Ecology

Chemical properties of submicrometer aerosols
Figure 1a presents the time series of the mass concentrations of OC and water-soluble OC (WSOC) in the submicrometer aerosol particles during the cruise. Overall, the patterns of the temporal OC and WSOC variations were similar to each other (r = 0.632, p  More

1.
Cohen, J. et al. Nat. Geosci. 7, 627–637 (2014).
CAS  Article  Google Scholar 
2.
Xiao, J. & Zhuang, Q. Environ. Res. Lett. 2, 044003 (2007).
Article  Google Scholar 

3.
Veraverbeke, S. et al. Nat. Clim. Change 7, 529–534 (2017).
Article  Google Scholar 

4.
Balshi, M. S. et al. J. Geophys. Res. 112, G02029 (2007).
Article  Google Scholar 

5.
Kelly, R., Genet, H., McGuire, A. D. & Hu, F. S. Nat. Clim. Change 6, 79–82 (2016).
CAS  Article  Google Scholar 

6.
Walker, X. J. et al. Nat. Clim. Change https://doi.org/10.1038/s41558-020-00920-8 (2020).

7.
Harden, J. W. et al. Glob. Chang. Biol. 6, 174–184 (2000).
Article  Google Scholar 

8.
Harmon, M. E. J. For. 99, 24–29 (2001).
Google Scholar 

9.
Loehman, R. A., Reinhardt, E. & Riley, K. L. For. Ecol. Manag. 317, 9–19 (2014).
Article  Google Scholar 

10.
Fauria, M. M. & Johnson, E. A. J. Geophys. Res.-Biogeo. 111, G04008 (2006).
Google Scholar 

11.
Holden, Z. A. & Jolly, W. M. For. Ecol. Manag. 262, 2133–2141 (2011).
Article  Google Scholar 

12.
Johnstone, J. F., Hollingsworth, T. N., Chapin, F. S. & Mack, M. C. Glob. Chang. Biol. 16, 1281–1295 (2010).
Article  Google Scholar  More

1.
Serreze, M. C., Holland, M. M. & Stroeve, J. Perspectives on the Arctic’s shrinking sea-ice cover. Science 315, 1533–1536 (2007).
CAS  Article  Google Scholar 
2.
Screen, J. A. & Simmonds, I. The central role of diminishing sea ice in recent Arctic temperature amplification. Nature 464, 1334–1337 (2010).
CAS  Article  Google Scholar 

3.
Dai, A., Luo, D., Song, M. & Liu, J. Arctic amplification is caused by sea-ice loss under increasing CO2. Nat. Commun. 10, 121–133 (2019).
Article  CAS  Google Scholar 

4.
Kim, K. et al. Vertical feedback mechanism of winter Arctic amplification and sea ice loss. Sci. Rep. 9, 1184 (2019).
Article  CAS  Google Scholar 

5.
Loeb, V. et al. Effects of sea-ice extent and krill or salp dominance on the Antarctic food web. Nature 387, 897–900 (1997).
CAS  Article  Google Scholar 

6.
Mundy, C. J. et al. Contribution of under-ice primary production to an ice-edge upwelling phytoplankton bloom in the Canadian Beaufort Sea. Geophys. Res. Lett. 36, L17601 (2009).
Article  Google Scholar 

7.
Sévellec, F., Fedorov, A. V. & Liu, W. Arctic sea-ice decline weakens the Atlantic Meridional Overturning Circulation. Nat. Clim. Chang. 7, 604–610 (2017).
Article  Google Scholar 

8.
Stroeve, J., Holland, M. M., Meier, W., Scambos, T. & Serreze, M. Arctic sea ice decline: faster than forecast. Geophys. Res. Lett. 34, L09501 (2007).
Article  Google Scholar 

9.
Routson, C. C. et al. Mid-latitude net precipitation decreased with Arctic warming during the Holocene. Nature 568, 83–87 (2019).
CAS  Article  Google Scholar 

10.
Parkinson, C. L. & Cavalieri, D. J. Arctic sea ice variability and trends, 1979–2006. J. Geophys. Res. 113, C07003 (2008).
Article  Google Scholar 

11.
Stroeve, J. C. et al. Trends in Arctic sea ice extent from CMIP5, CMIP3 and observations. Geophys. Res. Lett. 39, L16502 (2012).
Article  Google Scholar 

12.
Matsumura, S. & Kosaka, Y. Arctic–Eurasian climate linkage induced by tropical ocean variability. Nat. Commun. 10, 1–8 (2019).
CAS  Article  Google Scholar 

13.
Kaufman, D. S. et al. Holocene thermal maximum in the western Arctic (0-180°W). Quat. Sci. Rev. 23, 529–560 (2004).
Article  Google Scholar 

14.
Holmes, R. M. et al. A circumpolar perspective on fluvial sediment flux to the Arctic ocean. Global Biogeochem. Cycles 16, 1098 (2002).
Article  CAS  Google Scholar 

15.
Duk-Rodkin, A. & Hughes, O. L. Tertiary-quaternary drainage of the pre-glacial Mackenzie basin. Quat. Int. 22–23, 221–241 (1994).
Article  Google Scholar 

16.
McManus, J. F., Francois, R., Gherardl, J. M., Keigwin, L. & Drown-Leger, S. Collapse and rapid resumption of Atlantic meridional circulation linked to deglacial climate changes. Nature 428, 834–837 (2004).
CAS  Article  Google Scholar 

17.
Peltier, W. R., Vettoretti, G. & Stastna, M. Atlantic meridional overturning and climate response to Arctic Ocean freshening. Geophys. Res. Lett. 33, L06713 (2006).
Google Scholar 

18.
Broecker, W. S. et al. Routing of meltwater from the Laurentide Ice Sheet during the Younger Dryas cold episode. Nature 341, 318–321 (1989).
Article  Google Scholar 

19.
Keigwin, L. D. et al. Deglacial floods in the Beaufort Sea preceded Younger Dryas cooling. Nat. Geosci. 11, 599–604 (2018).
CAS  Article  Google Scholar 

20.
Leydet, D. J. et al. Opening of glacial Lake Agassiz’s eastern outlets by the start of the Younger Dryas cold period. Geology 46, 155–158 (2018).
CAS  Article  Google Scholar 

21.
Fisher, T. G. & Lowell, T. V. Testing northwest drainage from Lake Agassiz using extant ice margin and strandline data. Quat. Int. 260, 106–114 (2012).
Article  Google Scholar 

22.
Tarasov, L. & Peltier, W. R. Arctic freshwater forcing of the Younger Dryas cold reversal. Nature 435, 662–665 (2005).
CAS  Article  Google Scholar 

23.
Murton, J. B., Bateman, M. D., Dallimore, S. R., Teller, J. T. & Yang, Z. Identification of Younger Dryas outburst flood path from Lake Agassiz to the Arctic Ocean. Nature 464, 740–743 (2010).
CAS  Article  Google Scholar 

24.
Fisher, T. G., Waterson, N., Lowell, T. V. & Hajdas, I. Deglaciation ages and meltwater routing in the Fort McMurray region, northeastern Alberta and northwestern Saskatchewan, Canada. Quat. Sci. Rev. 28, 1608–1624 (2009).
Article  Google Scholar 

25.
Fisher, T. G., Smith, D. G. & Andrews, J. T. Preboreal oscillation caused by a glacial Lake Agassiz flood. Quat. Sci. Rev. 21, 873–878 (2002).
Article  Google Scholar 

26.
Jin, Y. K. ARA04C cruise report: barrow, US—Beaufort Sea, CAN—Nome, US 6-24 September 2013 (Korea Polar Research Institute, Incheon, 2013).

27.
Gamboa, A., Montero-Serrano, J. -C., St-Onge, G., Rochon, A. & Desiage, P. -A. Mineralogical, geochemical, and magnetic signatures of surface sediments from the Canadian Beaufort Shelf and Amundsen Gulf (Canadian Arctic). Geochem. Geophys. Geosyst. 18, 488–512 (2017).
CAS  Article  Google Scholar 

28.
Belt, S. T. et al. A novel chemical fossil of palaeo sea ice: IP25. Org. Geochem. 38, 16–27 (2007).
CAS  Article  Google Scholar 

29.
Brown, T. A., Belt, S. T., Tatarek, A. & Mundy, C. J. Source identification of the Arctic sea ice proxy IP 25. Nat. Commun. 5, 4197 (2014).
CAS  Article  Google Scholar 

30.
Müller, J. et al. Towards quantitative sea ice reconstructions in the northern North Atlantic: a combined biomarker and numerical modelling approach. Earth Planet. Sci. Lett. 306, 137–148 (2011).
Article  CAS  Google Scholar 

31.
Smik, L., Cabedo-Sanz, P. & Belt, S. T. Semi-quantitative estimates of paleo Arctic sea ice concentration based on source-specific highly branched isoprenoid alkenes: A further development of the PIP25 index. Org. Geochem. 92, 63–69 (2016).
CAS  Article  Google Scholar 

32.
Lü, X. et al. Hydroxylated isoprenoid GDGTs in Chinese coastal seas and their potential as a paleotemperature proxy for mid-to-low latitude marginal seas. Org. Geochem. 89, 31–43 (2015).
Article  CAS  Google Scholar 

33.
Volkman, J. K. A review of sterol markers for marine and terrigenous organic matter. Org. Geochem. 9, 83–99 (1986).
CAS  Article  Google Scholar 

34.
Fahl, K. & Stein, R. Biomarkers as organic-carbon-source and environmental indicators in the late quaternary Arctic Ocean: problems and perspectives. Mar. Chem. 63, 293–309 (1999).
CAS  Article  Google Scholar 

35.
Fahl, K. & Stein, R. Modern seasonal variability and deglacial/Holocene change of central Arctic Ocean sea-ice cover: new insights from biomarker proxy records. Earth Planet. Sci. Lett. 351, 123–133 (2012).
Article  CAS  Google Scholar 

36.
Rampen, S. W., Abbas, B. A., Schouten, S. & Damsté, J. S. S. A comprehensive study of sterols in marine diatoms (Bacillariophyta): implications for their use as tracers for diatom productivity. Limnol. Oceanogr. 55, 91–105 (2010).
CAS  Article  Google Scholar 

37.
Hopmans, E. C. et al. A novel proxy for terrestrial organic matter in sediments based on branched and isoprenoid tetraether lipids. Earth Planet. Sci. Lett. 224, 107–116 (2004).
CAS  Article  Google Scholar 

38.
Weijers, J. W. H., Schouten, S., van den Donker, J. C., Hopmans, E. C. & Sinninghe Damsté, J. S. Environmental controls on bacterial tetraether membrane lipid distribution in soils. Geochim. Cosmochim. Acta 71, 703–713 (2007).
CAS  Article  Google Scholar 

39.
Blaga, C. I. et al. Branched glycerol dialkyl glycerol tetraethers in lake sediments: can they be used as temperature and pH proxies? Org. Geochem. 41, 1225–1234 (2010).
CAS  Article  Google Scholar 

40.
De Jonge, C. et al. In situ produced branched glycerol dialkyl glycerol tetraethers in suspended particulate matter from the Yenisei River, Eastern Siberia. Geochim. Cosmochim. Acta 125, 476–491 (2014).
Article  CAS  Google Scholar 

41.
Zhang, Z., Metzger, P. & Sachs, J. P. Co-occurrence of long chain diols, keto-ols, hydroxy acids and keto acids in recent sediments of Lake El Junco, Galápagos Islands. Org. Geochem. 42, 823–837 (2011).
CAS  Article  Google Scholar 

42.
de Bar, M. W. et al. Constraints on the application of long chain diol proxies in the Iberian Atlantic margin. Org. Geochem. 101, 184–195 (2016).
Article  CAS  Google Scholar 

43.
Lattaud, J. et al. The C32 alkane-1,15-diol as a proxy of late Quaternary riverine input in coastal margins. Clim. Past 13, 1049–1061 (2017).
Article  Google Scholar 

44.
Lattaud, J. et al. The C32 alkane-1,15-diol as a tracer for riverine input in coastal seas. Geochim. Cosmochim. Acta 202, 146–158 (2017).
CAS  Article  Google Scholar 

45.
Pico, T., Mitrovica, J. X. & Mix, A. C. Sea level fingerprinting of the Bering Strait flooding history detects the source of the Younger Dryas climate event. Sci. Adv. 6, eaay2935 (2020).
CAS  Article  Google Scholar 

46.
Jakobsson, M. et al. Post-glacial flooding of the Bering Land Bridge dated to 11 cal ka BP based on new geophysical and sediment records. Clim. Past 13, 991–1005 (2017).
Article  Google Scholar 

