academic, 2019).

100.
Filbee-Dexter, K. & Smajdor, A. Ethics of assisted evolution in marine conservation. Front. Mar. Sci. 6, 20 (2019).
Google Scholar 

101.
Hobday, A. J. et al. Categorizing and naming marine heatwaves. Oceanography 31, 162–173 (2018).
Google Scholar 

102.
Chandrapavan, A., Caputi, N. & Kangas, M. I. The decline and recovery of a crab population from an extreme marine heatwave and a changing climate. Front. Mar. Sci. 6, 510 (2019).
Google Scholar 

103.
Oliver, E. C. J. Mean warming not variability drives marine heatwave trends. Clim. Dyn. 53, 1653–1659 (2019).
Google Scholar 

104.
Jacox, M. G. Marine heatwaves in a changing climate. Nature 571, 485–487 (2019).
Google Scholar 

105.
Vinagre, C. et al. Upper thermal limits and warming safety margins of coastal marine species – Indicator baseline for future reference. Ecol. Indic. 102, 644–649 (2019).
Google Scholar 

106.
Nakamura, N. & Huang, C. S. Y. Atmospheric blocking as a traffic jam in the jet stream. Science 361, 42–47 (2018).
Google Scholar 

107.
Mann, M. E. et al. Projected changes in persistent extreme summer weather events: the role of quasi-resonant amplification. Sci. Adv. 4, eaat3272 (2018).
Google Scholar 

108.
Straub, S. C. et al. Resistance, extinction, and everything in between – the diverse responses of seaweeds to marine heatwaves. Front. Mar. Sci. 6, 763 (2019).
Google Scholar 

109.
Frölicher, T. L. & Laufkötter, C. Emerging risks from marine heat waves. Nat. Commun. 9, 650 (2018).
Google Scholar 

110.
Jacox, M. G. et al. Seasonal-to-interannual prediction of North American coastal marine ecosystems: Forecast methods, mechanisms of predictability, and priority developments. Prog. Oceanogr. 183, 102307 (2020).
Google Scholar 

111.
Feng, M., McPhaden, M. J., Xie, S. P. & Hafner, J. La Niña forces unprecedented Leeuwin Current warming in 2011. Sci. Rep. 3, 1227 (2013).
Google Scholar 

112.
Gammelsrød, T., Bartholomae, C. H., Boyer, D. C., Filipe, V. L. L. & O’Toole, M. J. Intrusion of warm surface water along the Angolan Namibian coast in February–March 1995: the 1995 Benguela Niño. S. Afr. J. Mar. Sci. 19, 41–56 (1998).
Google Scholar 

113.
Spencer, T., Teleki, K. A., Bradshaw, C. & Spalding, M. D. Coral bleaching in the southern Seychelles during the 1997–1998 Indian Ocean warm event. Mar. Pollut. Bull. 40, 569–586 (2000).
Google Scholar 

114.
McPhaden, M. J. Genesis and evolution of the 1997-98 El Niño. Science 283, 950–954 (1999).
Google Scholar 

115.
Vivekanandan, E., Hussain Ali, M., Jasper, B. & Rajagopalan, M. Thermal thresholds for coral bleaching in the Indian seas. J. Mar. Biol. Assoc. India 50, 209–214 (2008).
Google Scholar 

116.
Krishnan, P. et al. Elevated sea surface temperature during May 2010 induces mass bleaching of corals in the Andaman. Curr. Sci. 100, 111–117 (2011).
Google Scholar 

117.
Collins, M. et al. in IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (eds Pörtner, H.-O. et al.) 589–655 (Intergovernmental Panel on Climate Change (IPCC), 2019).

118.
Frölicher, T. L. in Predicting Future Oceans (eds Cisneros-Montemayor, A. M., Cheung, W. W. L. & Yoshitaka, O.) 53–60 (Elsevier, 2019).

119.
Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. Atmos. 108, 4407 (2003).
Google Scholar 

120.
Huang, B. et al. Extended reconstructed sea surface temperature, version 5 (ERSSTv5): upgrades, validations, and intercomparisons. J. Clim. 30, 8179–8205 (2017).
Google Scholar 

121.
Hirahara, S., Ishii, M. & Fukuda, Y. Centennial-scale sea surface temperature analysis and its uncertainty. J. Clim. 27, 57–75 (2014).
Google Scholar 

122.
Laloyaux, P., Balmaseda, M., Dee, D., Mogensen, K. & Janssen, P. A coupled data assimilation system for climate reanalysis. Q. J. R. Meteorol. Soc. 142, 65–78 (2016).
Google Scholar 

123.
Giese, B. S., Seidel, H. F., Compo, G. P. & Sardeshmukh, P. D. An ensemble of ocean reanalyses for 1815–2013 with sparse observational input. J. Geophys. Res. Oceans 121, 6891–6910 (2016).
Google Scholar 

124.
Hobday, A. J. et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr. 141, 227–238 (2016).
Google Scholar 

125.
Griffin, C., Beggs H. & Majewski, L. GHRSST compliant AVHRR SST products over the Australian region – Version 1, Technical Report, 151 pp (Bureau of Meteorology, Melbourne, Australia, 2017).

126.
Wijffels, S. E. et al. A fine spatial-scale sea surface temperature atlas of the Australian regional seas (SSTAARS): Seasonal variability and trends around Australasia and New Zealand revisited. J. Mar. Syst. 187, 156–196 (2018).
Google Scholar 

127.
Oke, P. R. et al. Towards a dynamically balanced eddy-resolving ocean reanalysis: BRAN3. Ocean Model. 67, 52–70 (2013).
Google Scholar 

128.
Wessel, P. & Smith, W. H. F. A global self-consistent, hierarchical, high-resolution shoreline database. J. Geophys. Res. 101, 8741–8743 (1996).
Google Scholar 

129.
Perkins, S. E. & Alexander, L. V. On the measurement of heat waves. J. Clim. 26, 4500–4517 (2013).
Google Scholar 

130.
Pershing, A. J. et al. Challenges to natural and human communities from surprising ocean temperatures. Proc. Natl Acad. Sci. USA 116, 18378–18383 (2019).
Google Scholar  More

We hand-collected adult toads (n = 8 individuals per site) from four sites across the toads’ tropical range within Australia, from Townsville, Qld in the east (GPS coordinates: − 19.26, 146.82, 14 m altitude) to Richmond, Qld (− 20.73, 143.14, 218 m altitude), Middle Point, NT (− 12.56, 131.33, 12 m altitude) and Kununurra, WA (− 15.78, 128.74, 49 m altitude) in the west. That transect spans the toads’ 80-year invasion history. Although both temperatures and precipitation exhibit a general east–west cline, the greatest disparities in the duration of hot dry conditions per year lie between the easternmost site (Townsville) and the three other sites (Fig. 1). We recorded toad mass (after gently squeezing the animal in a standardized manner to induce it to empty its bladder) and snout-vent length (SVL) immediately before conducting the trials.
Figure 1

Data from Australian Bureau of Meteorology7.

Mean climatic conditions in the four sites from which we collected cane toads (Rhinella marina) for use in laboratory trials. The red line connects mean monthly maximum air temperatures, the green line shows mean monthly air temperatures, and the blue line shows mean monthly minimum air temperatures. Histograms show mean monthly rainfall.

Full size image

Toads were not fed for three days prior to experiments, to ensure they would not defecate during the experiment and minimize variability in mass due to stomach contents. Toads from all four populations were housed in a room kept at 18 °C, then moved concurrently to a temperature-controlled room set at 37 °C. All toads were in separate containers (ventilated plastic boxes of 1-L capacity), half of which had dry paper towel as substrate whereas the other half had 40 mL of water, enough to keep the ventral portion of the body moist but not the rest of the body.
We measured toad body temperatures at the beginning of the trial, and after 20 min and 40 min, using an infrared thermometer (Digitech QM7215) held ~ 10 cm from the toad’s dorsal surface. At the beginning and end of the experiment we measured internal temperatures with a cloacal probe (Digitech QM7215 with probe attachment), to check that our measurements of external body temperature offer robust estimates of internal temperature also. Cloacal temperatures were taken within 10 s of each toad’s removal from the container. After a trial, toads were kept at a temperature of 25 °C, allowed to fully hydrate and monitored for wellbeing during recovery. No adverse effects of the trials were evident.
We used mixed model repeated measures analysis to identify factors affecting body temperatures of cane toads during the 40-min heating trials. Sex and body mass were used as covariates in the analysis with climate at each collection site (# consecutive months per year with average maximum temperature  > 30 °C and with  More

1.
Myers, S. S. et al. Annu. Rev. Public Health 38, 259–277 (2017).
Article  Google Scholar 
2.
Zhao, C. et al. Proc. Natl Acad. Sci. USA 114, 9326–9331 (2017).
CAS  Article  Google Scholar 

3.
Sloat, L. L. et al. Nat. Commun. 11, 1243 (2020).
CAS  Article  Google Scholar 

4.
Janssens, C. et al. Nat. Clim. Change https://doi.org/10.1038/s41558-020-0847-4 (2020).

5.
Dellink, R., Hwang, H., Lanzi, E. & Chateau, J. International Trade Consequences of Climate Change (OECD Publishing, 2017).

6.
Climate Change and Trade Agreements: Friends or Foes? (The Economist Intelligence Unit, 2019).

7.
OECD. Regional Trade Agreements and Agriculture OECD Food, Agriculture and Fisheries Papers No. 79 (OECD Publishing, 2015).

