Ecology

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

16.
Van Lent, J., Hergoualc, H. K. & Verchot, L. V. Reviews and syntheses: Soil N2O and NO emissions from land use and land-use change in the tropics and subtropics: A meta-analysis. Biogeosciences 12, 7299–7313 (2015).
ADS  Article  Google Scholar 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

1.
Pecl, G. T. et al. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).
2.
Last, P. R. et al. Long-term shifts in abundance and distribution of a temperate fish fauna: a response to climate change and fishing practices. Global Ecol. Biogeogr. 20, 58–72. https://doi.org/10.1111/j.1466-8238.2010.00575.x (2011).
Article  Google Scholar 

3.
Pitt, N. R., Poloczanska, E. S. & Hobday, A. J. Climate-driven range changes in Tasmanian intertidal fauna. Mar. Freshw. Res. 61, 963–970. https://doi.org/10.1071/MF09225 (2010).
CAS  Article  Google Scholar 

4.
Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Change 3, 919–925. https://doi.org/10.1038/nclimate1958 (2013).
ADS  Article  Google Scholar 

5.
Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42. https://doi.org/10.1038/nature01286 (2003).
ADS  CAS  Article  PubMed  Google Scholar 

6.
Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L. & Levin, S. A. Marine taxa track local climate velocities. Science 341, 1239–1242. https://doi.org/10.1126/science.1239352 (2013).
ADS  CAS  Article  PubMed  Google Scholar 

7.
McLeod, D. J., Hobday, A. J., Lyle, J. M. & Welsford, D. C. A prey-related shift in the abundance of small pelagic fish in eastern Tasmania?. ICES J. Mar. Sci. 69, 953–960. https://doi.org/10.1093/icesjms/fss069 (2012).
Article  Google Scholar 

8.
Johnson, C. R. et al. Climate change cascades: shifts in oceanography, species’ ranges and subtidal marine community dynamics in eastern Tasmania. J. Exp. Mar. Biol. Ecol. 400, 17–32. https://doi.org/10.1016/j.jembe.2011.02.032 (2011).
Article  Google Scholar 

9.
Ling, S. D. Range expansion of a habitat-modifying species leads to loss of taxonomic diversity: a new and impoverished reef state. Oecologia 156, 883–894. https://doi.org/10.1007/s00442-008-1043-9 (2008).
ADS  CAS  Article  PubMed  Google Scholar 

10.
Sunday, J. M. et al. Species traits and climate velocity explain geographic range shifts in an ocean-warming hotspot. Ecol. Lett. 18, 944–953 (2015).
Article  Google Scholar 

11.
Pecl, G. et al. The east coast Tasmanian rock lobster fishery—vulnerability to climate change impacts and adaptation response options. (Report to the Department of Climate Change, Australia, 2009).

12.
Angilletta, M. J. Thermal Adaptation: A Theoretical and Empirical Synthesis (Oxford University Press, Oxford, 2009).
Google Scholar 

13.
Fry, F. E. J. Effects of the Environment on Animal Activity (University of Toronto Press, Toronto, 1947).
Google Scholar 

14.
Sinclair, B. J. et al. Can we predict ectotherm responses to climate change using thermal performance curves and body temperatures?. Ecol. Lett. 19, 1372–1385. https://doi.org/10.1111/ele.12686 (2016).
Article  PubMed  Google Scholar 

15.
Pörtner, H. O. & Farrell, A. P. Physiology and climate change. Science 322, 690–692. https://doi.org/10.2307/20145158 (2008).
Article  PubMed  Google Scholar 

16.
Pörtner, H. O. & Knust, R. Climate change affects marine fishes through the oxygen limitation of thermal tolerance. Science 315, 95–97. https://doi.org/10.1126/science.1135471 (2007).
ADS  CAS  Article  PubMed  Google Scholar 

17.
Schulte, P. M., Healy, T. M. & Fangue, N. A. Thermal performance curves, phenotypic plasticity, and the time scales of temperature exposure. Integr. Comp. Biol. 51, 691–702. https://doi.org/10.1093/icb/icr097 (2011).
Article  PubMed  Google Scholar 

18.
Donelson, J. M., McCormick, M. I., Booth, D. J. & Munday, P. L. Reproductive acclimation to increased water temperature in a tropical reef fish. PLoS ONE 9, e97223. https://doi.org/10.1371/journal.pone.0097223 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