47.
Laskar, J. et al. A long-term numerical solution for the insolation quantities of the Earth. Astron. Astrophys. 428, 261–285 (2004).
Article  Google Scholar 

48.
Niebauer, H. J. & Alexander, V. Oceanographic frontal structure and biological production at an ice edge. Cont. Shelf Res. 4, 367–388 (1985).
Article  Google Scholar 

49.
Smith, W. O. & Nelson, D. M. Phytoplankton bloom produced by a receding ice edge in the Ross Sea: spatial coherence with the density field. Science. 227, 163–166 (1985).
CAS  Article  Google Scholar 

50.
Ackley, S. F. & Sullivan, C. W. Physical controls on the development and characteristics of Antarctic sea ice biological communities-a review and synthesis. Deep Sea Res. I 41, 1583–1604 (1994).
Article  Google Scholar 

51.
Strass, V. H. & Nöthig, E. M. Seasonal shifts in ice edge phytoplankton blooms in the Barents Sea related to the water column stability. Polar Biol. 16, 409–422 (1996).
Article  Google Scholar 

52.
Collins, L. G. et al. Evaluating highly branched isoprenoid (HBI) biomarkers as a novel Antarctic sea-ice proxy in deep ocean glacial age sediments. Quat. Sci. Rev. 79, 87–98 (2013).
Article  Google Scholar 

53.
Belt, S. T. et al. Identification of paleo Arctic winter sea ice limits and the marginal ice zone: optimised biomarker-based reconstructions of late Quaternary Arctic sea ice. Earth Planet. Sci. Lett. 431, 127–139 (2015).
CAS  Article  Google Scholar 

54.
Smik, L., Belt, S. T., Lieser, J. L., Armand, L. K. & Leventer, A. Distributions of highly branched isoprenoid alkenes and other algal lipids in surface waters from East Antarctica: further insights for biomarker-based paleo sea-ice reconstruction. Org. Geochem. 95, 71–80 (2016).
CAS  Article  Google Scholar 

55.
Ribeiro, S. et al. Sea ice and primary production proxies in surface sediments from a High Arctic Greenland fjord: spatial distribution and implications for palaeoenvironmental studies. Ambio 46, 106–118 (2017).
CAS  Article  Google Scholar 

56.
Wagner, A., Lohmann, G. & Prange, M. Arctic river discharge trends since 7ka BP. Glob. Planet. Change 79, 48–60 (2011).
Article  Google Scholar 

57.
North Greenland Ice Core Project Members. High resolution record of Northern Hemisphere climate extending into the last interglacial period. Nature 431, 147–151 (2004).
Article  CAS  Google Scholar 

58.
Broecker, W. S. Was the Younger Dryas triggered by a flood? Science 312, 1146–1148 (2006).
CAS  Article  Google Scholar 

59.
Not, C. & Hillaire-Marcel, C. Enhanced sea-ice export from the Arctic during the Younger Dryas. Nat. Commun. 3, 1–5 (2012).
Article  CAS  Google Scholar 

60.
Fagel, N., Not, C., Gueibe, J., Mattielli, N. & Bazhenova, E. Late Quaternary evolution of sediment provenances in the Central Arctic Ocean: mineral assemblage, trace element composition and Nd and Pb isotope fingerprints of detrital fraction from the Northern Mendeleev Ridge. Quat. Sci. Rev. 92, 140–154 (2014).
Article  Google Scholar 

61.
Scott, D. B., Schell, T., St-Onge, G., Rochon, A. & Blasco, S. Foraminiferal assemblage changes over the last 15,000 years on the Mackenzie-Beaufort Sea Slope and Amundsen Gulf, Canada: implications for past sea ice conditions. Paleoceanography 24, PA2219 (2009).
Article  Google Scholar 

62.
Keigwin, L. D., Donnelly, J. P., Cook, M. S., Driscoll, N. W. & Brigham-Grette, J. Rapid sea-level rise and Holocene climate in the Chukchi Sea. Geology 34, 861–864 (2006).
Article  Google Scholar 

63.
Hill, J. C. & Driscoll, N. W. Paleodrainage on the Chukchi shelf reveals sea level history and meltwater discharge. Mar. Geol. 254, 129–151 (2008).
CAS  Article  Google Scholar 

64.
England, J. H. & Furze, M. F. A. New evidence from the western Canadian Arctic Archipelago for the resubmergence of Bering Strait. Quat. Res. 70, 60–67 (2008).
CAS  Article  Google Scholar 

65.
Dyke, A. S. & Savelle, J. M. Holocene history of the Bering Sea bowhead whale (Balaena mysticetus) in its Beaufort Sea summer grounds off Southwestern Victoria Island, Western Canadian Arctic. Quat. Res. 55, 371–379 (2001).
Article  Google Scholar 

66.
Dyke, A. S., Dale, J. E. & McNeely, R. N. Marine molluscs as indicators of environmental change in glaciated North America and greenland during the last 18 000 Years. Geogr. Phys. Quat. 50, 125–184 (1996).
Google Scholar 

67.
Belt, S. T., Smik, L., Köseoglu, D., Knies, J. & Husum, K. A novel biomarker-based proxy for the spring phytoplankton bloom in Arctic and sub-arctic settings–HBI T25. Earth Planet. Sci. Lett. 523, 115703 (2019).
CAS  Article  Google Scholar 

68.
Fietz, S., Huguet, C., Rueda, G., Hambach, B. & Rosell-Melé, A. Hydroxylated isoprenoidal GDGTs in the Nordic Seas. Mar. Chem. 152, 1–10 (2013).
CAS  Article  Google Scholar 

69.
Klotsko, S., Driscoll, N. & Keigwin, L. Multiple meltwater discharge and ice rafting events recorded in the deglacial sediments along the Beaufort Margin, Arctic Ocean. Quat. Sci. Rev. 203, 185–208 (2019).
Article  Google Scholar 

70.
Sachs, J. P. et al. An Arctic Ocean paleosalinity proxy from δ2H of palmitic acid provides evidence for deglacial Mackenzie River flood events. Quat. Sci. Rev. 198, 76–90 (2018).
Article  Google Scholar 

71.
Spielhagen, R. F., Erlenkeuser, H. & Siegert, C. History of freshwater runoff across the Laptev Sea (Arctic) during the last deglaciation. Glob. Planet. Change 48, 187–207 (2005).
Article  Google Scholar 

72.
Nørgaard-pedersen, N. et al. Arctic Ocean during the Last Glacial Maximum: Atlantic and polar domains of surface water mass distribution and ice cover. Paleoceanography 18, 1–19 (2003).
Article  Google Scholar 

73.
Stein, R. et al. The last deglaciation event in the eastern central Arctic. Ocean Sci. 264, 692–696 (1994).
CAS  Google Scholar 

74.
Poore, R. Z., Osterman, L., Hole, W. & Hole, W. Late Pleistocene and Holocene meltwater events in the western Arctic Ocean. Geology 27, 759–762 (1999).
CAS  Article  Google Scholar 

75.
Stein, R., Fahl, K. & Müller, J. Proxy reconstruction of Cenozoic Arctic Ocean sea ice history–from IRD to IP25. Polarforschung 82, 37–71 (2012).
Google Scholar 

76.
Häggi, C. et al. Modern and late Pleistocene particulate organic carbon transport by the Amazon River: Insights from long-chain alkyl diols. Geochim. Cosmochim. Acta 262, 1–19 (2019).
Article  CAS  Google Scholar 

77.
Breckenridge, A. The Tintah-Campbell gap and implications for glacial Lake Agassiz drainage during the Younger Dryas cold interval. Quat. Sci. Rev. 117, 124–134 (2015).
Article  Google Scholar 

78.
Praetorius, S. et al. The role of Northeast Pacific meltwater events in deglacial climate change. Sci. Adv. 6, eaay2915 (2020).
Article  Google Scholar 

79.
Boden, P., Fairbanks, G., Wright, D. & Burckle, H. High-resolution stable isotope records from southwest Sweden: the drainage of the Baltic Ice Lake and Younger Dryas ice margin oscillations. Paleoceanography 12, 39–49 (1997).
Article  Google Scholar 

80.
Schell, T. M., Scott, D. B., Rochon, A. & Blasco, S. Late quaternary paleoceanography and paleo-sea ice conditions in the Mackenzie Trough and Canyon, Beaufort Sea. Can. J. Earth Sci. 45, 1399–1415 (2008).
Article  Google Scholar 

81.
Andrews, J. T. & Dunhill, G. Early to mid-Holocene Atlantic water influx and deglacial meltwater events, Beaufort Sea slope, Arctic Ocean. Quat. Res. 61, 14–21 (2004).
CAS  Article  Google Scholar 

82.
Winterfeld, M. et al. Deglacial mobilization of pre-aged terrestrial carbon from degrading permafrost. Nat. Commun. 9, 3666 (2018).
Article  CAS  Google Scholar 

83.
Meyer, V. D. et al. Permafrost-carbon mobilization in Beringia caused by deglacial meltwater runoff, sea-level rise and warming. Environ. Res. Lett. 14, 085003 (2019).
CAS  Article  Google Scholar 

84.
Stein, R., Fahl, K., Dittmers, K., Nissen, F. & Stepanets, O. V. Holocene siliciclastic and organic carbon fluxes in the Oh and Yenisei estuaries and the adjacent inner Kara Sea: Quantification, variability, and paleoenvironmental implications. In Siberian River Run-off in the Kara Sea: Characterisation, Quantification, Variability and Environmental Significance 401–432 (Elsevier, Amsterdam, 2003).

85.
Stein, R. & Fahl, K. The Kara Sea: distribution, sources, variability and burial of organic carbon. in The Organic Carbon Cycle in the Arctic Ocean 213–237 (Springer-Verlag, Berlin, 2004). .

86.
Pearce, C. et al. Heinrich 0 on the east Canadian margin: source, distribution, and timing. Paleoceanography 30, 1613–1624 (2015).
Article  Google Scholar 

87.
Blaauw, M. & Christeny, J. A. Flexible paleoclimate age-depth models using an autoregressive gamma process. Bayesian Anal 6, 457–474 (2011).
Google Scholar 

88.
Blaauw, M. & Christen, J. A. Bacon Manual—v2.3.3 (2013).

89.
Reimer, P. J. et al. Intcal13 and Marine13 Radiocarbon Age Calibration Curves 0–50,000 Years Cal Bp. Radiocarbon 55, 1869–1887 (2013).
CAS  Article  Google Scholar 

90.
Brown, T. A. & Belt, S. T. Novel tri- and tetra-unsaturated highly branched isoprenoid (HBI) alkenes from the marine diatom Pleurosigma intermedium. Org. Geochem. 91, 120–122 (2016).
CAS  Article  Google Scholar 

91.
Boon, J. J. et al. Black Sea sterol—a molecular fossil for dinoflagellate blooms. Nature 277, 125–127 (1979).
CAS  Article  Google Scholar 

92.
Versteegh, G., Bosch, H. & De Leeuw, J. Potential palaeoenvironmental information of C24 to C36 mid-chain diols, keto-ols and mid-chain hydroxy fatty acids; a critical review. Org. Geochem. 27, 1–13 (1997).
CAS  Article  Google Scholar 

93.
Rampen, S. W. et al. Long chain 1,13- and 1,15-diols as a potential proxy for palaeotemperature reconstruction. Geochim. Cosmochim. Acta 84, 204–216 (2012).
CAS  Article  Google Scholar 

94.
Stein, R. & Macdonald, R. W. The Organic Carbon Cycle in the Arctic Ocean (Springer-Verlag, Berlin, 2004).

95.
Wu, J. et al. Biomarker data of sediment core ARA04C/37, Beaufort Sea, Arctic Ocean. PANGAEA https://doi.org/10.1594/PANGAEA.915048 (2020).