8.
Foster, V. & Briceño-Garmendia, C. M. Africa’s Infrastructure: a Time for Transformation (The World Bank, 2009).

9.
Cagé, J. & Gadenne, L. Explor. Econ. Hist. 70, 1–24 (2018).
Article  Google Scholar 

10.
Hallegatte, S. & Rozenberg, J. Nat. Clim. Change 7, 250–256 (2017).
Article  Google Scholar  More

1.
Turner, M. G. Landscape ecology: the effect of pattern on process. Ann. Rev. Ecol. Syst. 20, 171–197 (1989).
Google Scholar 
2.
Wiens, J. A. Spatial scaling in ecology. Func. Ecol. 3, 385–397 (1989).
Google Scholar 

3.
Levin, S. A. The problem of pattern and scale in ecology. Ecology 73, 1943–1967 (1992).
Google Scholar 

4.
Dunning, J. B. Jr., Danielson, B. J. & Pulliam, H. R. Ecological processes that affect populations in complex landscapes. Oikos 65, 169–175 (1992).
Google Scholar 

5.
Boström, C., Pittman, S. J., Simenstad, C. & Kneib, R. T. Seascape ecology of coastal biogenic habitats: advances, gaps, and challenges. Mar. Ecol. Prog. Ser. 427, 191–217 (2011).
ADS  Google Scholar 

6.
Kremen, C., Williams, N. M. & Thorp, R. W. Crop pollination from native bees at risk from agricultural intensification. Proc. Natl. Acad. Sci. USA 99, 16812–16816 (2002).
ADS  CAS  PubMed  Google Scholar 

7.
Robinson, N. M. et al. Refuges for fauna in fire prone landscapes: their ecological function and importance. J. Appl. Ecol. 50, 1321–1329 (2013).
Google Scholar 

8.
Chapin, F. S. III. et al. Consequences of changing biodiversity. Nature 405, 234–242 (2000).
CAS  PubMed  Google Scholar 

9.
Michel, N., Burel, F. & Butet, A. How does landscape use influence small mammal diversity, abundance and biomass in hedgerow networks of farming landscapes?. Acta Oecol. 30, 11–20 (2006).
ADS  Google Scholar 

10.
Kirk, D. A., Lindsay, K. E. & Brook, R. W. Risk of agricultural practices and habitat change to farmland birds. Avi. Conserv. Ecol. 6(1), 5 (2011).
Google Scholar 

11.
Connell, S. D. & Glasby, T. M. Do urban structures influence local abundance and diversity of subtidal epibiota? A case study from Sydney Harbour, Australia. Mar. Environ. Res. 47, 373–387 (1999).
CAS  Google Scholar 

12.
Tilman, D. et al. Diversity and productivity in a long-term grassland experiment. Science 294, 843–845 (2001).
ADS  CAS  PubMed  Google Scholar 

13.
Tylianakis, J. M. et al. Resource heterogeneity moderates the biodiversity-function relationship in real world ecosystems. PLoS Biol. 6(5), e122. https://doi.org/10.1371/journal.pbio.0060122 (2008).
CAS  Article  PubMed Central  Google Scholar 

14.
O’Connor, R. J. & Shrubb, M. Farming and Birds (Cambridge University Press, Cambridge, 1986).
Google Scholar 

15.
Galbraith, H. Effects of agriculture on the breeding ecology of lapwings Vanellus vanellus. J. Appl. Ecol. 25, 487–503 (1988).
Google Scholar 

16.
Benton, T. G., Vickery, J. A. & Wilson, J. D. Farmland biodiversity: is habitat heterogeneity the key?. Trends Ecol. Evol. 18, 182–188 (2003).
Google Scholar 

17.
Lubchenco, J. et al. The sustainable biosphere initiative: an ecological research agenda. Ecology 72, 371–412 (1991).
Google Scholar 

18.
Andrén, H. Effects of habitat fragmentation on birds and mammals in landscapes with different proportions of suitable habitat: a review. Oikos 71, 355–366 (1994).
Google Scholar 

19.
McHugh, D. J. Worldwide distribution of commercial resources of seaweeds including Gelidium. Hydrobiologia 221, 19–29 (1991).
Google Scholar 

20.
Jensen, A. Present and future needs for algae and algal products. Hydrobiologia 260, 15–23 (1993).
Google Scholar 

21.
Ask, E. I., Batibasaga, A., Zertuche-Gonzalez, J. A. & de San, M. Three decades of Kappaphycus alvarezii (Rhodophyta) introduction to non-endemic locations. In 17th International Seaweed Symposium (eds Chapman, A. R. O. et al.) 49–57 (Oxford Univ Press, Cape Town, 2001).
Google Scholar 

22.
Rönnbäck, P., Bryceson, I. & Kautsky, N. Coastal aquaculture development in eastern Africa and the Western Indian Ocean: prospects and problems for food security and local economies. Ambio 31, 537–542 (2002).
PubMed  Google Scholar 

23.
Food and Agriculture Organization (FAO). The state of world fisheries and aquaculture. Rome, Italy pp. 243 (2014).

24.
Abhilash, K. R. et al. Impact of long-term seaweed farming on water quality: a case study from Palk Bay, India. J. Coast. Conserv. 23, 485–499 (2019).
Google Scholar 

25.
Eggertsen, M. & Halling, C. Knowledge gaps and management recommendations for future paths of sustainable seaweed farming in the Western Indian Ocean. Ambio https://doi.org/10.1007/s13280-020-01319-7 (2020).
Article  PubMed  Google Scholar 

26.
Hehre, E. J. & Meeuwig, J. J. A Global analysis of the relationship between farmed seaweed production and herbivorous fish catch. PLoS ONE 11(2), e148250. https://doi.org/10.1371/journal.pone.0148250 (2016).
CAS  Article  Google Scholar 

27.
Hedberg, N. et al. Habitat preference for seaweed farming—A case study from Zanzibar, Tanzania. Ocean Coast. Manag. 154, 186–195. https://doi.org/10.1016/j.ocecoaman.2018.01.016 (2018).
Article  Google Scholar 

28.
de la Torre-Castro, M. & Rönnbäck, P. Links between humans and seagrasses—an example from tropical east Africa. Ocean Coast. Manag. 47, 361–387 (2004).
Google Scholar 

29.
Halling, C., Wikström, S. A., Lilliesköld-Sjöö Mörk, E., Lundør, E. & Zuccarello, G. C. Introduction of Asian strains and low genetic variation in farmed seaweeds: indications for new management practices. J. Appl. Phycol. 25, 89–95 (2013).
Google Scholar 

30.
Tano, S. A., Halling, C., Eggertsen, L., Buriyo, A. & Wikström, S. A. Extensive spread of farmed seaweeds causes a shift from native to non-native haplotypes in natural seaweed beds. Mar. Biol. 162, 1983–1992 (2015).
Google Scholar 

31.
Conklin, E. J. & Smith, J. E. Abundance and spread of the invasive red algae, Kappaphycus spp., in Kane’ohe Bay, Hawai’i and an experimental assessment of management options. Biol. Invat. 7, 1029–1039 (2005).
Google Scholar 

32.
Keats, D. W., Steele, D. H. & South, G. R. The role of fleshy macroalgae in the ecology of juvenile cod (Gadus morhua L,) in inshore waters off eastern Newfoundland. Can. J. Fish. Aquat. Sci. 65, 49–53. https://doi.org/10.1139/Z87-008 (1987).
Article  Google Scholar 

33.
Carr, M. H. Effects of macroalgal dynamics on recruitment of a temperate reef fish. Ecol. Soc. Am. 75, 1320–1333 (1994).
Google Scholar 

34.
Levin, P. & Hay, M. Responses of temperate reef fishes to alterations in algal structure and species composition. Mar. Ecol. Prog. Ser. 134, 37–47 (1996).
ADS  Google Scholar 

35.
Bertocci, I., Araújo, R., Oliveira, P. & Sousa-Pinto, I. Potential effects of kelp species on local fisheries. J. Appl. Ecol. 52, 1216–1226 (2015).
Google Scholar 

36.
Wilson, S. K. et al. Seasonal changes in habitat structure underpin shifts in macroalgae-associated tropical fish communities. Mar. Biol. 161, 2597–2607 (2014).
Google Scholar 

37.
Tano, S. et al. Tropical seaweed beds are important habitats for mobile invertebrate epifauna. Estuar. Coast. Shelf Sci. 183, 1–12 (2016).
ADS  Google Scholar 

38.
Tano, S. A. et al. Tropical seaweed beds as important habitats for juvenile fish. Mar. Freshw. Res. 68, 1921–1934 (2017).
Google Scholar 

39.
Eggertsen, L. et al. Seaweed beds support more juvenile reef fish than seagrass beds: carrying capacity in a south-western Atlantic tropical seascape. Estuar. Coast. Shelf Sci. 196, 97–108. https://doi.org/10.1016/j.ecss.2017.06.041 (2017).
ADS  Article  Google Scholar 

40.
Fulton, C. J. et al. Form and function of tropical macroalgal reefs in the Anthropocene. Funct. Ecol. 33, 989–999 (2019).
Google Scholar 

41.
Garrigue, C. Macrophyte associations on the soft bottoms of the south-west lagoon of New Caledonia: description, structure and biomass. Bot. Mar. 38, 481–492 (1995).
Google Scholar 

42.
Kobryn, H. T., Wouters, K., Beckley, L. E. & Heege, T. Ningaloo Reef: shallow marine habitats mapped using a hyperspectral sensor. PLoS ONE 8, e70105 (2013).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

43.
Rossier, O. & Kulbicki, M. A comparison of fish assemblages from two types of algal beds and coral reefs in the south-west lagoon of New Caledonia. Cybium 24, 3–26 (2000).
Google Scholar 