19.
Twiname, S., Fitzgibbon, Q. P., Hobday, A. J., Carter, C. G. & Pecl, G. T. Multiple measures of thermal performance of early stage eastern rock lobster in a fast-warming ocean region. Mar. Ecol. Prog. Ser. 624, 1–11 (2019).
ADS  CAS  Article  Google Scholar 

20.
Fitzgibbon, Q. P., Simon, C. J., Smith, G. G., Carter, C. G. & Battaglene, S. C. Temperature dependent growth, feeding, nutritional condition and aerobic metabolism of juvenile spiny lobster Sagmariasus verreauxi. Comp. Biochem. Phys. A 207, 13–20 (2017).
CAS  Article  Google Scholar 

21.
Lord, J. P., Barry, J. P. & Graves, D. Impact of climate change on direct and indirect species interactions. Mar. Ecol. Prog. Ser. 571, 1–11 (2017).
ADS  Article  Google Scholar 

22.
Dell, A. I., Pawar, S. & Savage, V. M. Temperature dependence of trophic interactions are driven by asymmetry of species responses and foraging strategy. J. Anim. Ecol. 83, 70–84. https://doi.org/10.1111/1365-2656.12081 (2014).
Article  PubMed  Google Scholar 

23.
Kordas, R. L., Harley, C. D. G. & O’Connor, M. I. Community ecology in a warming world: the influence of temperature on interspecific interactions in marine systems. J. Exp. Mar. Biol. Ecol. 400, 218–226. https://doi.org/10.1016/j.jembe.2011.02.029 (2011).
Article  Google Scholar 

24.
Marshak, A. R. & Heck, K. L. Interactions between range-expanding tropical fishes and the northern Gulf of Mexico red snapper Lutjanus campechanus. J. Fish Biol. 91, 1139–1165. https://doi.org/10.1111/jfb.13406 (2017).
CAS  Article  PubMed  Google Scholar 

25.
Milazzo, M., Mirto, S., Domenici, P. & Gristina, M. Climate change exacerbates interspecific interactions in sympatric coastal fishes. J. Anim. Ecol. 82, 468–477. https://doi.org/10.1111/j.1365-2656.2012.02034.x (2013).
Article  PubMed  Google Scholar 

26.
Grigaltchik, V. S., Ward, A. J. W. & Seebacher, F. Thermal acclimation of interactions: differential responses to temperature change alter predator-prey relationship. Proc. R. Soc. B-Biol. Sci. 279, 4058–4064. https://doi.org/10.1098/rspb.2012.1277 (2012).
Article  Google Scholar 

27.
Johansen, J. L. & Jones, G. P. Increasing ocean temperature reduces the metabolic performance and swimming ability of coral reef damselfishes. Glob. Change Biol. 17, 2971–2979. https://doi.org/10.1111/j.1365-2486.2011.02436.x (2011).
ADS  Article  Google Scholar 

28.
Batty, R. & Blaxter, J. The effect of temperature on the burst swimming performance of fish larvae. J. Exp. Biol. 170, 187–201 (1992).
Google Scholar 

29.
Temple, G. K. & Johnston, I. A. Testing hypotheses concerning the phenotypic plasticity of escape performance in fish of the family Cottidae. J. Exp. Biol. 201, 317–331 (1998).
CAS  PubMed  Google Scholar 

30.
Fry, F. E. J. & Hart, J. S. The relation of temperature to oxygen consumption in the goldfish. Biol. Bull. 94, 66–77. https://doi.org/10.2307/1538211 (1948).
CAS  Article  PubMed  Google Scholar 

31.
Clark, T. D., Sandblom, E. & Jutfelt, F. Aerobic scope measurements of fishes in an era of climate change: respirometry, relevance and recommendations. J. Exp. Biol. 216, 2771–2782. https://doi.org/10.1242/jeb.084251 (2013).
Article  PubMed  Google Scholar 

32.
Jutfelt, F. et al. Oxygen- and capacity-limited thermal tolerance: blurring ecology and physiology. J. Exp. Biol. 221, jeb169615 (2018).