96.
Peltier, W. R., Argus, D. F. & Drummond, R. Space geodesy constrains ice age terminal deglaciation: the global ICE-6G_C (VM5a) model. J. Geophys. Res. Solid Earth 120, 450–487 (2015).
Article  Google Scholar  More

1.
Muth, N. Z., Kluger, E. C., Levy, J. H., Edwards, M. J. & Niesenbaum, R. A. Increased per capita herbivory in the shade: necessity, feedback, or luxury consumption?. Ecoscience 15, 182–188 (2008).
Article  Google Scholar 
2.
Karolewski, P., Giertych, M. J., Żmuda, M., Jagodziński, A. M. & Oleksyn, J. Season and light affect constitutive defenses of understory shrub species against folivorous insects. Acta Oecol. 53, 19–32 (2013).
ADS  Article  Google Scholar 

3.
Łukowski, A., Giertych, M. J., Zadworny, M., Mucha, J. & Karolewski, P. Preferential feeding and occupation of sunlit leaves favors defense response and development in the flea beetle, Altica brevicollis coryletorum: a pest of Corylus avellana. PLoS ONE 10(4), e0126072 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

4.
Uyi, O. O., Zachariades, C., Heshula, L. U. & Hill, M. P. Developmental and reproductive performance of a specialist herbivore depend on seasonality of, and light conditions experienced by, the host plant. PLoS ONE 13(1), e0190700 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

5.
Moore, L. V., Myers, J. H. & Eng, R. Western tent caterpillars prefer the sunny side of the tree but why?. Oikos 51, 321–326 (1988).
Article  Google Scholar 

6.
Sipura, M. & Tahvanainen, J. Shading enhances the quality of willow leaves to leaf beetles: but does it matter?. Oikos 91, 550–558 (2000).
Article  Google Scholar 

7.
Henriksson, J. et al. Effects of host shading on consumption and growth of the geometrid Epirrita autumnata: interactive roles of water, primary and secondary compounds. Oikos 103, 3–16 (2003).
CAS  Article  Google Scholar 

8.
Diaz, R. et al. Differential performance of Tropical Soda Apple and its biological control agent Gratiana boliviana (Coleoptera: Chrysomelidae) in open and shaded habitats. Environ. Entomol. 40, 1437–1447 (2011).
PubMed  Article  PubMed Central  Google Scholar 

9.
Uyi, O. O., Uwagiahanor, B. I. & Ejomah, A. J. The nocturnal larvae of a specialist folivore prefer Chromolaena odorata (L.) foliage from a sunny environment, but does it matter?. Arthropod Plant Interact. 11, 603–611 (2017).
Article  Google Scholar 

10.
Moran, P. J. & Showler, A. T. Plant responses to water deficit and shade stresses in pigweed and their influence on feeding and oviposition by the beet armyworm (Lepidoptera: Noctuidae). Environ. Entomol. 34, 929–937 (2005).
Article  Google Scholar 

11.
Uyi, O. O., Zachariades, C., Hill, M. P. & Conlong, D. The nocturnal larvae of a specialist folivore perform better on Chromolaena odorata leaves from a shaded environment. Entomol. Exp. Appl. 156, 187–199 (2015).
Article  Google Scholar 

12.
Bryant, J. P., Chapin, F. S. III. & Klein, D. R. Carbon: nutrient balance of boreal plants in relation to vertebrate herbivory. Oikos 40, 357–368 (1983).
CAS  Article  Google Scholar 

13.
Bryant, J. P., Chapin, F. S. III., Reichardt, P. B. & Clausen, T. P. Response of winter chemical defence in Alaska paper birch and green alder to manipulation of plant carbon: nutrient balance. Oecologia 72, 510–514 (1987).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Herms, D. A. & Mattson, W. J. The dilemma of plants: to grow or defend. Q. Rev. Biol. 67, 283–335 (1992).
Article  Google Scholar 

15.
Barber, N. A. & Marquis, R. J. Light environment and impact of foliage quality on herbivorous insect attack and bird predation. Oecologia 166, 401–409 (2011).
ADS  PubMed  Article  PubMed Central  Google Scholar 

16.
Barbehenn, R. V., Chen, Z., Karowe, D. N. & Spickard, A. C3 grasses have higher nutritional quality than C4 grasses under ambient and elevated atmospheric CO2. Glob. Change Biol. 10, 1565–1575 (2004).
ADS  Article  Google Scholar 

17.
Barbehenn, R. V., Karowe, D. N. & Spickard, A. Effects of elevated atmospheric CO2 on the nutritional ecology of C3 and C4 grass-feeding caterpillars. Oecologia 140, 86–95 (2004).
ADS  PubMed  Article  PubMed Central  Google Scholar 

18.
Caswell, H., Reed, F. C., Stephenson, S. N. & Werner, P. Photosynthetic pathways and selective herbivory: a hypothesis. Am. Nat. 107, 465–480 (1973).
CAS  Article  Google Scholar 

19.
Nokelainen, O., van Ripley, B. S., Bergen, E., Osborne, C. P. & Brakefield, P. M. Preference for C4 shade grasses increases hatchling performance in the butterfly, Bicyclus safitza. Ecol. Evol. 6, 5246–5255 (2016).
PubMed  PubMed Central  Article  Google Scholar 

20.
Uyi, O. O. & Igbinosa, I. B. The status of Chromolaena odorata and its biocontrol in West Africa,in Zachariades, C., Strathie, L.W., Day, M.D. & Muniappan, R. (eds). Proceedings of the Eighth International Workshop on Biological Control and Management of Chromolaena odorataand other Eupatorieae, Nairobi, Kenya, 1–2 November 2010. Agricultural Research Council—Plant Protection Research Institute, Pretoria, South Africa, pp. 86–98 (2013).

21.
Uyi, O. O. et al. Chromolaena odorata invasion in Nigeria: a case for coordinated biological control. Manag. Biol. Invasions 5, 377–393 (2014).
Article  Google Scholar 

22.
Zachariades, C., Day, M., Muniappan, R. & Reddy, G. V. P. Chromolaena odorata (L.) King and Robinson (Asteraceae). In Biological Control of Tropical Weeds Using Arthropods (eds Muniappan, R. et al.) 130–160 (Cambridge University Press, Cambridge, 2009).
Google Scholar 

23.
Zhang, L. L. & Wen, D. Z. Structural and physiological responses of two invasive weeds, Mikania micrantha and Chromolaena odorata to contrasting light and soil water conditions. J. Plant. Res. 122, 69–79 (2009).
PubMed  Article  PubMed Central  Google Scholar 

24.
Muniappan, R., Sundaramurthy, V. T. & Virktamath, C. A. Distribution of Chromolaena odorata (Asteraceae) and bionomics and utilization of food by Pareuchaetes pseudoinsulata (Lepidoptera: Arctiidae) in India, in: Delfosse, E. S. (ed) Proceedings of the Seventh International Symposium on Biological Control of Weeds, 6–11 March, 1988, Rome, Italy, pp 401–409 (1989).

25.
Cruttwell, R. E. The insects of Eupatorium odoratum L. in Trinidad and their potential as agents for biological control. PhD Dissertation, University of the West Indies, Trinidad (1972).

26.
Cock, M. W. J. & Holloway, J. D. The history of, and prospects for, the biological control of Chromolaena odorata (Compositae) by Pareuchaetes pseudoinsulata Rego Barros and allies (Lepidoptera, Arctiidae). Bull. Entomol. Res. 72, 193–205 (1982).
Article  Google Scholar 

27.
Uyi, O., Egbon, I. N. & Igbinosa, I. B. Discovery of, and studies on Pareuchaetes pseudoinsulata (Lepidoptera: Arctiidae) in southern Nigeria. Int. J. Trop. Insect Sci. 31, 199–203 (2011).
Article  Google Scholar 

28.
Seibert, T. F. Biological control of the weed, Chromolaena odorata (Asteraceae) by Pareuchaetes pseudoinsulata (Lepidoptera: Arctiidae) in Guam and the Northern Mariana Islands. Entomophaga 35, 531–539 (1989).
Article  Google Scholar 

29.
Braimah, H., Ekyem, S.O., Issah, U.S. & Mochiah, M. Social perceptions and ecological impacts following biological control of Chromolaena odorataby Pareuchaetes pseudoinsulatain the forest region of Ghana, in: Zachariades, C., Strathie, L. W., Day, M. D. & Muniappan, R. (eds) Proceedings of the Eighth International Workshop on Biological Control and Management of Chromolaena odorataand other Eupatorieae, Nairobi, Kenya, 1–2 November 2010.Agricultural Research Council—Plant Protection Research Institute, Pretoria, South Africa pp. 110–116 (2013).

30.
Olckers, T. & Hulley, P. E. Host specificity tests on leaf-feeding insects: aberrations from the use of excised leaves. African Entomol. 2, 68–70 (1994).
Google Scholar 

31.
Blossey, B. & Nötzold, R. Evolution of increased competitive ability in invasive nonindigenous plants: a hypothesis. J. Ecol. 83, 887–889 (1995).
Article  Google Scholar 

32.
Friberg, M. & Wiklund, C. Butterflies and plants: preference/performance studies in relation to plant size and the use of intact plants vs. cuttings. Entomol. Exp. Appl. 160, 201–208 (2016).
CAS  Article  Google Scholar 

33.
Uyi, O. O., Hill, M. P. & Zachariades, C. Variation in host plant has no effect on the performance and fitness-related traits of the specialist herbivore, Pareuchaetes insulata. Entomol. Exp. Appl. 153, 64–75 (2014).
Article  Google Scholar 

34.
Bakr, E. M. A new software for measuring leaf area, and area damaged by Tetranychus urticae Koch. J. Appl. Entomol. 129, 173–175 (2005).
Article  Google Scholar 

35.
implications for insect defoliation. Steinbauer, M.J. Specific leaf weight as an indicator of juvenile leaf toughness in Tasmanian bluegum (Eucalyptus globulus ssp. globulus). Austral. For. 64, 32–37 (2001).
Article  Google Scholar 

36.
Watanabe, F. S. & Olsen, S. R. Test of an ascorbic acid method for determining phosphorus in water and NaHCO3 extracts from soil. Soil Sci. Soc. Am. Proc. 29, 677–678 (1965).
ADS  CAS  Article  Google Scholar 

37.
Marais, J. P. Evaluation of acid hydrolysis procedures for the rapid determination of total non-structural carbohydrates in plant species. Agrochemophysica 11, 1–3 (1979).
CAS  Google Scholar 

38.
Lockett, C. J., Dhileepan, K., Robinson, M. & Pukallus, K. J. Impact of a biological control agent, Chiasmia assimilis, on prickly acacia (Acacia nilotica ssp. indica) seedlings. Biol. Control 62, 183–188 (2012).
Article  Google Scholar 

39.
Mäntylä, E., Klemola, T., Sirkiä, P. & Laaksonen, T. Low light reflectance may explain the attraction of birds to defoliated trees. Behav. Ecol. 19, 325–330 (2008).
Article  Google Scholar 

40.
Uyi, O. O., Zachariades, C. & Hill, M. P. The life history traits of the arctiine moth Pareuchaetes insulata, a biological control agent of Chromolaena odorata in South Africa. Afr. Entomol. 22, 611–624 (2014).
Article  Google Scholar 

41.
Awmack, C. S. & Leather, S. R. Host plant quality and fecundity in herbivorous insects. Annu. Rev. Entomol. 47, 817–844 (2002).
CAS  PubMed  Article  PubMed Central  Google Scholar 

42.
Łukowski, A., Giertych, M. J., Walczak, U., Baraniak, E. & Karolewski, P. Light conditions affect the performance of Yponomeuta evonymellus on its native host Prunus padus and the alien Prunus serotina. Bull. Entomol. Res. 107, 208–216 (2017).
PubMed  Article  PubMed Central  Google Scholar 

43.
Behmer, S. T. Insect herbivore nutrient regulation. Annu. Rev. Entomol. 54, 165–187 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

44.
Simpson, S. J. & Raubenheimer, D. The Nature of Nutrition: A Unifying Framework from Animal Adaptation to Human Obesity (Princeton University Press, Princeton, 2012).
Google Scholar 

45.
Lee, K. P., Cory, J. S., Wilson, K., Raubenheimer, D. & Simpson, S. J. Flexible diet choice offsets protein costs of pathogen resistance in a caterpillar. Proc. R. Soc. Lond. B 273, 823–829 (2006).
CAS  Google Scholar 

46.
Clissold, F. J. The biomechanics of chewing and plant fracture: mechanisms and implications. Adv. Insect Physiol. 34, 317–372 (2007).
Article  Google Scholar 

47.
Raubenheimer, A. D., Lee, K. P. & Simpson, S. J. Does Bertrand’s rule apply to macronutrients?. Proc. R. Soc. B 272, 2429–2434 (2005).
CAS  PubMed  Article  PubMed Central  Google Scholar 

48.
Boersma, M. & Elser, J. J. Too much of a good thing: on stoichiometrically balanced diets and maximal growth. Ecology 87, 1325–1330 (2006).
PubMed  Article  PubMed Central  Google Scholar 

49.
Zehnder, C. & Hunter, M. D. More is not necessarily better: the impact of limiting and excessive nutrients on herbivore population growth rates. Ecol. Entomol. 34, 535–543 (2009).
Article  Google Scholar 

50.
Lee, K. P., Behmer, S. T., Simpson, S. J. & Raubenheimer, D. A geometric analysis of nutrient regulation in the generalist caterpillar, Spodoptera littoralis (Boisduval). J. Insect Physiol. 48, 655–665 (2002).
CAS  PubMed  Article  PubMed Central  Google Scholar 

51.
Clissold, F. J., Sanson, G. D. & Read, J. The paradoxical effects of nutrient ratios and supply rates on an outbreaking insect herbivore, the Australian plague locust. J. Anim. Ecol. 75, 1000–1013 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar  More

1.
Stige, L. C., Chan, K.-S., Zhang, Z., Frank, D. & Stenseth, N. C. Thousand-year-long Chinese time series reveals climatic forcing of decadal locust dynamics. Proc. Natl. Acad. Sci. 104, 16188–16193 (2007).
ADS  CAS  PubMed  Article  Google Scholar 
2.
Walker, F. Catalogue of the Specimens of Dermaptera Saltatoria in Collection of the British Museum. Part III. 485–594 (British Museum (Natural History), 1870).