44.
Chaves, L. T. C., Pereira, P. H. C. & Feitosa, J. L. L. Coral reef fish association with macroalgal beds on a tropical reef system in North-eastern Brazil. Mar. Freshw. Res. 64, 1101–1111 (2013).
Google Scholar 

45.
Evans, R. D., Wilson, S. K., Field, S. N. & Moore, J. A. Y. Importance of macroalgal fields as coral reef fish nursery habitat in north-west Australia. Mar. Biol. 161, 599–607 (2014).
Google Scholar 

46.
van Lier, J. R., Wilson, S. K., Depczynski, M., Wenger, L. N. & Fulton, C. J. Habitat connectivity and complexity underpin fish community structure across a seascape of tropical macroalgae meadows. Landsc. Ecol. 33, 1287–1300 (2018).
Google Scholar 

47.
Eggertsen, M., Chacin, D. H., Åkerlund, C., Halling, C. & Berkström, C. Contrasting distribution and foraging patterns of herbivorous and detritivorous fishes across multiple habitats in a tropical seascape. Mar. Biol. 166, 51. https://doi.org/10.1007/s00227-019-3498-0 (2019).
Article  Google Scholar 

48.
Johnstone, R. W. & Ólafsson, E. Some environmental aspects of open water algal cultivation, Zanzibar, Tanzania. Ambio 24, 465–469 (1995).
Google Scholar 

49.
Ólafsson, E., Johnstone, R. W. & Ndaro, S. G. M. Effects of intensive seaweed farming on the meiobenthos in a tropical lagoon. J. Exp. Mar. Biol. Ecol. 191, 101–117 (1995).
Google Scholar 

50.
Eklöf, J. S., de la Torre-Castro, M., Adelsköld, L., Jiddawi, N. S. & Kautsky, N. Differences in macrofaunal and seagrass assemblages in seagrass beds with and without seaweed farms. Estuar. Coast. Shelf Sci. 63, 385–396 (2005).
ADS  Google Scholar 

51.
Bergman, K. C., Svensson, S. & Öhman, M. C. Influence of algal farming on fish assemblages. Mar. Pollu. Bull. 42, 1379–1389 (2001).
CAS  Google Scholar 

52.
Russell, D. Ecology of the imported red seaweed Euchema striatum Schmitz on Coconut Island, Oahu, Hawaii. Pac. Sci. 37, 87–107 (1983).
Google Scholar 

53.
Eklöf, J. S., Henriksson, R. & Kautsky, N. Effects of tropical open-water seaweed farming on seagrass ecosystem structure and function. Mar. Ecol. Prog. Ser. 325, 73–84 (2006).
ADS  Google Scholar 

54.
Eklöf, J. S., de la Torre-Castro, M., Nilsson, C. & Rönnbäck, P. How do seaweed farms influence local fishery catches in a seagrass-dominated setting in Chwaka Bay, Zanzibar?. Aquat. Liv. Resour. 19, 137–147 (2006).
Google Scholar 

55.
Garpe, K. C. & Öhman, M. C. Coral and fish distribution patterns in Mafia Island Marine Park, Tanzania: fish−habitat interactions. Hydrobiologia 498, 191–211 (2003).
Google Scholar 

56.
McClanahan, T. R. Seasonality in East Africa’s coastal waters. Mar. Ecol. Prog. Ser. 44, 191–199 (1988).
ADS  Google Scholar 

57.
Msuya, F. E. Cultivation and utilisation of red seaweeds in the Western Indian Ocean (WIO) Region. J. Appl. Phycol. 26, 699–705 (2014).
CAS  Google Scholar 

58.
Msuya, F. E. The impact of seaweed farming on the social and economic structure of seaweed farming communities in Zanzibar, Tanzania. In World Seaweed Resources: An Authoritative Reference System (eds Critchley, A. T. et al.) (ETI BioInformatics, Amsterdam, 2006).
Google Scholar 

59.
Msuya, F. E. Social and economic dimensions of carrageenan seaweed farming in the United Republic of Tanzania. In Social and Economic Dimensions of Carrageenan Seaweed Farming Fisheries and Aquaculture Technical Paper No. 580 (eds Valderrama, D. et al.) 115–146 (FAO, Rome, 2013).
Google Scholar 

60.
Eklöf, J.S., Msuya, F.E., Lyimo, T.J. & Buriyo, A.S. Seaweed Farming in Chwaka Bay: A Sustainable Alternative in Aquaculture? – In: eds. de la Torre-Castro, M. and T. J. Lyimo, People, Nature and Research in Chwaka Bay, Zanzibar, Tanzania. ISBN: 978-9987-9559-1-6. Zanzibar Town: WIOMSA, 213–233 (2012).

61.
Valderrama, D. et al. The economics of Kappaphycus seaweed cultivation in developing countries: a comparative analysis of farming systems. Aquacul. Econ. Manag. 19, 251–277. https://doi.org/10.1080/13657305.2015.1024348 (2015).
Article  Google Scholar 

62.
Berkström, C., Jörgensen, T. L. & Hellström, M. Ecological connectivity and niche differentiation between two closely related fish species in the mangrove-seagrass-coral reef continuum. Mar. Ecol. Prog. Ser. 477, 01–215 (2013).
Google Scholar 

63.
Horrill, J. C., Darwall, W. R. T. & Ngoile, M. Development of a marine protected area: Mafia Island, Tanzania. Ambio 25, 50–57 (1996).
Google Scholar 

64.
Ogden, J. C. & Lobel, P. S. The role of herbivorous fishes and urchins in coral reef communities. Environ. Biol. Fish. 3, 49–63. https://doi.org/10.1007/BF00006308 (1978).
Article  Google Scholar 

65.
Lawrence, J. M. & Agatsuma, Y. Chapter 32: Tripneustes. In Sea urchins: Biology and Ecology (ed. Lawrence, J. M.) 491–507 (Elsevier BV, Amsterdam, 2013).
Google Scholar 

66.
Wall, K. R. & Stallings, C. D. Subtropical epibenthos varies with location, reef type, and grazing intensity. J. Exp. Mar. Biol. Ecol. 509, 54–65 (2018).
Google Scholar 

67.
Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Aust. Ecol. 26, 32–46 (2001).
Google Scholar 

68.
McArdle, B. H. & Anderson, M. J. Fitting multivariate models to community data: a comment on distance-based redundancy analysis. Ecology 82, 290–297 (2001).
Google Scholar 

69.
Dufrene, M. & Legendre, P. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol. Monog. 67, 345–366 (1997).
Google Scholar 

70.
Anderson, M. J. & Willis, T. J. Canonical analysis of principal coordinates: a useful method of constrained ordination for ecology. Ecology 84, 511–525 (2003).
Google Scholar 

71.
Legendre, P. & Legendre, L. Numerical Ecology. Vol 24 3rd Edition (2012).

72.
Anderson, M. J. Distance-based tests for homogeneity of multivariate dispersions. Biometrics 62, 245–253 (2006).
MathSciNet  PubMed  MATH  Google Scholar 

73.
Anderson, M. J., Ellingsen, K. E. & McArdle, B. H. Multivariate dispersion as a measure of beta diversity. Ecol. Lett. 9, 683–693 (2006).
PubMed  Google Scholar 

74.
Jones, D.L. Fathom Toolbox for Matlab: Software for Multivariate Ecological and Oceanographic Data Analysis. College of Marine Science, University of South Florida, St. Petersburg, FL, USA (2017) (Available from: https://www.marine.usf.edu/research/matlab-resources/fathom-toolbox-for-matlab/).

75.
Rojas-Sepulveda, J. Seaweeds, seagrasses, or both: feeding preferences of an important herbivore within a tropical seascape. Master Thesis. Stockholm University, Sweden (2017).

76.
Anyango, J. O., Mlewa, C. M. & Mwaluma, J. Abundance, diversity and trophic status of wild fish around seaweed farms in Kibuyuni, South Coast Kenya. Int. J. Fish. Aqua. Stud. 5, 440–446 (2017).
Google Scholar 

77.
Savino, J. F. & Stein, R. A. Predator–prey interaction between largemouth bass and bluegills as influenced by simulated submersed vegetation. Trans. Am. Fish. Soc. 111, 255–266 (1982).
Google Scholar 

78.
Anderson, T. W. Role of macroalgal structure in the distribution and abundance of a temperate reef fish. Mar. Ecol. Prog. Ser. 113, 279–290 (1994).
ADS  Google Scholar 

79.
Lim, I. E., Wilson, S. K., Holmes, T. H., Noble, M. M. & Fulton, C. Specialization within a shifting habitat mosaic underpins the seasonal abundance of a tropical fish. Ecosphere 7(2), e01212. https://doi.org/10.1002/ecs2.1212 (2016).
Article  Google Scholar 

80.
Wenger, L. N., van Lier, J. R. & Fulton, C. J. Microhabitat selectivity shapes the seascape ecology of a carnivorous macroalgae-associated tropical fish. Mar. Ecol. Prog. Ser. 590, 187–200 (2018).
ADS  Google Scholar 

81.
Tang, S., Graba-Landra, A. & Hoey, A. S. Density and height of Sargassum influence rabbit (F. siganidae) settlement on inshore reef flats of the Great Barrier reef. Coral Reefs 39, 467–473 (2020).
Google Scholar 

82.
Horinouchi, M. Review of the effects of within-patch scale structural complexity on seagrass fishes. J. Exp. Mar. Biol. Ecol. 350, 111–129 (2007).
Google Scholar 

83.
Chacin, D. H. & Stallings, C. D. Disentangling fine- and broad- scale effects of habitat on predator-prey interactions. J. Exp. Mar. Biol. Ecol. 483, 10–19 (2016).
Google Scholar 