33.
Pörtner, H.-O., Bock, C. & Mark, F. C. Oxygen- and capacity-limited thermal tolerance: bridging ecology and physiology. J. Exp. Biol. 220, 2685 (2017).
Article  Google Scholar 

34.
Norin, T., Malte, H. & Clark, T. D. Aerobic scope does not predict the performance of a tropical eurythermal fish at elevated temperatures. J. Exp. Biol. 217, 244–251. https://doi.org/10.1242/jeb.089755 (2014).
Article  PubMed  Google Scholar 

35.
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789. https://doi.org/10.1890/03-9000 (2004).
Article  Google Scholar 

36.
Domenici, P. & Blake, R. The kinematics and performance of fish fast-start swimming. J. Exp. Biol. 200, 1165–1178 (1997).
CAS  PubMed  Google Scholar 

37.
Dell, A. I., Pawar, S. & Savage, V. M. Systematic variation in the temperature dependence of physiological and ecological traits. Proc. Natl. Acad. Sci. USA 108, 10591–10596. https://doi.org/10.1073/pnas.1015178108 (2011).
ADS  Article  PubMed  Google Scholar 

38.
Ohlund, G., Hedstrom, P., Norman, S., Hein, C. L. & Englund, G. Temperature dependence of predation depends on the relative performance of predators and prey. Proc. R. Soc. B-Biol. Sci. https://doi.org/10.1098/rspb.2014.2254 (2015).
Article  Google Scholar 

39.
England, W. R. & Baldwin, J. Anaerobic energy metabolism in the tail musculature of the Australian Yabby Cherax destructor (Crustacea, Decapoda, Parastacidae): role of phosphagens and anaerobic glycolysis during escape behavior. Physiol. Zool. 56, 614–622 (1983).
CAS  Article  Google Scholar 

40.
De Zwaan, A. & v.d. Thillart, G. in Circulation, Respiration, and Metabolism (ed Raymond Gilles) 166–192 (Springer, Berlin, 1985).

41.
Ellington, W. R. The recovery from anaerobic metabolism in invertebrates. J. Exp. Zool. 228, 431–444. https://doi.org/10.1002/jez.1402280305 (1983).
CAS  Article  Google Scholar 

42.
Hobday, A. & Pecl, G. Identification of global marine hotspots: sentinels for change and vanguards for adaptation action. Rev. Fish Biol. Fisher 24, 415–425. https://doi.org/10.1007/s11160-013-9326-6 (2014).
Article  Google Scholar 

43.
Ridgway, K. R. Long-term trend and decadal variability of the southward penetration of the East Australian Current. Geophys. Res. Lett. 34, L13613. https://doi.org/10.1029/2007GL030393 (2007).
ADS  Article  Google Scholar 

44.
Robinson, L. et al. Rapid assessment of an ocean warming hotspot reveals “high” confidence in potential species’ range extensions. Glob. Environ. Change 31, 28–37 (2015).
Article  Google Scholar 

45.
Redmap Australia. Institute for Marine and Antarctic Studies, University of Tasmania, (2020).

46.
Ling, S. D., Johnson, C. R., Ridgway, K., Hobday, A. J. & Haddon, M. Climate-driven range extension of a sea urchin: inferring future trends by analysis of recent population dynamics. Glob. Change Biol. 15, 719–731. https://doi.org/10.1111/j.1365-2486.2008.01734.x (2009).
ADS  Article  Google Scholar 

47.
Booth, D. J., Figueira, W. F., Gregson, M. A., Brown, L. & Beretta, G. Occurrence of tropical fishes in temperate southeastern Australia: role of the East Australian Current. Estuar. Coast Shelf Sci. 72, 102–114. https://doi.org/10.1016/j.ecss.2006.10.003 (2007).
ADS  Article  Google Scholar 

48.
Figueira, W. F., Biro, P., Booth, D. J. & Valenzuela, V. C. Performance of tropical fish recruiting to temperate habitats: role of ambient temperature and implications of climate change. Mar. Ecol. Prog. Ser. 384, 231–239. https://doi.org/10.3354/meps08057 (2009).
ADS  Article  Google Scholar 

49.
Cetina-Heredia, P., Roughan, M., Sebille, E., Feng, M. & Coleman, M. A. Strengthened currents override the effect of warming on lobster larval dispersal and survival. Glob. Change Biol. 21, 4377–4386. https://doi.org/10.1111/gcb.13063 (2015).
ADS  Article  Google Scholar 