3.
Wright, D. E. Analysis of the development of major plagues of the Australian plague locust Chortoicetes terminifera (Walker) using a simulation model. Aust. J. Ecol. 12, 423–437 (1987).
Article  Google Scholar 

4.
Deveson, E. D. & Walker, P. W. Not a one-way trip: Historical distribution data for Australian plague locusts support frequent seasonal exchange migrations. J. Orthoptera Res. 14, 91–105 (2005).
Article  Google Scholar 

5.
Wang, H. Quantitative assessment of Australian plague locust habitats in the inland of eastern Australia using RS and GIS technologies in Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI vol. 9239 92390D (International Society for Optics and Photonics, 2014).

6.
Chapuis, M.-P. et al. Challenges to assessing connectivity between massive populations of the Australian plague locust. Proc. R. Soc. B Biol. Sci. 278, 3152–3160 (2011).
Article  Google Scholar 

7.
Murray, D. A. H., Clarke, M. B. & Ronning, D. A. Estimating invertebrate pest losses in six major Australian grain crops. Aust. J. Entomol. 52, 227–241 (2013).
Article  Google Scholar 

8.
Zhang, L., Lecoq, M., Latchininsky, A. & Hunter, D. Locust and grasshopper management. Annu. Rev. Entomol. 64, 15–34 (2019).
CAS  PubMed  Article  Google Scholar 

9.
Adriaansen, C., Woodman, J., Deveson, E. & Drake, V. The Australian Plague Locust: risk and response. Environ. Hazards Risks Disasters Biol https://doi.org/10.1016/B978-0-12-394847-2.00005-X (2016).
Article  Google Scholar 

10.
Farrow, R. A. & Longstaff, B. C. Comparison of the annual rates of increase of locusts in relation to the incidence of plagues. Oikos 2, 207–222 (1986).
Article  Google Scholar 

11.
Wardhaugh, K. G. The effects of temperature and moisture on the inception of diapause in eggs of the Australian plague locust, Chortoicetes terminifera Walker (Orthoptera: Acrididae). Aust. J. Ecol. 5, 187–191 (1980).
Article  Google Scholar 

12.
Wardhaugh, K. G. Diapause strategies in the Australian plague locust (Chortoicetes terminifera Walker). In The evolution of insect life cycles 89–104 (Springer, Berlin, 1986).
Google Scholar 

13.
Clark, D. P. Flights after sunset by the Australian plague locust, Chortoicetes terminifera (Walker) and their significance in dispersal and migration. Aust. J. Zool. 19, 159–176 (1971).
Article  Google Scholar 

14.
Farrow, R. A. Origin and decline of the 1973 plague locust outbreak in central western New South Wales. Aust. J. Zool. 25, 455–489 (1977).
Article  Google Scholar 

15.
Wang, B. et al. Future climate change likely to reduce the Australian plague locust (Chortoicetes terminifera) seasonal outbreaks. Sci. Total Environ. 668, 947–957 (2019).
ADS  CAS  PubMed  Article  Google Scholar 

16.
Veran, S. et al. Modeling spatiotemporal dynamics of outbreaking species: influence of environment and migration in a locust. Ecology 96, 737–748 (2015).
PubMed  Article  Google Scholar 

17.
Maywald, G., Kriticos, D., Sutherst, R. & Bottomley, W. DYMEX model builder version 3: user’s guide. (2007).

18.
Meynard, C. N. et al. Climate-driven geographic distribution of the desert locust during recession periods: Subspecies’ niche differentiation and relative risks under scenarios of climate change. Glob. Change Biol. 23, 4739–4749 (2017).
ADS  Article  Google Scholar 

19.
Piou, C. et al. Coupling historical prospection data and a remotely-sensed vegetation index for the preventative control of Desert locusts. Basic Appl. Ecol. 14, 593–604 (2013).
Article  Google Scholar 

20.
Tratalos, J. A., Cheke, R. A., Healey, R. G. & Stenseth, N. C. Desert locust populations, rainfall and climate change: Insights from phenomenological models using gridded monthly data. Clim. Res. 43, 229–239 (2010).
Article  Google Scholar 

21.
Tian, H. et al. Reconstruction of a 1,910-y-long locust series reveals consistent associations with climate fluctuations in China. Proc. Natl. Acad. Sci. 108, 14521–14526 (2011).
ADS  CAS  PubMed  Article  Google Scholar 

22.
Ehrlén, J. & Morris, W. F. Predicting changes in the distribution and abundance of species under environmental change. Ecol. Lett. 18, 303–314 (2015).
PubMed  PubMed Central  Article  Google Scholar 

23.
Croft, S., Chauvenet, A. L. & Smith, G. C. A systematic approach to estimate the distribution and total abundance of British mammals. PLoS ONE 12, e0176339 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

24.
Woodman, J. D. High-temperature survival is limited by food availability in first-instar locust nymphs. Aust. J. Zool. 58, 323–330 (2011).
Article  Google Scholar 

25.
Guisan, A., Edwards, T. C. & Hastie, T. Generalized linear and generalized additive models in studies of species distributions: Setting the scene. Ecol. Model. 157, 89–100 (2002).
Article  Google Scholar 

26.
Yee, T. W. & Mitchell, N. D. Generalized additive models in plant ecology. J. Veg. Sci. 2, 587–602 (1991).
Article  Google Scholar 

27.
Bučas, M. et al. Empirical modelling of benthic species distribution, abundance, and diversity in the Baltic Sea: Evaluating the scope for predictive mapping using different modelling approaches. ICES J. Mar. Sci. 70, 1233–1243 (2013).
Article  Google Scholar 

28.
Heersink, D. K. et al. Statistical modeling of a larval mosquito population distribution and abundance in residential Brisbane. J. Pest Sci. 89, 267–279 (2016).
Article  Google Scholar 

29.
Jeffrey, S. J., Carter, J. O., Moodie, K. B. & Beswick, A. R. Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environ. Model. Softw. 16, 309–330 (2001).
Article  Google Scholar 

30.
Tozer, C. R., Kiem, A. S. & Verdon-Kidd, D. C. On the uncertainties associated with using gridded rainfall data as a proxy for observed. Hydrol. Earth Syst. Sci. 16, 1481–1499 (2012).
ADS  Article  Google Scholar 

31.
Gregg, P. Development of the Australian Plague Locust, Chortoicetes terminifera, in relation to weather I. Effects of constant temperature and humidity. Aust. J. Entomol. 22, 247–251 (1983).
Article  Google Scholar 

32.
Pruess, K. P. Day-degree methods for pest management. Environ. Entomol. 12, 613–619 (1983).
Article  Google Scholar 

33.
McVicar, T. R., Briggs, P. R., King, E. A. & Raupach, M. R. A review of predictive modelling from a natural resource management perspective: the role of remote sensing of the terrestrial environment (CSIRO Land and Water CSIRO Earth Observation Centre, Canberra, 2003).
Google Scholar 

34.
Grundy, M. J. et al. Soil and landscape grid of Australia. Soil Res. 53, 835–844 (2015).
Article  Google Scholar 

35.
Cressie, N. & Wikle, C. K. Statistics for spatio-temporal data (John Wiley & Sons, New York, 2015).
Google Scholar 

36.
James, G., Witten, D., Hastie, T. & Tibshirani, R. An introduction to statistical learning (Springer, Berlin, 2013).
Google Scholar 

37.
Nelder, J. A. & Wedderburn, R. W. Generalized linear models. J. R. Stat. Soc. Ser. Gen. 135, 370–384 (1972).
Article  Google Scholar 

38.
Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).
PubMed  PubMed Central  Article  Google Scholar 

39.
Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S (Springer-Verlag, Berlin, 2002). https://doi.org/10.1007/978-0-387-21706-2.
Google Scholar 

40.
Wood, S. N., Goude, Y. & Shaw, S. Generalized additive models for large data sets. J. R. Stat. Soc. Ser. C Appl. Stat. 64, 139–155 (2015).
MathSciNet  Article  Google Scholar 

41.
Clark, D. P. The influence of rainfall on the densities of adult Chortoicetes terminifera (Walker) in central western New South Wales, 1965–73. Aust. J. Zool. 22, 365–386 (1974).
Article  Google Scholar 

42.
Shelford, V. E. The ecology of North America. Ecol. N. Am. 2, 2 (1963).
Google Scholar 

43.
Deveson, E. D. Satellite normalized difference vegetation index data used in managing Australian plague locusts. J. Appl. Remote Sens. 7, 075096 (2013).
ADS  Article  Google Scholar 

44.
Kuhnert, P. M. & Lucchesi, L. Vizumap: An R package for visualizing uncertainty in spatial data (Zenodo, Boca Raton, 2018). https://doi.org/10.5281/zenodo.1479951.
Google Scholar 

45.
Lucchesi, L. R. & Wikle, C. K. Visualizing uncertainty in areal data with bivariate choropleth maps, map pixelation and glyph rotation. Stat 6, 292–302 (2017).
MathSciNet  Article  Google Scholar 

46.
Benfekih, L., Chara, B. & Doumandji-Mitiche, B. Influence of anthropogenic impact on the habitats and swarming risks of Dociostaurus maroccanus and Locusta migratoria (Orthoptera, Acrididae) in the Algerian Sahara and the semi-arid zone. J. Orthoptera Res. 11, 243–250 (2002).
Article  Google Scholar 

47.
Štrumbelj, E. & Kononenko, I. Explaining prediction models and individual predictions with feature contributions. Knowl. Inf. Syst. 41, 647–665 (2014).
Article  Google Scholar 

48.
Escorihuela, M. J. et al. SMOS based high resolution soil moisture estimates for desert locust preventive management. Remote Sens. Appl. Soc. Environ. 11, 140–150 (2018).
Google Scholar 

49.
Myneni, R. B. & Williams, D. L. On the relationship between FAPAR and NDVI. Remote Sens. Environ. 49, 200–211 (1994).
ADS  Article  Google Scholar 

50.
Hu, G. et al. Long-term seasonal forecasting of a major migrant insect pest: the brown planthopper in the Lower Yangtze River Valley. J. Pest Sci. 92, 417–428 (2019).
Article  Google Scholar  More

ALSD is widespread in Majorca
The summer of 2017, following the Xf official detection in October 2016, provided a good scenario for testing whether Xf was implicated in the almond decline. An episode of high water demand (three consecutive days of Tmax  > 33 °C and RH  More