84.
Orth, R. J., Heck, K. L. & Vanmontfrans, J. Faunal communities in seagrass beds: a review of the influence of plant structure and prey characteristics on predator prey relationships. Estuaries 7, 339–350 (1984).
Google Scholar 

85.
Heck, K. L. & Crowder, L. B. Habitat structure and predator–prey interactions in vegetated aquatic systems. In Habitat Complexity: The Physical Arrangement of Objects in Space (eds Bell, S. S. et al.) 280–299 (Chapman and Hall, New York, 1991).
Google Scholar 

86.
Johnson, D. W. Predation, habitat complexity, and variation in density-dependent mortality of temperate reef fishes. Ecology 87, 1179–1188 (2006).
PubMed  Google Scholar 

87.
Gregor, C. A. & Anderson, T. W. Relative importance of habitat attributes to predation risk in a temperate reef fish. Environ. Biol. Fish. 99, 539–556 (2016).
Google Scholar 

88.
Hardin, G. The competitive exclusion principle. Science 131, 1292–1297 (1960).
ADS  CAS  PubMed  Google Scholar 

89.
Hortal, J., Triantis, K. A., Meiri, S., Thebault, E. & Sfenthourakis, S. Island species richness increases with habitat diversity. Am. Nat. 174, 205–217 (2009).
Google Scholar 

90.
Genner, M. J., Turner, G. F. & Hawkins, S. J. Foraging of rocky habitat cichlid fishes in Lake Malawi: co-existence through niche partitioning?. Oecologia 121, 283–292 (1999).
ADS  PubMed  Google Scholar 

91.
Arrizabalaga-Escudero, A. et al. Assessing niche partitioning of co-occurring sibling bat species by DNA metabarcoding. Mol. Ecol. 27, 1273–1283 (2018).
PubMed  Google Scholar 

92.
Wilson, S. & Bellwood, D. R. Cryptic dietary components of territorial damselfishes (Pomacentridae, Labroidei). Mar. Ecol. Prog. Ser. 153, 299–310 (1997).
ADS  CAS  Google Scholar 

93.
Horn, M. H. Biology of marine herbivorous fishes. Oceanog. Mar. Biol. Ann. Rev. 27, 167–272 (1989).
Google Scholar 

94.
Arnold, G. W., Maller, R. A. & Litchfield, R. Comparison of bird populations in remnants of Wandoo woodland and in adjacent farmland. Aust. Wildl. Res. 14, 331–341. https://doi.org/10.1071/WR9870331 (1987).
Article  Google Scholar 

95.
Bretagnolle, V. et al. Towards sustainable and multifunctional agriculture in farmland landscapes: lessons from the integrative approach of a French LTSER platform. Sci. Total Environ. 627, 822–834 (2018).
ADS  CAS  PubMed  Google Scholar 

96.
Carcamo, H. A., Niemala, J. K. & Spence, J. R. Farming and ground beetles – effects of agronomic practice on populations and community structure. Can. Entomol. 127, 123–140 (1995).
Google Scholar 

97.
Locham, A. G., Kaunda-Arara, B., Wakibia, J. G. & Muya, S. Diet and niche breadth variation in the marbled parrotfish, Leptoscarus vaigiensis, among coral reef sites in Kenya. Afr. J. Ecol. 53, 560–571 (2015).
Google Scholar 

98.
Fox, R. J. & Bellwood, D. R. Remote video bioassays reveal the potential feeding impact of the rabbitfish Siganus canaliculatus (f:Siganidae) on an inner-shelf reef of the Great Barrier Reef. Coral Reefs 27, 605–615 (2008).
ADS  Google Scholar 

99.
Hoey, A. S. & Bellwood, D. R. Limited functional redundancy in a high diversity system: single species dominates key ecological process on coral reefs. Ecosystems 12, 1316–1328 (2009).
Google Scholar 

100.
Öhman, M. C. & Rajasuriya, A. Relationships between habitat structure and fish assemblages on coral and sandstone reefs. Environ. Biol. Fish. 53, 19–31 (1998).
Google Scholar 

101.
Gratwicke, B. & Speight, M. R. Effects of habitat complexity on Caribbean marine fish assemblages. Mar. Ecol. Prog. Ser. 292, 301–310 (2005).
ADS  Google Scholar 

102.
Humphries, P., Potter, I. C. & Loneragan, N. R. The fish community in the shallows of a temperate Australian estuary: relationships with the aquatic marcophyte Ruppia megacarpa and environmental variables. Estuar. Coast. Shelf Sci. 34, 32–346 (1992).
Google Scholar 

103.
Nelson, W. G. Development of an epiphyte indicator of nutrient enrichment: a critical evaluation of observational and experimental studies. Ecol. Indic. 79, 207–227 (2017).
PubMed  PubMed Central  Google Scholar 

104.
Gullström, M., Berkström, C., Öhman, M., Bodin, M. & Dahlberg, M. Scale-dependent patterns of variability of a grazing parrotfish (Leptoscarus vaigiensis) in a tropical seagrass-dominated seascape. Mar. Biol. 158, 1483–1495 (2011).
Google Scholar 

105.
Vonk, J. A., Marjolijin, J. A. & Stapel, J. Redefining the trophic importance of seagrasses for fauna in tropical Indo-Pacific meadows. Estuar. Coast. Shelf. Sci. 79, 653–660 (2008).
ADS  Google Scholar 

106.
Wilson, J. D., Morris, A. J., Arroyo, B. E., Clark, S. C. & Bradbury, R. B. A review of the abundance and diversity of invertebrate and plant foods of granivorous birds in northern Europe in relation to agricultural change. Agric. Eco. Envir. 75, 13–30 (1999).
Google Scholar 

107.
Hoey, A. S. & Bellwood, D. R. Cross-shelf variation in browsing intensity on the Great Barrier Reef. Coral Reefs 29, 499–508 (2010).
ADS  Google Scholar 

108.
Chong-Seng, K. M., Nash, K. L., Bellwood, D. R. & Graham, N. A. J. Macroalgal herbivory on recovering versus degrading coral reefs. Coral Reefs 33, 409–419 (2014).
ADS  Google Scholar 

109.
Hoey, A. S. & Bellwood, D. R. Suppression of herbivory by macroalgal density: a critical feedback on coral reefs. Ecol. Lett. 14, 267–273 (2011).
PubMed  Google Scholar 

110.
Bauman, A. G. et al. Fear effects associated with predator presence and habitat structure interact to alter herbivory on coral reefs. Biol. Lett. https://doi.org/10.1098/rsbl.2019.0409 (2019).
Article  PubMed  Google Scholar 

111.
Menge, B. A. Organization of the New England rocky intertidal community: role of predation, competition, and environmental heterogeneity. Ecol. Monog. 46, 355–393 (1976).
Google Scholar 

112.
Siriwardena, G. M. Trends in the abundance of farmland birds: a quantitative comparison of smoothed Common Birds Census indices. J. Appl. Ecol. 35, 24–43 (1998).
Google Scholar 

113.
Krebs, J. R., Wilson, J. D., Bradbury, R. B. & Siriwardena, G. M. The second Silent Spring. Nature 400, 611–612 (1999).
ADS  CAS  Google Scholar 

114.
Heikkinen, R. K., Luoto, M., Virkkala, R. & Rainio, K. Effects of habitat cover, landscape structure and spatial variables on the abundance of birds in an agricultural-forest mosaic. J. Appl. Ecol. 41, 824–835 (2004).
Google Scholar 

115.
Dauber, J. et al. Local vs. landscape controls on diversity: a test using surface-dwelling soil macroinvertebrates of differing mobility. Glob. Ecol. Biogeol. 14, 213–221 (2005).
Google Scholar 

116.
Hendrickx, F. et al. How landscape structure, land-use intensity and habitat diversity affect components of total arthropod diversity in agricultural landscapes. J. Appl. Ecol. 44, 340–351 (2007).
Google Scholar 

117.
Froehlich, H. E., Afflerbach, J. C., Frazier, M. & Halpern, B. S. Blue growth potential to mitigate climate change through seaweed offsetting. Curr. Biol. 18, 3087–3093. https://doi.org/10.1016/j.cub.2019.07.041 (2019).
CAS  Article  Google Scholar  More

1.
Redding, D. W., Moses, L. M., Cunningham, A. A., Wood, J. & Jones, K. E. Environmental-mechanistic modelling of the impact of global change on human zoonotic disease emergence: a case study of Lassa fever. Methods Ecol. Evol. 7, 646–655 (2016).
Google Scholar 
2.
McCormick, J. B. & Fisher-Hoch, S. P. in Arenaviruses I: The Epidemiology, Molecular and Cell Biology of Arenaviruses — Current Topics in Microbiology and Immunology Vol. 262 (ed. Oldstone, M. B. A.) 75–109 (Springer, 2002).