50.
Dahlke, F. T., Wohlrab, S., Butzin, M. & Pörtner, H.-O. Thermal bottlenecks in the life cycle define climate vulnerability of fish. Science 369, 65–70. https://doi.org/10.1126/science.aaz3658 (2020).
ADS  CAS  Article  PubMed  Google Scholar 

51.
Fitzgibbon, Q. P., Ruff, N., Tracey, S. R. & Battaglene, S. C. Thermal tolerance of the nektonic puerulus stage of spiny lobsters and implications of ocean warming. Mar. Ecol. Prog. Ser. 515, 173–186. https://doi.org/10.3354/meps10979 (2014).
ADS  Article  Google Scholar 

52.
Storch, D., Fernández, M., Navarrete, S. A. & Pörtner, H. O. Thermal tolerance of larval stages of the Chilean kelp crab Taliepus dentatus. Mar. Ecol. Prog. Ser. 429, 157–167 (2011).
ADS  Article  Google Scholar 

53.
Walther, K., Anger, K. & Pörtner, H. O. Effects of ocean acidification and warming on the larval development of the spider crab Hyas araneus from different latitudes (54° vs. 79°N). Mar. Ecol. Prog. Ser. 417, 159–170 (2010).
ADS  Article  Google Scholar 

54.
54Phillips, B. F., Booth, J. D., Cobb, J. S., Jeffs, A. G. & McWilliam, P. in Lobsters: Biology, Management, Aquaculture and Fisheries (ed Bruce F. Phillips) 231–262 (Blackwell Publishing Ltd, 2006).

55.
Hamner, W. M., Jones, M. S., Carleton, J. H., Hauri, I. R. & Williams, D. M. Zooplankton, planktivorous fish, and water currents on a windward reef face: Great Barrier Reef Australia. Bull. Mar. Sci. 42, 459–479 (1988).
Google Scholar 

56.
Emery, A. R. Comparative ecology and functional osteology of fourteen species of damselfish (Pisces: Pomacentridae) at alligator Reef Florida keys. Bull. Mar. Sci. 23, 649–770 (1973).
Google Scholar 

57.
Hinkle, P. C. P/O ratios of mitochondrial oxidative phosphorylation. BBA-Bioenergetics 1706, 1–11. https://doi.org/10.1016/j.bbabio.2004.09.004 (2005).
CAS  Article  PubMed  Google Scholar 

58.
Pörtner, H. O. Climate variations and the physiological basis of temperature dependent biogeography: systemic to molecular hierarchy of thermal tolerance in animals. Comp. Biochem. Phys. A 132, 739–761. https://doi.org/10.1016/S1095-6433(02)00045-4 (2002).
Article  Google Scholar 

59.
Speed, S. R., Baldwin, J., Wong, R. J. & Wells, R. M. G. Metabolic characteristics of muscles in the spiny lobster, Jasus edwardsii, and responses to emersion during simulated live transport. Comp. Biochem. Phys. B 128, 435–444. https://doi.org/10.1016/S1096-4959(00)00340-7 (2001).
CAS  Article  Google Scholar 

60.
Morris, S. & Adamczewska, A. M. Utilisation of glycogen, ATP and arginine phosphate in exercise and recovery in terrestrial red crabs Gecarcoidea natalis. Comp. Biochem. Phys. A 133, 813–825. https://doi.org/10.1016/S1095-6433(02)00217-9 (2002).
Article  Google Scholar 

61.
Head, G. & Baldwin, J. Energy metabolism and the fate of lactate during recovery from exercise in the Australian freshwater crayfish Cherax destructor. Mar. Freshw. Res. 37, 641–646. https://doi.org/10.1071/MF9860641 (1986).
CAS  Article  Google Scholar 

62.
Clark, T. D., Messmer, V., Tobin, A. J., Hoey, A. S. & Pratchett, M. S. Rising temperatures may drive fishing-induced selection of low-performance phenotypes. Sci. Rep. 7, 40571. https://doi.org/10.1038/srep40571 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

63.
Foo, S. A. & Byrne, M. Acclimatization and Adaptive capacity of marine species in a changing ocean. Adv. Mar. Biol. 74, 69–116. https://doi.org/10.1016/bs.amb.2016.06.001 (2016).
CAS  Article  PubMed  Google Scholar 