Ethics statement
All animal experiments were performed at the Max F. Perutz Laboratories of the University of Vienna, Austria. All experiments were discussed and approved by the University of Veterinary Medicine, Vienna, Austria, and conducted in accordance with protocols approved by the Federal Ministry for Education, Science and Research of the Republic of Austria under the license number BMWF-66.006/0001-WF/V/3b/2016. Animals were randomized for interventions but researchers processing the samples and analyzing the data were aware which intervention group corresponded to which cohort of animals.
Mouse colon incubations
Three adult (6-8 weeks old) C57BL/6N mice bred at the Max F. Perutz Laboratories, University of Vienna, under SPF conditions were sacrificed per experiment, and their colon was harvested anaerobically (85% N2, 10% CO2, 5% H2) in an anaerobic tent (Coy Laboratory Products, USA). Contents from each colon were suspended in 7.8 mL of 50% D2O-containing PBS and homogenized by vortexing. Similar conditions have been successfully applied in the past to monitor activity of individual cells in gut communities without causing major changes in the activity of individual community members34. The homogenate was left to settle for 10 min, and the supernatant was then distributed into glass vials and supplemented with different concentrations of mucosal sugar monosaccharides, glucose, mucin or nothing (no-amendment control) (all amendment chemicals were from Sigma-Aldrich, except D(+)-galactose which was purchased from Carl Roth GmbH) (Fig. 1a,c). After incubation for 6 h at 37 °C, glycerol was added (to achieve a final concentration of 20% (v/v) of glycerol in the microcosms) and the vials were crimp-sealed with rubber stoppers and stored at −80 °C until further processing. Prior to glycerol addition, subsamples of the biomass were collected, pelleted and supernatants stored at −80 °C for HILIC LC-MS/MS measurements. Pellets were washed with PBS to remove D2O and were fixed in 3% formaldehyde for 2 h at 4 °C and stored in 50% PBS/50% ethanol solution at −20 °C until further use. A total of three biological replicates were established using starting material pooled from three animals each (experiments MonoA, MonoB and MonoC). For the MonoA and MonoB experiments, microcosms were established for all the different concentrations of monosaccharides tested (Fig. 1c), while for MonoC only the highest concentrations of monosaccharides tested in incubations MonoA and MonoB were supplemented. Note that analysis of mucin-amended sorted fractions has been published elsewhere35. Since mucin contains all the monosaccharides included in this study, it constitutes an important control, and therefore we processed the sequencing data from mucin sorts in parallel with our samples and included it in our analyses (Fig. 2).
Mass spectrometric analysis of mucosal monossaccharides
Hydrophilic interaction chromatography (HILIC) LC-MS/MS was used for the measurement of mucosal monosaccharides in microcosm supernatants. Frozen samples were thawed at room temperature and centrifuged for 10 min at 18.000 × g and 4 °C. Supernatants were then diluted 1:50 with acetonitrile:water (1:1; v/v) and a volume of 3 µl was injected onto the chromatographic column. The UHPLC system (UltiMate 3000, Thermo Scientific) was coupled to a triple quadrupole mass spectrometer (TSQ Vantage, Thermo Scientific) by an electrospray ionization interface. Hydrophilic interaction chromatographic separation was realized on a Luna aminopropyl column (3 µm, 150 × 2 mm; Phenomenex, Torrance, CA) at a flow rate of 0.25 ml/min. Eluent A consisted of 95% water and 5% acetonitrile with 20 mM ammonium acetate and 40 mM ammonium hydroxide as additives and eluent B of 95% acetonitrile and 5% water. A multi-step gradient was optimized as follows: 100% B until minute 2, then linearly decreased to 80% B until minute 20 and further to 0% B until minute 25. The column was kept at 0% B for 4 min before it was equilibrated for 5 min at the initial conditions. The column temperature was maintained at 40 °C. The mass spectrometer was operated in multiple reaction monitoring (MRM) mode. Electrospray ionization (ESI) was optimized as follows: spray voltage 2800 V (positive mode) and 3000 V (negative mode); vaporizer temperature 250 °C; sheath gas pressure 30 Arb; ion sweep gas pressure 2 Arb; auxiliary gas pressure 10 Arb; capillary gas temperature 260 °C. Mass spectrometric parameters were optimized by direct injection and are reported together with the retention times of individual sugars in Supplementary Table 6. Spiking experiments and regular quality control checks were conducted to evaluate and ensure the systems’ proper performance.
Confocal Raman microspectroscopy and spectral processing of fixed samples
Formaldehyde-fixed samples were spotted on aluminum-coated slides (Al136; EMF Corporation) and washed by dipping into ice-cold Milli-Q (MQ) water (Millipore) to remove traces of buffer components. Individual cells were observed under a 100×/0.75 NA microscope objective. Single microbial cell spectra were acquired using a LabRAM HR800 confocal Raman microscope (Horiba Jobin-Yvon) equipped with a 532-nm neodymium-yttrium aluminum garnet (Nd:YAG) laser and either 300 grooves/mm diffraction grating. Spectra were acquired in the range of 400–3200 cm−1 for 30 s with 2.18 mW laser power. Raman spectra were background-corrected using the sensitive nonlinear iterative peak algorithm, and afterwards normalized to the sum of its absolute spectral intensity34. For quantification of the degree of D substitution in CH bonds (%CD), the bands assigned to C–D (2040–2,300 cm−1) and C–H (2,800–3,100 cm−1) were calculated using integration of the specified region34.
Raman-activated cell sorting
For RACS of D-labeled cells, 100 μl of glycerol-preserved microcosms containing non-fixed cells were pelleted, washed once with MQ water containing 0.3 M glycerol and finally resuspended in 0.5 ml of 0.3 M glycerol in MQ water. Cell sorting was performed in a fully automated manner using a Raman microspectroscope (LabRAM HR800, Horiba Scientific, France) combined with optical tweezers and a polydimethylsiloxane (PDMS) microfluidic sorter. The optical tweezers (1,064 nm Nd:YAG laser at 500 mW) and Raman (532 nm Nd:YAG laser at 45 mW or 80 mW; see below) laser were focused at the same position of the interface between the sample and sheath streams using a single objective (63x, 1.2 NA water-immersion, Zeiss). The in-house software based on the graphical user interface (GUI; written in MATLAB) detected the single-cell capture and its deuterium labeling status by calculating the cell index (PC = I1,620-1,670/Ifluid,1,620-1,670; where I is the integrated intensity between the indicated wavenumbers) and the labeling index (PL = I2040-2300/I1850-1900), respectively. We did not detect a significant change in the C–D peak region (2040–2300 cm−1) due the presence of 0.3 M of glycerol in the sorting fluid (added to minimize the osmotic stress when the sample was re-suspended for the RACS) (Supplementary Fig. 2a). Other spectral regions (e.g., 2700 cm−1) were slightly affected, but the sorting algorithm employed and the parameters described above take these small changes into account: the cell index PC (I1620–1670/Ifluid,1620–1670) used to detect single-cell capture was calculated by comparing the Raman intensity of cells measured in real-time to that of the working fluid measured in the calibration (conducted before the actual sorting). The threshold value for PL (I2040–2300/I1850–1900) was chosen based on the measurement of the control sample (i.e., sample incubated in non-D2O-containing medium). We used two software versions, each of which uses 45 mW (version 1) and 80 mW (version 2) Raman laser powers, respectively. The second version operates with higher power based on the addition of a laser shutter that blocks the Raman laser while the cells are being translocated, reducing the laser-induced damage on the cell. This version allows shorter acquisition times to be employed, and therefore higher throughput of the platform. The laser power for each version was chosen based on visual inspection of captured cells as described35. For the NeuAc and GlcNAc-amendment sorts (version 1, since version 2 was not yet available), PC value was calculated from cell spectra acquired for 2 s at the “capture location”, while the PL value was calculated from spectra obtained with a 5 s exposure time at the “evaluation location”. Fucose, GalNAc, and galactose-supplemented sorts were performed with version 2 of the platform, which in the meantime became available, significantly reducing sorting times. For these sorts both PC and PL values were simultaneously measured at the “capture location” with a 0.3 s exposure time. Only the D-labeled cells were translocated to the ‘evaluation location’ and immediately released. In order to determine the threshold PL above which a cell from the microcosms should be considered D-labeled (and therefore selected and sorted), cells from glucose-supplemented microcosms incubated in the absence or presence of D (0% versus 50% D2O in the microcosms) were run on the platform prior to sorting. The threshold PL number can vary across microcosms due to different microbial compositions and/or physiological status of cells present in the starting material, as well as due to different laser powers employed. Therefore we determined the PL threshold separately for both MonoA and MonoB incubations using both 45 and 80 mW laser power. Nevertheless, we reached a PL threshold of 6.19 for all sets of conditions and incubations tested (Supplementary Fig. 2b). We speculate this was due to the identical conditions used in both incubations and the fact that both communities have a similar microbial composition (Fig. 1e). To test the sorting accuracy of the platform on our samples, the negative control (H2O, glucose-supplemented microcosm) was re-run in the platform and sorted using the adopted threshold (PL = 6.19) (Supplementary Table 1). As expected, no cells were considered labeled by the platform under these conditions. Sorted fractions were nevertheless collected and sequenced as controls.
Preparation of 16S rRNA gene amplicon libraries and 16S rRNA gene sequence analyses
DNA extracted from the mouse colon microcosms or from mouse fecal pellets using a phenol-chloroform bead-beating protocol52 was used as a template for PCR. PCR amplification was performed with a two-step barcoding approach53. In the first-step PCR, the 16S rRNA gene of most bacteria was targeted using oligonucleotide primers (Supplementary Table 7) containing head adaptors (5′-GCTATGCGCGAGCTGC-3′) in order to be barcoded in a second step PCR53. Barcode primers consisted of the 16 bp head sequence and a sample-specific 8 bp barcode from a previously published list at the 5′ end. The barcoded amplicons were purified with the ZR-96 DNA Clean-up Kit (Zymo Research, USA) and quantified using the Quant-iT PicoGreen dsDNA Assay (Invitrogen, USA). An equimolar library was constructed by pooling samples, and the resulting library was sent for sequencing on an Illumina MiSeq platform at Microsynth AG (Balgach, Switzerland). Paired-end reads were quality-filtered and processed using QIIME 153,54. Reads were then clustered into operational taxonomic units (OTUs) of 97% sequence identity and screened for chimeras using UPARSE implemented in USEARCH v8.1.186155. OTUs were classified using the RDPclassifier v2.1256 as implemented in Mothur v1.39.557 using the Silva database v13258. Sequencing libraries were rarefied and analyzed using the vegan package (v2.4-3) of the software R (https://www.r-project.org/, R 3.4.0).
Sequencing of mouse gut isolates
Bacteroides sp. Isolate FP24 was isolated from YCFA agar plates (DSMZ medium 1611- YCFA MEDIUM (modified)) by plating ten-fold dilution series of a microcosms supplemented with 2 mg/ml of NeuAc (experiment MonoB)). Escherichia sp. isolate FP11 and Anaerotruncus sp. isolate FP23 were isolated from C. difficile minimal medium17 agar plates supplemented with 0.25% NeuAc or 0.25% GlcNAc by plating ten-fold dilution series of a microcosms supplemented with 2 mg/ml of NeuAc (experiment MonoB) or of a microcosms supplemented with 5 mg/ml of GlcNAc (experiment MonoA), respectively. Colonies were re-streak on the same medium plates until complete purity. Pure colonies were grown overnight in 10 mL of BHI medium (Brain heart infusion at 37 g per liter of medium) supplemented with: yeast extract, 5 g; Na2CO3, 42 mg; cysteine, 50 mg; vitamin K1, 1 mg; hemin, 10 µg. DNA was extracted from pelleted biomass using the QIAGEN DNAeasy Tissue and Blood kit (Qiagen, Austin, TX, USA) according to the manufacturer´s instructions. Sequencing libraries were prepared using the NEBNext® Ultra™ II FS DNA kit (Illumina) and sequenced in an Illumina MiSeq platform with 300-bp paired-end sequencing chemistry (Joint Microbiome Facility, University of Vienna and Medical University of Vienna, Austria). Reads were quality trimmed with the bbduk option of BBmap (v 34.00) at phrad score 15. Quality-trimmed reads were assembled with SPAdes (v 3.11.1)59. For isolate FP11, assembled reads were subsequently, iteratively (n = 6) reassembled with SPAdes using contigs of >1 kb from the previous assembly as “trusted contigs” for input and iterating kmers from 11 to 121 in steps of 10. CheckM (v1.0.6) assessment60 of these genomes is summarized in Supplementary Data 1.
Mini-metagenome sequencing and genomic analyses
Labeled RACS cells were collected into PCR tubes, lysed and subjected to whole-genome amplification using the Repli-g Single Cell Kit (QIAGEN), according to the manufacturer’s instructions. Shotgun libraries were generated using the amplified DNA from WGA reactions (sorted fractions) or DNA isolated using the phenol-chloroform method (initial microcosms) as a template and Nextera XT (Illumina) reagents. Libraries were sequenced with a HiSeq 3000 (Illumina) in 2 × 150 bp mode at the Biomedical Sequencing Facility, Medical University of Vienna, Austria. The sequence reads were quality trimmed and filtered using BBMap v34.00 (https://sourceforge.net/projects/bbmap/). The remaining reads were assembled de novo using SPAdes 3.11.159 in single-cell mode (k-mer sizes: 21, 35, 55). Binning of the assembled reads into metagenome-assembled genomes (MAGs) was performed with MetaBAT 2 (v2.12.1)61 using the following parameters: minContig 2000, minCV 1.0, minCVSum 1.0, maxP 95%, minS 60, and maxEdges 200. The quality and contamination of all MAGs were checked with CheckM 1.0.660 (Supplementary Data 1). MAGs >200 kb obtained from all samples were compared and de-replicated using dRep 1.4.362. Automatic genome annotation of contigs >2 kb within each de-replicated MAG was performed with RAST 2.063. Taxonomic classification of each MAG was obtained using GTDB-Tk64 (v0.1.3, gtdb.ecogenomic.org/).
The relative abundance of each MAG on the initial microcosms was calculated based on metagenomic coverage. Filtered reads from each sequenced microcosm were mapped competitively against all retrieved MAGs using BBMap (https://sourceforge.net/projects/bbmap/). Read coverage was normalized by genome size and relative abundances of each genome in each sample were calculated based on the formula: covA = (bpA/gA)/(bpT/gT), where covA is the relative abundance of MAG A on a particular sample, bpA is the number of base pairs from reads matching MAG A, gA is the genome length of MAG A, bpT is the total number of base pairs from reads matching all MAGs recovered from that particular sample and gT is the sum of all MAGs genome lengths.
For determination of the presence of encoded enzymes for catabolism of mucin monosaccharides among MAGs, predicted protein sequences from recovered MAGs were subject to local BLASTP analyses65, against a custom database. The database was composed of all enzymes involved initial hydrolysis and catabolism of mucosal sugar monosaccharides (Supplementary Data 2), which were previously curated from a total of 395 human gut bacteria15. A strict e-value threshold of 10−50 was used for all BLASTP analyses. During initial setup of the analysis pipeline, functional assignments of proteins that gave positive BLASTP hits were manually verified by examining annotations from RAST 2.0 and by performing BLASTP analyses against the NCBI-nr database (NCBIBlast 2.2.26).