3.
Jonsson, C. B., Figueiredo, L. T. M. & Vapalahti, O. A global perspective on hantavirus ecology, epidemiology, and disease. Clin. Microbiol. Rev. 23, 412–441 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

4.
Edson, D. et al. Routes of Hendra virus excretion in naturally-infected flying-foxes: implications for viral transmission and spillover risk. PLoS ONE 10, e0140670 (2015).
PubMed  PubMed Central  Google Scholar 

5.
Luby, S. P., Gurley, E. S. & Jahangir Hossain, M. Transmission of human infection with Nipah virus. Clin. Infect. Dis. 49, 1743–1748 (2009).
PubMed  PubMed Central  Google Scholar 

6.
Georgiou, G. et al. Display of heterologous proteins on the surface of microorganisms: from the screening of combinatorial libraries to live recombinant vaccines. Nat. Biotechnol. 15, 29–34 (1997).
CAS  PubMed  Google Scholar 

7.
Leitner, W. W., Ying, H. & Restifo, N. P. DNA and RNA-based vaccines: principles, progress and prospects. Vaccine 18, 765–777 (1999).
CAS  PubMed  PubMed Central  Google Scholar 

8.
Pardi, N., Hogan, M. J., Porter, F. W. & Weissman, D. mRNA vaccines — a new era in vaccinology. Nat. Rev. Drug Discov. 17, 261–279 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

9.
Rollier, C. S., Reyes-Sandoval, A., Cottingham, M. G., Ewer, K. & Hill, A. V. S. Viral vectors as vaccine platforms: deployment in sight. Curr. Opin. Immunol. 23, 377–382 (2011).
CAS  PubMed  Google Scholar 

10.
Ferretti, L. et al. Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science 368, eabb6936 (2020).
CAS  PubMed  PubMed Central  Google Scholar 

11.
Morse, S. S. et al. Prediction and prevention of the next pandemic zoonosis. Lancet 380, 1956–1965 (2012).
PubMed  PubMed Central  Google Scholar 

12.
Rupprecht, C. E., Hanlon, C. A. & Slate, D. in Control of Infectious Animal Diseases by Vaccination — Developments in Biologicals Vol. 119 (eds Schudel, A. & Lombard, M.) 173–184 (Karger, 2004).

13.
Bull, J. J., Smithson, M. W. & Nuismer, S. L. Transmissible viral vaccines. Trends Microbiol. 26, 6–15 (2018).
CAS  PubMed  Google Scholar 

14.
Murphy, A. A., Redwood, A. J. & Jarvis, M. A. Self-disseminating vaccines for emerging infectious diseases. Expert Rev. Vaccines 15, 31–39 (2016).
CAS  PubMed  Google Scholar 

15.
Shellam, G. R. The potential of murine cytomegalovirus as a viral vector for immunocontraception. Reprod. Fertil. Dev. 6, 401–409 (1994).
CAS  PubMed  Google Scholar 

16.
Tyndale-Biscoe, C. H. Virus-vectored immunocontraception of feral mammals. Reprod. Fertil. Dev. 6, 281–287 (1994).
CAS  PubMed  Google Scholar 

17.
Barcena, J. et al. Horizontal transmissible protection against myxomatosis and rabbit hemorrhagic disease by using a recombinant myxoma virus. J. Virol. 74, 1114–1123 (2000).
CAS  PubMed  PubMed Central  Google Scholar 

18.
Torres, J. M. et al. First field trial of a transmissible recombinant vaccine against myxomatosis and rabbit hemorrhagic disease. Vaccine 19, 4536–4543 (2001).
CAS  PubMed  Google Scholar 

19.
Angulo, E. & Barcena, J. Towards a unique and transmissible vaccine against myxomatosis and rabbit haemorrhagic disease for rabbit populations. Wildl. Res. 34, 567–577 (2007).
CAS  Google Scholar 

20.
Nuismer, S. L. et al. Eradicating infectious disease using weakly transmissible vaccines. Proc. R. Soc. B 283, 20161903 (2016).
PubMed  Google Scholar 

21.
Basinski, A. J., Nuismer, S. L. & Remien, C. H. A little goes a long way: weak vaccine transmission facilitates oral vaccination campaigns against zoonotic pathogens. PLoS Negl. Trop. Dis. 13, e0007251 (2019).
PubMed  PubMed Central  Google Scholar 

22.
Basinski, A. J. et al. Evaluating the promise of recombinant transmissible vaccines. Vaccine 36, 675–682 (2018).
CAS  PubMed  Google Scholar 

23.
Smithson, M. W., Basinki, A. J., Nuismer, S. L. & Bull, J. J. Transmissible vaccines whose dissemination rates vary through time, with applications to wildlife. Vaccine 37, 1153–1159 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

24.
Lecompte, E. et al. Mastomys natalensis and Lassa fever, West Africa. Emerg. Infect. Dis. 12, 1971–1974 (2006).
PubMed  PubMed Central  Google Scholar 

25.
Olayemi, A. et al. New hosts of the Lassa virus. Sci. Rep. 6, 25280 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

26.
Douglass, R. J. et al. Longitudinal studies of Sin Nombre virus in deer mouse-dominated ecosystems of Montana. Am. J. Trop. Med. Hyg. 65, 33–41 (2001).
CAS  PubMed  Google Scholar 

27.
Luis, A. D., Douglass, R. J., Mills, J. N. & Bjornstad, O. N. The effect of seasonality, density and climate on the population dynamics of Montana deer mice, important reservoir hosts for Sin Nombre hantavirus. J. Anim. Ecol. 79, 462–470 (2010).
PubMed  Google Scholar 

28.
Viana, M. et al. Assembling evidence for identifying reservoirs of infection. Trends Ecol. Evol. 29, 270–279 (2014).
PubMed  PubMed Central  Google Scholar 

29.
Fenton, A., Streicker, D. G., Petchey, O. L. & Pedersen, A. B. Are all hosts created equal? Partitioning host species contributions to parasite persistence in multihost communities. Am. Nat. 186, 610–622 (2015).
PubMed  PubMed Central  Google Scholar 

30.
Fichet-Calvet, E. et al. Fluctuation of abundance and Lassa virus prevalence in Mastomys natalensis in Guinea, West Africa. Vector-Borne Zoonotic Dis. 7, 119–128 (2007).
PubMed  Google Scholar 

31.
Marien, J. et al. Evaluation of rodent control to fight Lassa fever based on field data and mathematical modelling. Emerg. Microbes Infect. 8, 640–649 (2019).
PubMed  PubMed Central  Google Scholar 

32.
Towner, J. S. et al. Marburg virus infection detected in a common african bat. PLoS ONE 2, e764 (2007).
PubMed  PubMed Central  Google Scholar 

33.
Nziza, J. et al. Coronaviruses detected in bats in close contact with humans in Rwanda. EcoHealth 17, 152–159 (2020).
PubMed  Google Scholar 

34.
Anthony, S. J. et al. Further evidence for bats as the evolutionary source of Middle East respiratory syndrome coronavirus. Mbio 8, e00373–17 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

35.
Ge, X.-Y. et al. Isolation and characterization of a bat SARS-like coronavirus that uses the ACE2 receptor. Nature 503, 535–538 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

36.
Bird, B. H. & Mazet, J. A. K. Detection of emerging zoonotic pathogens: an integrated one health approach. Annu. Rev. Anim. Biosci. 6, 121–139 (2018).
CAS  PubMed  Google Scholar 

37.
Goldstein, T. et al. The discovery of Bombali virus adds further support for bats as hosts of ebolaviruses. Nat. Microbiol. 3, 1084–1089 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

38.
Pernet, O. et al. Evidence for henipavirus spillover into human populations in Africa. Nat. Commun. 5, 5342 (2014).
PubMed  PubMed Central  Google Scholar 

39.
Grard, G. et al. A novel rhabdovirus associated with acute hemorrhagic fever in Central Africa. PLoS Pathog. 8, e1002924 (2012).
PubMed  PubMed Central  Google Scholar 

40.
Han, B. A. & Drake, J. M. Future directions in analytics for infectious disease intelligence. EMBO Rep. 17, 785–789 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

41.
Han, B. A., Schmidt, J. P., Bowden, S. E. & Drake, J. M. Rodent reservoirs of future zoonotic diseases. Proc. Natl Acad. Sci. USA 112, 7039–7044 (2015).
CAS  PubMed  Google Scholar 

42.
Han, B. A. et al. Undiscovered bat hosts of filoviruses. PLoS Negl. Trop. Dis. 10, e0004815 (2016).
PubMed  PubMed Central  Google Scholar 

43.
Guth, S., Visher, E., Boots, M. & Brook, C. E. Host phylogenetic distance drives trends in virus virulence and transmissibility across the animal-human interface. Philos. Trans. R. Soc. B 374, 20190296 (2019).
Google Scholar 

44.
Olival, K. J. et al. Host and viral traits predict zoonotic spillover from mammals. Nature 546, 646–650 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

45.
Pepin, K. M., Lass, S., Pulliam, J. R. C., Read, A. F. & Lloyd-Smith, J. O. Identifying genetic markers of adaptation for surveillance of viral host jumps. Nat. Rev. Microbiol. 8, 802–813 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

46.
Babayan, S. A., Orton, R. J. & Streicker, D. G. Predicting reservoir hosts and arthropod vectors from evolutionary signatures in RNA virus genomes. Science 362, 577–580 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

47.
Bakker, K. M. et al. Fluorescent biomarkers demonstrate prospects for spreadable vaccines to control disease transmission in wild bats. Nat. Ecol. Evol. 3, 1697–1704 (2019).
PubMed  PubMed Central  Google Scholar 

48.
Garnier, R., Gandon, S., Chaval, Y., Charbonnel, N. & Boulinier, T. Evidence of cross-transfer of maternal antibodies through allosuckling in a mammal: potential importance for behavioral ecology. Mamm. Biol. 78, 361–364 (2013).
Google Scholar 

49.
Stading, B. et al. Protection of bats (Eptesicus fuscus) against rabies following topical or oronasal exposure to a recombinant raccoon poxvirus vaccine. PLoS Negl. Trop. Dis. 11, e0005958 (2017).
PubMed  PubMed Central  Google Scholar 

50.
Schreiner, C. L., Nuismer, S. L. & Basinski, A. J. When to vaccinate a fluctuating wildlife population: is timing everything? J. Appl. Ecol. 57, 307–319 (2020).
PubMed  Google Scholar 