64.
Evans, T. G., Diamond, S. E. & Kelly, M. W. Mechanistic species distribution modelling as a link between physiology and conservation. Conserv. Physiol. 3, cov056. https://doi.org/10.1093/conphys/cov056 (2015).
Article  PubMed  PubMed Central  Google Scholar 

65.
Seebacher, F., White, C. R. & Franklin, C. E. Physiological plasticity increases resilience of ectothermic animals to climate change. Nat. Clim. Change 5, 61–66. https://doi.org/10.1038/nclimate2457 (2015).
ADS  Article  Google Scholar 

66.
Donelson, J. M., Munday, P. L., McCormick, M. I. & Nilsson, G. E. Acclimation to predicted ocean warming through developmental plasticity in a tropical reef fish. Glob. Change Biol. 17, 1712–1719. https://doi.org/10.1111/j.1365-2486.2010.02339.x (2011).
ADS  Article  Google Scholar 

67.
Oliver, E. C. J. et al. The unprecedented 2015/16 Tasman Sea marine heatwave. Nat. Commun. 8, 16101. https://doi.org/10.1038/ncomms16101 (2017).
ADS  Article  PubMed  PubMed Central  Google Scholar 

68.
Oliver, E. C. J. et al. Marine heatwaves off eastern Tasmania: trends, interannual variability, and predictability. Prog. Oceanogr. 161, 116–130. https://doi.org/10.1016/j.pocean.2018.02.007 (2018).
ADS  Article  Google Scholar 

69.
Stobart, B., Mayfield, S., Mundy, C., Hobday, A. J. & Hartog, J. R. Comparison of in situ and satellite sea surface-temperature data from South Australia and Tasmania: how reliable are satellite data as a proxy for coastal temperatures in temperate southern Australia?. Mar. Freshw. Res. 67, 612–625. https://doi.org/10.1071/MF14340 (2016).
Article  Google Scholar 

70.
Grose, M. R. et al. Climate Futures for Tasmania: general climate impacts technical report. (Antarctic Climate & Ecosystems Cooperative Research Centre, Hobart, Tasmania., 2010).

71.
Gilman, S. E. Predicting indirect effects of predator-prey interactions. Integr. Comp. Biol. 57, 148–158. https://doi.org/10.1093/icb/icx031 (2017).
Article  PubMed  Google Scholar 

72.
Fitzgibbon, Q. P. & Battaglene, S. C. Effect of photoperiod on the culture of early-stage phyllosoma and metamorphosis of spiny lobster (Sagmariasus verreauxi). Aquaculture 368, 48–54. https://doi.org/10.1016/j.aquaculture.2012.09.018 (2012).
Article  Google Scholar 

73.
Fitzgibbon, Q. P., Battaglene, S. C. & Ritar, A. J. Effect of water temperature on the development and energetics of early, mid and late-stage phyllosoma larvae of spiny lobster Sagmariasus verreauxi. Aquaculture 344–349, 153–160. https://doi.org/10.1016/j.aquaculture.2012.03.008 (2012).
Article  Google Scholar 

74.
Jensen, M. A., Fitzgibbon, Q. P., Carter, C. G. & Adams, L. R. Effect of body mass and activity on the metabolic rate and ammonia-N excretion of the spiny lobster Sagmariasus verreauxi during ontogeny. Comp. Biochem. Phys. A 166, 191–198. https://doi.org/10.1016/j.cbpa.2013.06.003 (2013).
CAS  Article  Google Scholar 

75.
Fitzgibbon, Q. P., Jeffs, A. G. & Battaglene, S. C. The Achilles heel for spiny lobsters: the energetics of the non-feeding post-larval stage. Fish Fish 15, 312–326. https://doi.org/10.1111/faf.12018 (2014).
Article  Google Scholar 

76.
Harvey, E., Shortis, M., Stadler, M. & Cappo, M. A Comparison of the accuracy and precision of measurements from single and stereo-video systems. Mar. Technol. Soc. J. 36, 38–49. https://doi.org/10.4031/002533202787914106 (2002).
Article  Google Scholar 

77.
R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2017).

78.
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 

79.
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, Berlin, 2009).
Google Scholar  More