To verify the enrichment of a selected dataset of mucin-degrading enzymes35 in the assemblies derived from sorted fractions, BLASTX analyses of scaffolds from each fraction as well as from the initial microcosms metagenomes (unsorted) were performed against the selected mucin-degrading enzyme sequences15 (Supplementary Table 3). An e-value threshold of 10−50 was also used for all BLASTX analyses.
Phylogenomic analyses
A concatenated marker alignment of 34 single-copy genes was generated for all MAGs using CheckM 1.0.660 and the resulting alignment was used to calculate a tree with the approximate maximum-likelihood algorithm of FastTree 2.1.1066. Phylogenomic trees were visualized and formatted using iTOL v4 (https://itol.embl.de/). In order to identify the closest relative for each MAG, the query MAG and close reference genomes (based on the generated phylogenomic tree) were compared using dRep 1.4.362. Compared genomes with a whole-genome-based average nucleotide identity (ANIm) >99%39 were considered to be the same population genome.
High-resolution mass spectrometric analyses
Glycerol-preserved biomass (150 μL) from microcosm incubations was pelleted and suspended in 50 μL of lysis buffer (1% sodium dodecyl sulfate (SDS), 10 mM TRIS base, pH 7.5). Protein lysates were subjected to SDS polyacrylamide gel electrophoresis followed by in-gel tryptic digestion. Proteins were stained with colloidal Coomassie Brilliant Blue G-250 (Roth, Kassel, Germany) and detained with Aqua dest. Whole protein bands were cut into gel pieces and in-gel-digestion with trypsin 30 µL (0.005 µg/µL) was performed overnight. Extracted peptides where dried and resolved in 0.1% formic acid and purified by ZipTip® treatment (EMD Millipore, Billerica, MA, USA).
In total, 5 µg of peptides were injected into nanoHPLC (UltiMate 3000 RSLCnano, Dionex, Thermo Fisher Scientific), followed by separation on a C18-reverse phase trapping column (C18 PepMap100, 300 µm × 5 mm, particle size 5 µm, nano viper, Thermo Fischer Scientific), followed by separation on a C18-reverse phase analytical column (Acclaim PepMap® 100, 75 µm × 25 cm, particle size 3 µm, nanoViper, Thermo Fischer Scientific). Mass spectrometric analysis of eluted peptides where performed on a Q Exactive HF mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) coupled with a TriVersa NanoMate (Advion, Ltd., Harlow, UK) source in LC chip coupling mode. LC Gradient, ionization mode and mass spectrometry mode were performed as described before67. Briefly, peptide lysate were injected into a Nano-HPLC and trapped in a C18-reverse phase column (Acclaim PepMap® 100, 75 µm × 2 cm, particle size 3 µM, nanoViper, Thermo Fisher) for 5 min. Peptide separation was followed by a two-step gradient in 90 min from 4 to 30% of B (B: 80% acetonitrile, 0.1% formic acid in MS-grade water) and then 30 min from 30 to 55% of B. The temperature of the separation column was set to 35 °C and the flow rate was 0.3 µL/min. The eluted peptides were ionized and measured. The MS was set to a full MS/dd-MS2 mode scan with positive polarity. The full MS scan was adjusted to 120,000 resolution, the automatic gain control (AGC) target of 3 × 106 ions, maximum injection time for MS of 80 s and a scan range of 350 to 1550 m/z. The dd-MS2 scan was set to a resolution of 15,000 with the AGC target of 2 × 105 ions, a maximum injection time for 120 ms, TopN 20, isolation window of 1.6 m/z, scan range of 200 to 2000 m/z and a dynamic exclusion of 30 s.
Raw data files were converted into mzML files and searched with MS-GF + against a database obtained from microcosm metagenomes composed of 276,284 predicted protein-encoding sequences. The following parameters were used for peptide identification: enzyme specificity was set to trypsin with one missed cleavage allowed using 10 ppm peptide ion tolerance and 0.05 Da MS/MS tolerance. Oxidation (methionine) and carbamidomethylation (cysteine) were selected as modifications. False discovery rates (FDR) were determined with the node Percolator68. Proteins were considered as identified when at least one unique peptide passed a FDR of 5%.
The MetaProSIP toolshed69 embedded in the Galaxy framework70 (v2.3.2, http://galaxyproject.org/) was used to identify the incorporation of stable isotopes into peptides. MetaProSIP calculates the relative isotope abundance (RIA) on detected isotopic mass traces (m/z tolerance of ±10 ppm, intensity threshold of 1000, and an isotopic trace correlation threshold of 0.7).
In vitro growth experiments
A. muciniphila strain Muc (DSM 22959), Ruthenibacterium lactatiformans strain 585-1 (DSM 100348) and Alistipes timonensis strain JC136 (DSM 25383) were obtained from DSMZ. Muribaculum intestinale strain YL27 (DSM 28989) was kindly provided by Prof. Bärbel Stecher (Max-von-Pettenkofer Institute, LMU Munich, Germany). Bacteroides sp. FP24 was isolated from YCFA agar plates (DSMZ medium 1611-YCFA MEDIUM (modified)). All strains were grown in reduced A II medium71 consisting of (per liter of medium): BHI, 18.5 g; yeast extract, 5 g; trypticase soy broth, 15 g; K2HPO4, 2.5 g; hemin, 10 µg; glucose, 0.5 g; Na2CO3, 42 mg; cysteine, 50 mg; menadione, 5 µg; fetal calf serum (complement-inactivated), 3% (vol/vol). For A. muciniphila cultivation, the growth medium was supplemented with 0.025% (w/v) of mucin. C. difficile was grown in BHI medium (37 g per liter of medium) supplemented with: yeast extract, 5 g; Na2CO3, 42 mg; cysteine, 50 mg; vitamin K1, 1 mg; hemin, 10 µg. All strains were grown at 37 °C under anaerobic conditions until stationary phase, and then serially diluted and plated into media agar plates in order to determine the number of viable cells present in 1 ml of stationary phase-culture. For mixed-growth experiments, the culture volume equivalent to 1 × 106 CFU of each strain was pelleted, cells were washed with PBS, mixed in equal proportions and finally resuspended in 100 μl of PBS. This bacterial mixture containing a total of 5 × 106 BacMix cells was then used to inoculate 2.5 ml of A II medium (diluted two fold in 2× PBS) supplemented or not with 0.25% (10 mM) carbon source (0.125% or 4 mM of NeuAc and 0.125% or 6 mM of GlcNAc). After 12 h, the same tube was inoculated with 1 × 106 C. difficile CFU and bacterial growth was followed by measuring the OD at 600 nm every hour until stationary phase. At three distinct points of the C. difficile growth curve—lag (t12, right after C. difficile addition), mid-exponential (t18) and early stationary phase (t21)—a sample aliquot was collected and ten-fold dilutions were plated in a C. difficile selective medium72. This selective medium (CCFA) includes antibiotics such as cycloserine and cefoxitn at concentrations that are inhibitory to most gut organisms, except for C. difficile, allowing to determine total C. difficile counts. A second aliquot was immediately pelleted and the pellet was stored at −80 °C for RNA extraction.
Quantitative PCR of C. difficile 16S rRNA gene copy number density
DNA was extracted from 100 mg of mouse fecal pellet using the QIAGEN DNAeasy Tissue and Blood kit (Qiagen, Austin, TX, USA) according to the manufacturer´s instructions, with an additional step of mechanical cell disruption by bead beating (30 s at 6.5 m/s) right after addition of kit lysis buffer AL. Extracted DNA (2 μl) was subjected to quantitative PCR using 0.2 μM of primers specifically targeting the C. difficile 16S rRNA gene73 (Supplementary Table 7) and 1× SYBR green Master Mix (Bio-Rad) in a total reaction volume of 20 μl. Standard curves were generated from DNAs extracted from fecal pellets of SPF (uninfected) mice spiked in with different known numbers of C. difficile cells (102, 103, 104, 105, 106, 107, and 108) as described in Kubota et al., 2014. Amplification and detection were performed using a CFX96™ Real-Time PCR Detection System (Bio-Rad) using the following cycling conditions: 95 °C for 5 min, followed by 40 cycles of 95 °C for 15 s, 56 °C for 20 s, and 72 °C for 30 s. To determine the specificity of PCR reactions, melt curve analysis was carried out after amplification by slow cooling from 95 to 60 °C, with fluorescence collection at 0.3 °C intervals and a hold of 10 s at each decrement. Only assays with amplification efficiencies above 80% were considered for analysis.
RNA extraction and quantitative real-time PCR
Total nucleic acids (TNA) were extracted from mouse fecal pellets or from in vitro cultures using a phenol-chloroform bead-beating protocol52. RNA was purified from DNAse-treated TNA fractions using the GeneJET Cleanup and Concentration micro kit (Thermo Fisher Scientific). cDNA was synthesized from 0.5 μg of total RNA with 1 μl of random hexamer oligonucleotide primers. Samples were heated for 5 min at 70 °C. After a slow cooling, 2 μl of deoxynucleoside triphosphates (dNTP; 2.5 mM each), 40 units of recombinant ribonuclease inhibitor (RNaseOUT) and 4 μl of reverse transcription (RT) buffer were added and cDNAs were synthesized for 2 h at 50 °C using 200 units SuperScript™ III Reverse Transcriptase (all reagents used in cDNA synthesis were from Thermo Fisher Scientific). Real-time quantitative PCR was performed in a 20-μl reaction volume containing 2 μl of cDNA, 1x SYBR green Master Mix (Bio-Rad) and 0.2 μM of gene-specific C. difficile primers targeting the following genes: DNA polymerase III PolC-type dnaF74, nanA, nanT17 and nagB (this work; Supplementary Table 7). Amplification and detection were performed as described above. In each sample, the quantity of cDNAs of a gene was normalized to the quantity of cDNAs of the C. difficile DNA polymerase lII gene74 (dnaF). The relative change in gene expression was recorded as the ratio of normalized target concentrations (threshold cycle [ΔΔCT] method75). Fold changes were normalized to in vitro growths in C. difficile minimal medium containing 0.5% glucose17. To determine the specificity of PCR reactions, melt curve analysis was carried out after amplification by slow cooling from 95 to 60 °C, with fluorescence collection at 0.3 °C intervals and a hold of 10 s at each decrement. Only assays with amplification efficiencies above 80% were considered for analysis.
Murine in vivo adoptive transfer experiments
Female C57BL/6N 6-8 weeks old mice (n = 33 total) were purchase from Janvier Labs. Animals were kept in isolated, ventilated cages under specific pathogen-free conditions at the animal facility of the Max F. Perutz Laboratories, University of Vienna, Austria, with controlled temperature of 21 ± 1 °C and humidity of 50 ± 10%, in a 12-h light/dark cycle. Mice received a standard diet (V1124-300; Ssniff, Soest, Germany) and autoclaved water ad libitum. Mice were administered antibiotics (0.25 mg/ml clindamycin (Sigma-Aldrich) for six days in drinking water) and subsequently assigned randomly to one of two groups. One day following antibiotic cessation, mice from each group were split into 3 cages (to minimize the cage effect) and each mouse received either 5,000,000 CFU of a 5-bacteria suspension (BacMix, containing equal numbers of A. muciniphila strain Muc (DSM 22959), Ruthenibacterium lactatiformans strain 585-1 (DSM 100348), Alistipes timonensis strain JC136 (DSM 25383), Muribaculum intestinale strain YL27 (DSM 28989), and Bacteroides sp. isolate FP24) or vehicle (PBS) by gavage (Fig. 5a). At the time of BacMix and BacMixC administration, the mouse diet was switched from a standard diet (V1124-300; Ssniff, Soest, Germany) to a isocaloric polysaccharide-deficient chow76 with sucrose but no cellulose or starch (Ssniff, Soest, Germany). For the BacMixC adoptive transfer, each mouse (n = 10 per group) received 5,000,000 CFU of a 3-bacteria suspension (BacMixC, containing equal numbers of Anaerotruncus sp. isolate FP23; Lactobacillus hominis strain DSM 23910 and of Escherichia sp. isolate FP11) or vehicle (PBS) by gavage (Supplementary Fig. 7). One day after BacMix or BacMixC administration, mice were challenged with 1,000,000 CFU of C. difficile strain 630 deltaErm77. A. muciniphila, R. lactatiformans and M. intestinale were grown in reduced A II medium (supplemented with 0.025% mucin for A. muciniphila). Anaerotruncus sp. isolate FP23, A. timonensis and Bacteroides sp. isolate FP24 were grown in PYG (DSMZ medium 104). Lactobacillus hominis and Escherichia sp. isolate FP11 were grown in YCFA medium (DSMZ medium 1611). C. difficile was grown in BHI medium (37 g per liter of medium). All bacteria were grown under anaerobic conditions (5% H2, 10% CO2, rest N2) at 37 °C and resuspended in anaerobic PBS prior to administration to animals. C. difficile titers were quantified in fecal samples obtained from mice 24, 48, 72, and 120 h after infection by overnight cultivation in C. difficile selective agar plates72. Animals were monitored throughout the entire experiment and weight loss was recorded.
Measurement of mucus thickness and goblet cell volume
Segments of mouse colon (approximately 10 mm long) were fixed in 2% PFA in phosphate-buffered saline (PBS) for 12 h at 4 °C. Samples were washed with 1× PBS and then stored in 70% ethanol at 4 °C until embedding. For embedding, samples were immersed in 3 changes of xylene for 1 h each, then immersed in 3 changes of molten paraffin wax (Paraplast, Electron Microscopy Sciences) at 56–58 °C for one hour each. Blocks were allowed to harden at room temperature. Sections were cut to 4 μm thickness using a Leica microtome (Leica Microsystems) and were floated on a water bath at 40–45 °C, then transferred to slides, dried, and stored at room temperature. In preparation for staining, slides were de-paraffinized by heating at 60 °C for 30 min followed by immersion in 2 changes of xylene for 5 min each, then in 2 changes of 100% ethanol for 1 minute each, then rehydrated through 80%, and 70% ethanol for 1 minute each. Slides were then dipped in water, drained, air-dried and a drop of Alcian Blue stain (Sigma Aldrich) applied on top. Samples were incubated with Alcian Blue for 20 min at room temperature and then washed in water to remove the excess of stain. Samples were mounted in Vectashield Hardset™ Antifade Mounting Medium (Vector Laboratories) and visualized using a Leica Confocal scanning laser microscope (Leica TCS SP8X, Germany). For measurements of the width of the mucus layer and determination of goblet cell volume per crypt, ImageJ software (https://imagej.nih.gov/ij/, 1.48 v) was used.
Histology and histopathological scoring
Mouse colons were flushed with cold PBS to remove all contents, and the entire colon was rolled into Swiss rolls. The tissues were fixed in 2% PFA for 12 h at 4 °C and then washed in 1x PBS and transferred to 70% ethanol until embedding. Swiss rolls were embedded in the same manner as described above for segments of mouse colon. Paraffin-embedded, PFA fixed tissues were sectioned at 4.5 μm. Tissue sections were de-paraffinized and hematoxylin and eosin stained (Mayer’s Hematoxylin, Thermo Scientific; Eosin 1%, Morphisto, Germany). Histopathological analyses were performed using a semi-quantitative scoring system78 that evaluated the severity of crypt damage and cellular infiltration, epithelial erosion and tissue thickening using a severity score from 0 to 3 (0 = intact, 1 = mild, 2 = moderate, 3 = severe), and those scores were multiplied by a score for percent involvement (0 = 0%, 1 = 1–25%, 2 = 26–50%, 3 = 50–100%). A trained and blinded scientist performed the scoring. Representative images were acquired using an Olympus CKX53 microscope and Olympus SC50 camera.
Quantification of C. difficile toxin TcdB
Levels of TcdB in mouse colon contents were quantified relative to a standard curve of purified TcdB using an ELISA assay kit (“Separate detection of C. difficile toxins A and B”, TGC Biomics) according to the manufacturer’s instructions. For each mouse, approximately 10 mg of colon content were used in the assay. The limit of detection for the assay in our conditions was determined to be 5.14 ng of TcdB per gram of colon content (Supplementary Fig. 8c). One of the mice from the BacMix group had toxin levels below the detection limit and was therefore excluded from analysis.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article. More