51.
Varrelman, T. J., Basinski, A. J., Remien, C. H. & Nuismer, S. L. Transmissible vaccines in heterogeneous populations: implications for vaccine design. One Health 7, 100084 (2019).
PubMed  PubMed Central  Google Scholar 

52.
Alizon, S., Hurford, A., Mideo, N. & Van Baalen, M. Virulence evolution and the trade-off hypothesis: history, current state of affairs and the future. J. Evol. Biol. 22, 245–259 (2009).
CAS  PubMed  Google Scholar 

53.
Kew, O. M., Sutter, R. W., de Gourville, E. M., Dowdle, W. R. & Pallansch, M. A. Vaccine-derived polioviruses and the endgame strategy for global polio eradication. Annu. Rev. Microbiol. 59, 587–635 (2005).
CAS  PubMed  Google Scholar 

54.
Bull, J. J. Evolutionary reversion of live viral vaccines: can genetic engineering subdue it? Virus Evol. 1, vev005 (2015).
PubMed  PubMed Central  Google Scholar 

55.
Lauring, A. S., Jones, J. O. & Andino, R. Rationalizing the development of live attenuated virus vaccines. Nat. Biotechnol. 28, 573–579 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

56.
Nuismer, S. L., Basinski, A. & Bull, J. J. Evolution and containment of transmissible recombinant vector vaccines. Evol. Appl. 12, 1595–1609 (2019).
PubMed  PubMed Central  Google Scholar 

57.
Kew, O. M. et al. Circulating vaccine-derived polioviruses: current state of knowledge. Bull. World Health Organ. 82, 16–23 (2004).
PubMed  PubMed Central  Google Scholar 

58.
Hampson, K. et al. Estimating the global burden of endemic canine rabies. PLoS Negl. Trop. Dis. 9, e0003709 (2015).
PubMed  PubMed Central  Google Scholar 

59.
Cost of the Ebola Epidemic (US Centers for Disease Control and Prevention, 2020); https://go.nature.com/38iF7cg

60.
Forum on Microbial Threats Learning from SARS: Preparing for the Next Disease Outbreak: Workshop Summary (National Academies Press, 2004). More

1.
IPCC: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [eds. Pachauri, R.K & L.A. Meyer) (IPCC, 2014).
2.
AMAP (Arctic Monitoring and Assessment Programme). Snow, Water (Ice and Permafrost in the Arctic, AMAP, Tromsø, 2017).
Google Scholar 

3.
Wik, M., Varner, R. K., Walter Anthony, K., MacIntyre, S. & Bastviken, D. Climate-sensitive northern lakes and ponds are critical components of methane release. Nat. Geosci. 9, 99–105 (2016).
ADS  CAS  Article  Google Scholar 

4.
Holgerson, M. A. & Raymond, P. A. Large contribution to inland water CO2 and CH4 emissions from very small ponds. Nat. Geosci. 9, 222–226 (2016).
ADS  CAS  Article  Google Scholar 

5.
Bastviken, D., Cole, J., Pace, M. & Tranvik, L. Methane emissions from lakes: Dependence of lake characteristics, two regional assessments, and a global estimate. Global Biogeochem. Cyc. 18, 1–12 (2004).
Article  Google Scholar 

6.
Bastviken, D., Tranvik, L. J., Downing, J. A., Crill, P. M. & Enrich-Prast, A. Freshwater methane emissions offset the continental carbon sink. Science 331, 50–51 (2011).
ADS  CAS  Article  Google Scholar 

7.
Walter, K. M., Smith, L. C. & Chapin, F. S. Methane bubbling from northern lakes: present and future contributions to the global methane budget. Philos. Trans. R. Soc. A 365, 1657–1676 (2007).
ADS  CAS  Article  Google Scholar 

8.
Walter Anthony, K. M. & Anthony, P. Constraining spatial variability of methane ebullition seeps in thermokarst lakes using point process models. J. Geophys. Res. Biogeosci. 118, 1015–1034. https://doi.org/10.1002/jgrg.20087 (2013).
Article  Google Scholar 

9.
Tan, Z. & Zhuang, Q. Arctic lakes are continuous methane sources to the atmosphere under warming conditions. Environ. Res. Lett. 10, 1–9 (2015).
Article  Google Scholar 

10.
Tan, Z. & Zhuang, Q. Methane emissions from pan-Arctic lakes during the 21st century: An analysis with process-based models of lake evolution and biogeochemistry. J. Geophys. Res. Biogeosci. 120, 2641 (2015).
CAS  Article  Google Scholar 

11.
Lehner, B. & Doell, P. Development and validation of a global database of lakes, reservoirs and wetlands. J. Hydrol. 296, 1–22 (2004).
ADS  Article  Google Scholar 

12.
Verpoorter, C., Kutser, T., Seekell, D. A. & Tranvik, L. J. A global inventory of lakes based on high-resolution satellite imagery. Geophys. Res. Lett. 41, 6396–6402 (2014).
ADS  Article  Google Scholar 

13.
Messager, M. L., Lehner, B., Grill, G., Nedeva, I. & Schmitt, O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. Commun. 7, 13603 (2016).
ADS  CAS  Article  Google Scholar 

14.
Downing, J. A. & Duarte, C. M. Abundance and size distribution of lakes, ponds, and impoundments. In Encyclopedia of Inland Water, 1 (ed. Likens, G. E.) 469–478 (Elsevier, Amsterdam, 2009).
Google Scholar 

15.
McGinnis, D. F., Greinert, J., Artemov, Y., Beaubien, S. E. & Wüest, A. Fate of rising methane bubbles in stratified waters: how much methane reaches the atmosphere?. J. Geophys. Res. 111, C09007 (2006).
ADS  Article  Google Scholar 

16.
Lamarche, C. et al. Compilation and validation of SAR and optical data products for a complete and global map of inland/ocean water tailored to the climate modeling community. Rem. Sens. 9, 36 (2017).
ADS  Article  Google Scholar 

17.
Pekel, J.-F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).
ADS  CAS  Article  Google Scholar 

18.
Sanches, L. F., Guenet, B., Marinho, C. C., Barros, N. & de Assis Esteves, F. Global regulation of methane emission from natural lakes. Sci. Rep. 9, 255 (2019).
ADS  Article  Google Scholar 

19.
Bruhwiler, L. et al. CarbonTracker-CH4: an assimilation system for estimating emissions of atmospheric methane. Atmos. Chem. Phys. 14, 8269–8293 (2014).
ADS  Article  Google Scholar 

20.
Du, J., Kimball, J. S., Duguay, C., Kim, Y. & Watts, J. D. Satellite microwave assessment of Northern Hemisphere lake ice phenology from 2002 to 2015. Cryosphere 11, 47–63 (2017).
ADS  Article  Google Scholar 

21.
Kim, Y., Kimball, J. S., McDonald, K. C. & Glassy, J. Developing a global data record of daily landscape freeze/thaw status using satellite microwave remote sensing, Version 4. IEEE Trans. Geosci. Rem. Sens. 49, 949–960 (2016).
ADS  Article  Google Scholar 

22.
Brown, J., Ferrians, O.J., Heginbottom, J.A. & Melnikov, E.S. Circum-Arctic map of permafrost and ground-ice conditions, Version 2. Boulder, CO, National Snow and Ice Data Center/World Data Center for Glaciology. https://doi.org/10.3133/cp45 (2002).

23.
Obu, J. et al. Northern hemisphere permafrost map based on TTOP modelling for 2000–2016 at 1 km2 scale. Earth Sci. Rev. 193, 299–316 (2019).
ADS  Article  Google Scholar 

24.
Harmonized World Soil Database (HWSD) https://daac.ornl.gov/SOILS/guides/HWSD.html.

25.
Allen, G. H. & Pavelsky, T. M. Global extent of rivers and streams. Science 361, 585–588 (2018).
MathSciNet  CAS  Article  Google Scholar 

26.
Lehner, B. et al. High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management. Front. Ecol. Environ. 9(9), 494–502 (2011).
Article  Google Scholar 

27.
Mulligan, M., Saenz-Cruz, L., van Soesbergen, A., Smith, V.T. & Zurita, L. The Global georeferenced Database of Dams (GOOD2), Version 1. Global dams database and geowiki. https://geodata.policysupport.org/dams (2009).

28.
Chau, Y. K., Snodgrass, W. J. & Wong, P. T. S. A sampler for collecting evolved gases from sediment. Water Res. 11, 807–809 (1977).
CAS  Article  Google Scholar 

29.
Howard, D.L., Frea, J.I. & Pfister, R.M. The potential for methane carbon cycling in Lake Erie. In Paper Presented at 14th Conference on Great Lakes Research (Int. Assoc. of Great Lakes Res., Ann Arbor, Mich. 1971).