1.
Bowman, W.D., Hacker, S.D., Cain, M.L. Ecology, 4th Edn. (Sinauer Press, 2017).
2.
Eggleston, D. B., Lipcius, R. N. & Hines, A. H. Density-dependent predation by blue crabs upon infaunal clam species with contrasting distribution and abundance patterns. Mar. Ecol. Progr. Ser. 85, 55–68 (1992).
ADS  Article  Google Scholar 

3.
Boström-Einarsson, L., Bonin, M. C., Munday, P. L. & Jones, G. P. Strong intraspecific competition and habitat selectivity influence abundance of a coral-dwelling damselfish. J. Exp. Mar. Biol. Ecol. 448, 85–92 (2013).
Article  Google Scholar 

4.
Ruggerone, G.T., Zimmermann, M., Myers, K.W., Nielsen, J.L., & Rogers, D.E. Competition between Asian pink salmon (Oncorhynchus gorbuscha) and Alaskan sockeye salmon (O. nerka) in the North Pacific Ocean. Fish. Oceanogr. 12, 209–219 (2003).

5.
Greer, A. L., Briggs, C. J. & Collins, J. P. Testing a key assumption of host-pathogen theory: Density and disease transmission. Oikos 117, 1667–1673 (2008).
Article  Google Scholar 

6.
Turchin, P. Does population ecology have general laws?. Oikos 94, 17–26 (2001).
Article  Google Scholar 

7.
Yenni, G., Adler, P. B. & Ernest, S. M. Strong self-limitation promotes the persistence of rare species. Ecology 93, 456–461 (2012).
PubMed  Article  Google Scholar 

8.
Weis, A. E., Simms, E. L. & Hochberg, M. E. Will plant vigor and tolerance be genetically correlated? Effects of intrinsic growth rate and self-limitation on regrowth. Evol. Ecol. 14, 331–352 (2000).
Article  Google Scholar 

9.
Marino, A., Rodríguez, V. & Pazos, G. Resource-defense polygyny and self-limitation of population density in free-ranging guanacos. Behav. Ecol. 27, 757–765 (2016).
Article  Google Scholar 

10.
Chamaillé-Jammes, S., Fritz, H., Valeix, M., Murindagomo, F. & Clobert, J. Resource variability, aggregation and direct density dependence in an open context: The local regulation of an African elephant population. J. Anim. Ecol. 77, 135–144 (2008).
PubMed  Article  Google Scholar 

11.
Westoby, M. The self-thinning rule. Adv. Ecol. Res. 14, 167–225 (1984).
Article  Google Scholar 

12.
Sedinger, J. S., Herzog, M. P., Person, B. T., Kirk, M. T., Obritchkewitch, T., Martin, P. P., & Bosque, C. Large-scale variation in growth of Black Brant goslings related to food availability. Auk 118, 1088–1095 (2001).

13.
Marschall, E. A. & Crowder, L. B. Density-dependent survival as a function of size in juvenile salmonids in streams. Can. J. Fish. Aquat. Sci. 52, 136–140 (1995).
Article  Google Scholar 

14.
Zheng, X., Huang, L., Huang, B. & Lin, Y. Factors regulating population dynamics of the amphipod Ampithoe valida in a eutrophic subtropical coastal lagoon. Acta Oceanol. Sin. 32, 56–65 (2013).
ADS  CAS  Article  Google Scholar 

15.
Li, G. Y., & Zhang, Z. Q. Does size matter? Fecundity and longevity of spider mites (Tetranychus urticae) in relation to mating and food availability. Syst. Appl. Acarol.-UK 23, 1796–1808 (2018).

16.
Niu, H., Zhao, L. & Sun, J. Phenotypic plasticity of reproductive traits in response to food availability in invasive and native species of nematode. Biol. Inv. 15, 1407–1415 (2013).
Article  Google Scholar 

17.
Cannizzo, Z. J., Lang, S. Q., Benitez-Nelson, B. & Griffen, B. D. An artificial habitat increases the reproductive fitness of a range-shifting species within a newly colonized ecosystem. Sci. Rep. 10, 1–13 (2020).
Article  CAS  Google Scholar 

18.
Zera, A. J. & Harshman, L. G. The physiology of life history trade-offs in animals. Annu. Rev. Ecol. Evol. Syst. 32, 95–126 (2001).
Article  Google Scholar 

19.
Strayer, D. L., D’Antonio, C. M., Essl, F., Fowler, M. S., Geist, J., Hilt, S., & Latzka, A. W. Boom‐bust dynamics in biological invasions: Towards an improved application of the concept. Ecol. Lett. 20, 1337–1350 (2017).

20.
Jaćimović, M. et al. Boom-bust like dynamics of invasive black bullhead (Ameiurus melas) in Lake Sava (Serbia). Fish. Manag. Ecol. 26, 153–164 (2019).
Article  Google Scholar 

21.
Alcorlo, P., Geiger, W., & Otero, M. Reproductive biology and life cycle of the invasive crayfish Procambarus clarkii (Crustacea: Decapoda) in diverse aquatic habitats of South-Western Spain: Implications for population control. Fund. Appl. Limnol./Arch. Hydrobiol. 173, 197–212 (2008).

22.
Melero, Y., Robinson, E., & Lambin, X. Density-and age-dependent reproduction partially compensates culling efforts of invasive non-native American mink. Biol. Invasions 17, 2645–2657.

23.
Yoshida, K., Hoshikawa, K., Wada, T. & Yusa, Y. Patterns of density dependence in growth, reproduction and survival in the invasive freshwater snail Pomacea canaliculata in Japanese rice fields. Freshw. Biol. 58, 2065–2073 (2013).
Article  Google Scholar 

24.
Williams, A. B. & McDermott, J. J. An eastern United States record for the western Indo-Pacific crab, Hemigrapsus sanguineus (Crustacea: Decapoda: Grapsidae). Proc. Biol. Soc. Wash. 103, 108–109 (1990).
Google Scholar 

25.
Breton, G., Faasse, M., Noël, P. & Vincent, T. A new alien crab in Europe: Hemigrapsus sanguineus (Decapoda: Brachyura: Grapsidae). J. Crustacean Biol. 22, 184–189 (2002).
Article  Google Scholar 

26.
Blakeslee, A. M., Kamakura, Y., Onufrey, J., Makino, W., Urabe, J., Park, S., & Miura, O. Reconstructing the invasion history of the Asian shorecrab, Hemigrapsus sanguineus (De Haan 1835) in the Western Atlantic. Mar. Biol. 164, 47 (2017).