30.
Townsend-Small, A. et al. Quantifying emissions of methane derived from anaerobic organic matter respiration and natural gas extraction in Lake Erie. Limnol. Oceanogr. 61, S356–S366 (2016).
CAS  Article  Google Scholar 

31.
Joung, D., Leonte, M. & Kessler, J. D. Methane sources in the waters of Lake Michigan and Lake Superior as revealed by natural radiocarbon measurements. Geophys. Res. Lett. 46, 5436–5444 (2019).
ADS  CAS  Article  Google Scholar 

32.
Shimoda, Y. et al. Our current understanding of lake ecosystem response to climate change: what have we really learned from the north temperate deep lakes?. J. Great Lakes Res. 37, 173–193 (2011).
CAS  Article  Google Scholar 

33.
Blenckner, T. R. et al. Large-scale climatic signatures in lakes across Europe: a meta-analysis. Glob. Change Biol. 13, 1314–1326 (2007).
ADS  Article  Google Scholar 

34.
van Huissteden, J. et al. Methane emissions from permafrost thaw lakes limited by lake drainage. Nat. Clim. Change 1, 119–123 (2011).
ADS  Article  Google Scholar 

35.
Kalff, J. Limnology, Inland Water Ecosystems (Prentice Hall, Upper Saddle River, 2002).
Google Scholar 

36.
Kourzeneva, E., Asensio, H., Martin, E. & Faroux, S. Global gridded dataset of lake coverage and lake depth for use in numerical weather prediction and climate modelling. Tellus A 64, 1–14 (2012).
Google Scholar  More

Experimental design
Following five focus groups with SERNAPESCA’s head of enforcement and other personnel, we designed and implemented an online survey that targeted fisheries enforcement officers who are responsible for monitoring IUU activities in Chile. The survey was structured to capture expert knowledge on various aspects of illegal activities, as well as the relative experience of the officers. The survey defined illegal fishing as a fishing activity carried out in national jurisdiction waters by national or international boats that is in violation of the national fishing law, conducted without a legal permit, or activities that involve unreported or misreported captures to the authorities. The Director of SERNAPESCA delivered the survey via email to all SERNAPESCA enforcement officers. The list of officers was constructed by the Director (n = 86). The survey was anonymous in that the officers were not asked to report their name nor any information that could be used for identification (e.g., email). Answers to questions were not mandatory; that is, respondents could opt-out of answering particular questions and continue with the survey. The survey was available online for ten weeks, over which five reminder emails were sent to officers requesting them to complete the survey.
The survey, in Spanish, consisted of two sections. First, we asked respondents to rank the magnitude of illegal activity for twenty fisheries on a nominal scale (1–5), along with their relative experience with each fishery (nominal scale, 1–5). The twenty fisheries were selected a priori based on our focus groups and known information about illegal activity. All fisheries were single species, with the exception of four that included multiple species: skates (2 species, Zearaja chilensis and Bathyraja macloviana), kelp (4 species: Lessonia spicate, L. berteroana, L. traberculata, Macrocystis pyrifera), red algae (3 species: Sarcothalia crispate, Gigartina skottsbergii, Mazzaella laminarioides), and crabs (10 species excluding southern king crab: Cancer edwardsi, C. porter, C. setosus, C. coronatus, Homalaspis plana, Ovalipes trimaculatus, Taliepus dentatus, T. marginatus, Mursia gaudichaudi, Hemigrapsus crenulatus). In the second part of the survey, we asked respondents additional questions for four focal fisheries: South Pacific hake (Merluccius gayi gayi), southern hake (M. australis), loco or Chilean abalone (Concholepas concholepas), and kelp. For each fishery, we asked respondents to score on a nominal scale (1–5),

The frequency of six specific illegal activities in the industrial sector: size, gear, season, area, transshipment, and port.

The frequency of six specific illegal activities in the small-scale sector: size, gear, season, area, transshipment, and port.

The participation of illegal activity for six different stakeholders along the supply chain: fisher, purchaser, processor, wholesaler, exporter, and restaurateur.

The utilization of seven infrastructure types in illegal activities: fishing boats, refrigeration trucks, processing plants, markets, transshipment boats, export vehicles, and restaurants.

This study was approved by the Advanced Conservation Strategies and Pontificia Universidad Católica ethics institutional review boards and followed guidelines established by their ethics committees, which complies with national and international standards. The surveys included a written informed consent approved by all interviewees, which acknowledged research objectives and established that the survey was anonymous and that interviewees were free to choose to not answer questions. While all species have common names in Chile (which were used in the survey), we use Fishbase and Sealifebase as the taxonomic authority and for the common names reported here to facilitate comparisions34,35.
Statistical analysis
For both sections of the survey, we used a Bayesian cumulative multinomial logit model to predict illegal estimates. First, we fitted a model for illegal estimates for each of the twenty fisheries jointly. Second, we fitted models for illegal estimates for various aspects of the four focal fisheries (i.e., activities, stakeholders, and infrastructure) in a single analysis for each aspect. In both models, we included a random intercept term for respondent, along with a fixed effect for fishery. We evaluated the role of experience, as self-reported by the respondents, by comparing the difference between the illegal score by a respondent for a fishery and the model prediction for that fishery across respondents. If higher levels of expertise increased or decreased the value of a respondent’s scoring, there would be a relationship between the size of the differences and the level of experience reported for a fishery. Experience may also affect the difference in mean responses (i.e., bias), potentially due to more personal experience over a longer period of time, which would lead to a correlation between expertise and mean illegality scores. Depending on the patterns observed in the data, there are several ways to control for a respondent’s experience in illegality estimates. In our case, we used experience scores as a covariate in the model.
For the twenty fisheries, we used the following model,

$$Prleft{{S}_{ij}=kright}=phi left({tau }_{k}-left({varvec{beta}}{{varvec{x}}}_{{varvec{i}}}+{{varvec{z}}}_{{varvec{j}}}{{varvec{V}}}_{{varvec{i}}}right)right)-phi left({tau }_{k-1}-left({varvec{beta}}{{varvec{x}}}_{{varvec{i}}}+{{varvec{z}}}_{{varvec{j}}}{{varvec{V}}}_{{varvec{j}}}right)right)$$
(1)

in which the probability that the score for the level of illegal landings ({S}_{ij}) for the ith species by the jth respondent is equal to category k, can be represented as a latent continuous variable which is divided into K categories, by K − 1 thresholds at ({tau }_{k}). This latent continuous variable is represented by the cumulative normal distribution, (phi). For a given observation, the regression equation is composed of coefficients multiplied times predictor variables ({varvec{beta}}{{varvec{x}}}_{{varvec{i}}}) plus a design matrix for the random effect, multiplied times the error term for the jth respondent, ({{varvec{z}}}_{{varvec{j}}}{{varvec{V}}}_{{varvec{i}}}) . The probability of that observation falling in category k, (Prleft{{S}_{ij}=kright}), is thus the probability of it being in a category equal to or smaller than k, (phi left({tau }_{k}-left({varvec{beta}}{{varvec{x}}}_{{varvec{i}}}+{{varvec{z}}}_{{varvec{j}}}{{varvec{V}}}_{{varvec{i}}}right)right)), less the probability of the observation being in a category smaller than k, (phi left({tau }_{k-1}-left({varvec{beta}}{{varvec{x}}}_{{varvec{i}}}+{{varvec{z}}}_{{varvec{j}}}{{varvec{V}}}_{{varvec{j}}}right)right)). Implemented in the R statistical language, using the brms package36, the call to fit this model looks like the following:

$${text{Score}}; , sim ;{text{Species}} + {text{Experience }} + left( {{1}|{text{Respondent}}} right),;{text{ data}} = {text{SurveyData}},;{text{family}} = {text{cumulative}}),$$

where Score is ({S}_{ij}) in (1) above, the fixed effects, ({varvec{beta}}{{varvec{x}}}_{{varvec{i}}}) are the experience of the respondent and the species that was scored, and (1|Respondent) denotes a random intercept model, where each has a different intercept term, drawn from a shared error distribution. For more information on the application of this model to ordinal response data, see Burkner and Vuorre37.
For the estimates for the various aspects of the four focal fisheries, we used the following model,

$${text{Response}}; sim ;{text{Species}} + {text{Experience}} + left( {{1}|{text{Respondent}}} right),;{text{data}} = {text{SurveyData}},;{text{family}} = {text{cumulative}}),$$

which is structured as per (1) above, but with the responses to the various focal species questions (i.e., activities per sector, stakeholders, and infrastructure) substituted for the species scores as in (1).
We compared both models with simpler models, including a single-term null model using leave-one-out cross-validation. We did so in the R statistical language using the loo packages36,38,39. Prior distributions for all regression terms were improper flat priors over the real numbers, the default in the brms package for population parameters. The priors on the intercept and the random effects were student t3,0,10 distributions, as per the default for uninformative priors in the brms package.
We carried out a Principal Components Analysis (PCA) with the four focal fisheries as categorical variables and the illegal activity, stakeholder, and infrastructure estimates from the Bayesian cumulative multinomial logit model. For each fishery, we used 10,000 estimates from the model, along with a qualitative variable that represented the different factors (e.g., restaurateur). The latter has no influence on the principal components of the analysis but helps to interpret the dimensions of variability. Principal Components Analysis is especially powerful as an approach to visualize patterns, such as clusters, clines, and outliers in a dataset40. In our case, we sought to visualize whether there were common illegal factors with similar set of scores and whether there was any association between high or low scores of illegal factors and the focal fisheries. We used the FactoMineR package in the R statistical language41. More

1.
Bowe, L. M., Coat, G. & DePamphilis, C. W. Phylogeny of seed plants based on all three genomic compartments: Extant gymnosperms are monophyletic and Gnetales’ closest relatives are conifers. Proc. Natl. Acad. Sci. USA 97, 4092–4097 (2000).
ADS  Article  CAS  Google Scholar 
2.
Bohlmann, J. & Keeling, C. I. Terpenoid biomaterials. Plant J. 54, 656–669 (2008).
Article  CAS  Google Scholar 

3.
Celedon, J. M. & Bohlmann, J. Oleoresin defenses in conifers: Chemical diversity, terpene synthases, and limitations of oleoresin defense under climate change. New Phytol. 224, 1444–1463 (2019).
Article  CAS  Google Scholar 

4.
Whitehill, J. G. A. et al. Functions of stone cells and oleoresin terpenes in the conifer defense syndrome. New Phytol. 221, 1503–1517 (2019).
Article  CAS  Google Scholar 

5.
Hilker, M., Kobs, C., Varama, M. & Schrank, K. Insect egg deposition induces Pinus sylvestris to attract egg parasitoids. J. Exp. Biol. 205, 455–461 (2002).
PubMed  Google Scholar 

6.
Mumm, R., Schrank, K., Wegener, R., Schulz, S. & Hilker, M. Chemical analysis of volatiles emitted by Pinus sylvestris after induction by insect oviposition. J. Chem. Ecol. 29, 1235–1252 (2003).
Article  CAS  Google Scholar 

7.
Martin, D. M., Gershenzon, J. & Bohlmann, J. Induction of volatile terpene biosynthesis and diurnal emission by methyl jasmonate in foliage of Norway spruce. Plant Physiol. 132, 1586–1599 (2003).
Article  CAS  Google Scholar 

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

9.
Niinemets, U., Reichstein, M., Staudt, M., Seufert, G. & Tenhunen, J. D. Stomatal constraints may affect emission of oxygenated monoterpenoids from the foliage of Pinus pinea. Plant Physiol. 130, 1371–1385 (2002).
Article  CAS  Google Scholar 

10.
Harley, P., Eller, A., Guenther, A. & Monson, R. K. Observations and models of emissions of volatile terpenoid compounds from needles of ponderosa pine trees growing in situ: Control by light, temperature and stomatal conductance. Oecologia 176, 35–55 (2014).
ADS  Article  Google Scholar 

11.
Tissier, A. Plant secretory structures: More than just reaction bags. Curr. Opin. Biotechnol. 49, 73–79 (2018).
Article  CAS  Google Scholar 

12.
Schilmiller, A. L., Last, R. L. & Pichersky, E. Harnessing plant trichome biochemistry for the production of useful compounds. Plant J. 54, 702–711 (2008).
Article  CAS  Google Scholar 

13.
Wagner, G. J., Wang, E. & Shepherd, R. W. New approaches for studying and exploiting an old protuberance, the plant trichome. Ann. Bot. 93, 3–11 (2004).
Article  CAS  Google Scholar 

14.
Lange, B. M. The evolution of plant secretory structures and the emergence of terpenoid chemical diversity. Annu. Rev. Plant Biol. 66, 139–159 (2015).
Article  CAS  Google Scholar 

15.
Lange, B. M. et al. Probing essential oil biosynthesis and secretion by functional evaluation of expressed sequence tags from mint glandular trichomes. Proc. Natl. Acad. Sci. USA 97, 2934–2939 (2000).
ADS  Article  CAS  Google Scholar 

16.
Wang, G. et al. Terpene biosynthesis in glandular trichomes of Hop. Plant Physiol. 148, 1254–1266 (2008).
ADS  Article  CAS  Google Scholar 

17.
Nagel, J. et al. EST analysis of hop glandular trichomes identifies an o-methyltransferase that catalyzes the biosynthesis of xanthohumol. Plant Cell 20, 186–200 (2008).
Article  CAS  Google Scholar 

18.
Czechowski, T. et al. Artemisia annua mutant impaired in Artemisinin synthesis demonstrates importance of nonenzymatic conversion in terpenoid metabolism. Proc. Natl. Acad. Sci. USA. 113, 15150–15155 (2016).
Article  CAS  Google Scholar 

19.
Johnson, H. B. Plant pubescence: An ecological perspective. Bot. Rev. 41, 233–258 (1975).
Article  Google Scholar 

20.
Fernald, M. L. Gray’s Manual of Botany. (American Book Company, 1950).

21.
Hernandez-Castillo, G. R., Stockey, R. A., Rothwell, G. W. & Mapes, G. Reconstructing Emporia lockardii (Voltziales: Emporiaceae) and initial thoughts on paleozoic conifer ecology. Int. J. Plant Sci. 170, 1056–1074 (2009).
Article  Google Scholar 

22.
Rothwell, G. W., Mapes, G. & Hernandez-Castillo, G. R. Hanskerpia gen. nov. and phylogenetic relationships among the most ancient conifers (Voltziales). Taxon 54, 733–750 (2005).

23.
Heinrich, M. Das Harz der Nadelhölzer, seine Entstehung, Vertheilung, Bedeutung und Gewinnung (Springer, Berlin, 1894).
Google Scholar 

24.
Tomlin, E. S., Antonejevic, E., Alfaro, R. I. & Borden, J. H. Changes in volatile terpene and diterpene resin acid composition of resistant and susceptible white spruce leaders exposed to simulated white pine weevil damage. Tree Physiol. 20, 1087–1095 (2000).
Article  CAS  Google Scholar 

25.
Birol, I. et al. Assembling the 20 Gb white spruce (Picea glauca) genome from whole-genome shotgun sequencing data. Bioinformatics 29, 1492–1497 (2013).
Article  CAS  Google Scholar 

26.
Warren, R. L. et al. Improved white spruce (Picea glauca) genome assemblies and annotation of large gene families of conifer terpenoid and phenolic defense metabolism. Plant J. 83, 189–212 (2015).
Article  CAS  Google Scholar 

27.
Zulak, K. G., Dullat, H. K., Keeling, C. I., Lippert, D. & Bohlmann, J. Immunofluorescence localization of levopimaradiene/abietadiene synthase in methyl jasmonate treated stems of Sitka spruce (Picea sitchensis) shows activation of diterpenoid biosynthesis in cortical and developing traumatic resin ducts. Phytochemistry 71, 1695–1699 (2010).
Article  CAS  Google Scholar 

28.
Whitehill, J. G. A., Henderson, H., Strong, W., Jaquish, B. & Bohlmann, J. Function of Sitka spruce stone cells as a physical defense against white pine weevil. Plant. Cell Environ. 39, 2545–2556 (2016).
Article  CAS  Google Scholar 

29.
Franceschi, V. R., Krokene, P., Krekling, T. & Christiansen, E. Phloem parenchyma cells are involved in local and distant defense responses to fungal inoculation or bark-beetle attack in Norway spruce (Pinaceae). Am. J. Bot. 87, 314–326 (2000).
Article  CAS  Google Scholar 

30.
Parent, G. J., Giguère, I., Mageroy, M., Bohlmann, J. & MacKay, J. J. Evolution of the biosynthesis of two hydroxyacetophenones in plants. Plant Cell Environ. 41, 620–629 (2018).
Article  CAS  Google Scholar 

31.
Mageroy, M. H. et al. A conifer UDP-sugar dependent glycosyltransferase contributes to acetophenone metabolism and defense against insects. Plant Physiol. 175, 00611.2017 (2017).

32.
Tissier, A. Glandular trichomes: What comes after expressed sequence tags?. Plant J. 70, 51–68 (2012).
Article  CAS  Google Scholar 

33.
Sacher, J. A. Structure and seasonal activity of the shoot apices of Pinus lambertiana and Pinus ponderosa. Am. J. Bot. 41, 749–759 (1954).
Article  Google Scholar 

34.
De Simón, B. F., Vallejo, M. C. G., Cadahía, E., Miguel, C. A. & Martinez, M. C. Analysis of lipophilic compounds in needles of Pinus pinea L. Ann. For. Sci. 58, 449–454 (2001).
Article  Google Scholar 

35.
Lange, W. & Weissman, G. Untersuchungen der Harzbalsame von Pinus resinosa Ait. und Pinus pinea L. Holz als Roh- und Werkst. 49, 476–480 (1991).

36.
Geisler, K., Jensen, N. B., Yuen, M. M. S., Madilao, L. & Bohlmann, J. Modularity of conifer diterpene resin acid biosynthesis: P450 enzymes of different CYP720B clades use alternative substrates and converge on the same products. Plant Physiol. 171, 152–164 (2016).
Article  CAS  Google Scholar 

37.
Hamberger, B., Ohnishi, T., Hamberger, B., Séguin, A. & Bohlmann, J. Evolution of diterpene metabolism: Sitka spruce CYP720B4 catalyzes multiple oxidations in resin acid biosynthesis of conifer defense against insects. Plant Physiol. 157, 1677–1695 (2011).
Article  CAS  Google Scholar 

38.
Ro, D., Arimura, G., Lau, S. Y. W., Piers, E. & Bohlmann, J. Loblolly pine abietadienol/abietadienal oxidase PtAO (CYP720B1) is a multifunctional, multisubstrate cytochrome P450 monooxygenase. Proc. Natl. Acad. Sci. USA 102, 8060–8065 (2005).
ADS  Article  CAS  Google Scholar 

39.
Hilker, M., Stein, C., Schröder, R., Varama, M. & Mumm, R. Insect egg deposition induces defence responses in Pinus sylvestris: characterisation of the elicitor. J. Exp. Biol. 208, 1849–1854 (2005).
Article  Google Scholar 

40.
Schuurink, R. & Tissier, A. Glandular trichomes: Micro-organs with model status?. New Phytol. https://doi.org/10.1111/nph.16283 (2019).
Article  PubMed  Google Scholar 

41.
Huchelmann, A., Boutry, M. & Hachez, C. Plant glandular trichomes: Natural cell factories of high biotechnological interest. Plant Physiol. 175, 00727.2017 (2017).

42.
Sallaud, C. et al. Characterization of two genes for the biosynthesis of the labdane diterpene Z-abienol in tobacco (Nicotiana tabacum) glandular trichomes. Plant J. 72, 1–17 (2012).
Article  CAS  Google Scholar 

43.
Liu, Y. et al. A geranylfarnesyl diphosphate synthase provides the precursor for sesterterpenoid (C25) formation in the glandular trichomes of the mint species Leucosceptrum canum. Plant Cell 28, 804–822 (2016).
Article  CAS  Google Scholar 

44.
Reynolds, E. S. The use of lead citrate at high pH as an electron-opaque stain in electron microscopy. J. Cell Biol. 17, 208–212 (1963).
Article  CAS  Google Scholar  More