27.
Lohrer, A. M. & Whitlatch, R. B. Interactions among aliens: Apparent replacement of one exotic species by another. Ecology 83, 719–732 (2002).
Article  Google Scholar 

28.
Kraemer, G. P., Sellberg, M., Gordon, A. & Main, J. Eight-year record of Hemigrapsus sanguineus (Asian shore crab) invasion in western Long Island Sound estuary. Northeast. Nat. 14, 207–224 (2007).
Article  Google Scholar 

29.
Epifanio, C. E. Invasion biology of the Asian shore crab Hemigrapsus sanguineus: A review. J. Exp. Mar. Biol. Ecol. 441, 33–49 (2013).
Article  Google Scholar 

30.
Lord, J. P. & Williams, L. M. Increase in density of genetically diverse invasive Asian shore crab (Hemigrapsus sanguineus) populations in the Gulf of Maine. Biol. Invasions 19, 1153–1168 (2017).
PubMed  PubMed Central  Article  Google Scholar 

31.
Brousseau, D. J., Kriksciun, K. & Baglivo, J. A. Fiddler crab burrow usage by the Asian crab, Hemigrapsus sanguineus, in a Long Island Sound salt marsh. Northeast. Nat. 10, 415–420 (2003).
Article  Google Scholar 

32.
O’Connor, N. J. Invasion dynamics on a temperate rocky shore: from early invasion to establishment of a marine invader. Biol. Invasions 16, 73–87 (2014).
Article  Google Scholar 

33.
O’Connor, N. J. Changes in population sizes of Hemigrapsus sanguineus (Asian Shore Crab) and resident crab species in southeastern New England (2010–2016). Northeast. Nat. 25, 197–201 (2018).
Article  Google Scholar 

34.
Schab, C. M., Park, S., Waidner, L. A. & Epifanio, C. E. Return of the native: Historical comparison of invasive and indigenous crab populations near the mouth of Delaware Bay. J. Shellfish Res. 32, 751–758 (2013).
Article  Google Scholar 

35.
Bloch, C. P., Curry, K. D., Fisher-Reid, M. C. & Surasinghe, T. D. Population Decline of the Invasive Asian Shore Crab (Hemigrapsus sanguineus) and Dynamics of Associated Intertidal Invertebrates on Cape Cod, Massachusetts. Northeast. Nat. 26, 772–784 (2019).
Article  Google Scholar 

36.
Kraemer, G. P. Changes in population demography and reproductive output of the invasive Hemigrapsus sanguineus (Asian Shore Crab) in the Long Island Sound from 2005 to 2017. Northeast. Nat. 26, 81–94 (2019).
Article  Google Scholar 

37.
Stentiford, G. D., Bateman, K. S., Dubuffet, A., Chambers, E., & Stone, D. M. Hepatospora eriocheir (Wang and Chen, 2007) gen. et comb. nov. infecting invasive Chinese mitten crabs (Eriocheir sinensis) in Europe. J. Invertebr. Pathol. 108, 156–166 (2011).

38.
Bateman, A. W., Buttenschön, A., Erickson, K. D. & Marculis, N. G. Barnacles vs bullies: Modelling biocontrol of the invasive European green crab using a castrating barnacle parasite. Theor. Ecol. 10, 305–318 (2017).
Article  Google Scholar 

39.
Bojko, J., Stebbing, P. D., Dunn, A. M., Bateman, K. S., Clark, F., Kerr, Stewart-Clark, S., Johannesen, Á., & Stentiford, G. D. Green crab Carcinus maenas symbiont profiles along a North Atlantic invasion route. Dis. Aquat. Organ. 128, 147–168 (2018).

40.
Jensen, G. C., McDonald, P. S. & Armstrong, D. A. East meets west: competitive interactions between green crab Carcinus maenas, and native and introduced shore crab Hemigrapsus spp. Mar. Ecol. Progr. Ser. 225, 251–262 (2002).
ADS  Article  Google Scholar 

41.
DeRivera, C. E., Ruiz, G. M., Hines, A. H. & Jivoff, P. Biotic resistance to invasion: Native predator limits abundance and distribution of an introduced crab. Ecology 86, 3364–3376 (2005).
Article  Google Scholar 

42.
Kim, A. K. & O’Connor, N. J. Early stages of the Asian shore crab Hemigrapsus sanguineus as potential prey for the striped killifish Fundulus majalis. J. Exp. Mar. Biol. Ecol. 346, 28–35 (2007).
Article  Google Scholar 

43.
Brousseau, D. J., Murphy, A. E., Enriquez, N. P. & Gibbons, K. Foraging by two estuarine fishes, Fundulus heteroclitus and Fundulus majalis, on juvenile Asian shore crabs (Hemigrapsus sanguineus) in Western Long Island Sound. Estuar. Coast. 31, 144–151 (2008).
Article  Google Scholar 

44.
Savaria, M. C. & O’Connor, N. J. Predation of the non-native Asian shore crab Hemigrapsus sanguineus by a native fish species, the cunner (Tautogolabrus adspersus). J. Exp. Mar. Biol. Ecol. 449, 335–339 (2013).
Article  Google Scholar 

45.
Griffen, B. D. & Delaney, D. G. Species invasion shifts the importance of predator dependence. Ecology 88, 3012–3021 (2007).
PubMed  Article  Google Scholar 

46.
Keogh, C. L., Miura, O., Nishimura, T. & Byers, J. E. The double edge to parasite escape: invasive host is less infected but more infectable. Ecology 98, 2241–2247 (2017).
PubMed  Article  Google Scholar 

47.
Kroft, K. L. & Blakeslee, A. M. Comparison of parasite diversity in native panopeid mud crabs and the invasive Asian shore crab in estuaries of northeast North America. Aquat. Invasions 11, 287–301 (2016).
Article  Google Scholar 

48.
Blakeslee, A. M., Keogh, C. L., Byers, J. E., Lafferty, A. M. K. K. D. & Torchin, M. E. Differential escape from parasites by two competing introduced crabs. Mar. Ecol. Progr. Ser. 393, 83–96 (2009).
ADS  Article  Google Scholar 

49.
Lohrer, A. M., Fukui, Y., Wada, K. & Whitlatch, R. B. Structural complexity and vertical zonation of intertidal crabs, with focus on habitat requirements of the invasive Asian shore crab, Hemigrapsus sanguineus (de Haan). J. Exp. Mar. Biol. Ecol. 244, 203–217 (2000).
Article  Google Scholar 

50.
Ledesma, M. E. & O’Connor, N. J. Habitat and diet of the non-native crab Hemigrapsus sanguineus in southeastern New England. Northeast. Nat. 8, 63–78 (2001).
Article  Google Scholar 

51.
Brousseau, D. J. & Goldberg, R. Effect of predation by the invasive crab Hemigrapsus sanguineus on recruiting barnacles Semibalanus balanoides in western Long Island Sound, USA. Mar. Ecol. Progr. Ser. 339, 221–228 (2007).
ADS  Article  Google Scholar 

52.
Brousseau, D. J. & Baglivo, J. A. Laboratory investigations of food selection by the Asian shore crab, Hemigrapsus sanguineus: Algal versus animal preference. J. Crustacean Biol. 25, 130–134 (2005).
Article  Google Scholar 

53.
Griffen, B. D. Linking individual diet variation and fecundity in an omnivorous marine consumer. Oecologia 174, 121–130 (2014).
ADS  PubMed  Article  Google Scholar 

54.
Riley, M. E., Vogel, M. & Griffen, B. D. Fitness-associated consequences of an omnivorous diet for the mangrove tree crab Aratus pisonii. Aquat. Biol. 20, 35–43 (2014).
Article  Google Scholar 

55.
Griffen, B. D. & Norelli, A. P. Spatially variable habitat quality contributes to within-population variation in reproductive success. Ecol. Evol. 5, 1474–1483 (2015).
PubMed  PubMed Central  Article  Google Scholar 

56.
Griffen, B. D. & Riley, M. E. Potential impacts of invasive crabs on one life history strategy of native rock crabs in the Gulf of Maine. Biol. Invasions 17, 2533–2544 (2015).
Article  Google Scholar 

57.
Belgrad, B. A. & Griffen, B. D. The influence of diet composition on fitness of the blue crab, Callinectes sapidus. PLoS ONE 11, e0145481 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

58.
Griffen, B. D., Vogel, M., Goulding, L. & Hartman, R. Energetic effects of diet choice by invasive Asian shore crabs: Implications for persistence when prey are scarce. Mar. Ecol. Progr. Ser. 522, 181–192 (2015).
ADS  Article  Google Scholar 

59.
Griffen, B. D. The timing of energy allocation to reproduction in an important group of marine consumers. PLoS ONE 13, e0199043 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

60.
Guiñez, R., Petraitis, P. S. & Castilla, J. C. Layering, the effective density of mussels and mass-boundary curves. Oikos 110, 186–190 (2005).
Article  Google Scholar 

61.
Bertness, M. D., Gaines, S. D. & Yeh, S. M. Making mountains out of barnacles: The dynamics of acorn barnacle hummocking. Ecology 79, 1382–1394 (1998).
Article  Google Scholar 

62.
Guiñez, R. & Castilla, J. C. An allometric tridimensional model of self-thinning for a gregarious tunicate. Ecology 82, 2331–2341 (2001).
Article  Google Scholar 

63.
Alunno-Bruscia, M., Petraitis, P. S., Bourget, E. & Fréchette, M. Body size–density relationship for Mytilus edulis in an experimental food-regulated situation. Oikos 90, 28–42 (2000).
Article  Google Scholar 

64.
Weller, D. E. A reevaluation of the‐3/2 power rule of plant self‐thinning. Ecol. Monogr. 57, 23–43 (1987).

65.
Griffen, B. D. & Byers, J. E. Community impacts of two invasive crabs: the interactive roles of density, prey recruitment, and indirect effects. Biol. Invasions 11, 927–940 (2009).
Article  Google Scholar 

66.
Lohrer, A. M. & Whitlatch, R. B. Relative impacts of two exotic brachyuran species on blue mussel populations in Long Island Sound. Mar. Ecol. Progr. Ser. 227, 135–144 (2002).
ADS  Article  Google Scholar 

67.
Nelson, K. Scheduling of reproduction in relation to molting and growth in malacostracan crustaceans. Crustacean Egg Product. 7, 77–116 (1991).
Google Scholar 

68.
Kibria, G. Studies on molting, molting frequency and growth of shrimp Penaeus monodon fed on natural and compounded diets. Asian Fish. Sci. 6, 203–211 (1993).
Google Scholar 

69.
Petit, H., Nègre-Sadargues, G., Castillo, R. & Trilles, J. P. The effects of dietary astaxanthin on growth and moulting cycle of postlarval stages of the prawn, Penaeus japonicus (Crustacea, Decapoda). Comp. Biochem. Physiol. A Physiol. 117, 539–544 (1997).
Article  Google Scholar 

70.
Clark, R. M., Zera, A. J. & Behmer, S. T. Nutritional physiology of life-history trade-offs: How food protein–carbohydrate content influences life-history traits in the wing-polymorphic cricket Gryllus firmus. J. Exp. Biol. 218, 298–308 (2015).
PubMed  Article  Google Scholar 

71.
Rosa, R., Calado, R., Narciso, L. & Nunes, M. L. Embryogenesis of decapod crustaceans with different life history traits, feeding ecologies and habitats: A fatty acid approach. Mar. Biol. 151, 935–947 (2007).
Article  Google Scholar 

72.
Hines, A. H. Allometric constraints and variables of reproductive effort in brachyuran crabs. Mar. Biol. 69, 309–320 (1982).
Article  Google Scholar 

73.
Sorte, C. J., Davidson, V. E., Franklin, M. C., Benes, K. M., Doellman, M. M., Etter, R. J., & Menge, B. A. Long‐term declines in an intertidal foundation species parallel shifts in community composition. Global Change Biol.23, 341–352 (2017).

74.
Goedknegt, M. A., Havermans, J., Waser, A. M., Luttikhuizen, P. C., Velilla, E., Camphuysen, K. C., & Thieltges, D. W. Cross-species comparison of parasite richness, prevalence, and intensity in a native compared to two invasive brachyuran crabs. Aquat. Invasions12, 201–212 (2017).

75.
Latham, A. & Poulin, R. Field evidence of the impact of two acanthocephalan parasites on the mortality of three species of New Zealand shore crabs (Brachyura). Mar. Biol. 141, 1131–1139 (2002).
Article  Google Scholar 

76.
Latham, A. D. M. & Poulin, R. Effect of acanthocephalan parasites on hiding behaviour in two species of shore crabs. J. Helminthol. 76, 323–326 (2002).
CAS  PubMed  Article  Google Scholar 

77.
Griffen, B. D., van den Akker, D., NiNuzzo, E. R., Anderson, L. III, & Vernier, A. Comparing methods for predicting the impacts of invasive species (in press).

78.
Tyrrell, M.C., & Harris, L.G. Potential impact of the introduced Asian shore crab, Hemigrapsus sanguineus, in northern New England: Diet, feeding preferences, and overlap with the green crab, Carcinus maenas. in Marine Bioinvasions: Proceedings of the First National Conference, Cambridge, MA, 24–27 January 1999 (pp. 208–220). (MIT Sea Grant College Program, 2000).

79.
Spilmont, N., Gothland, M. & Seuront, L. Exogenous control of the feeding activity in the invasive Asian shore crab Hemigrapsus sanguineus (De Haan, 1835). Aquat. Invasions 10, 327–332 (2015).
Article  Google Scholar 

80.
McDermott, J. J. The western Pacific brachyuran Hemigrapsus sanguineus (Grapsidae) in its new habitat along the Atlantic coast of the United States: reproduction. J. Crustacean Biol. 18, 308–316 (1998).
Article  Google Scholar 

81.
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, 2019). https://www.R-project.org/.

82.
Griffen, B. D. & Mosblack, H. Predicting diet and consumption rate differences between and within species using gut ecomorphology. J. Anim. Ecol. 80, 854–863 (2011).
PubMed  Article  Google Scholar 

83.
Wolcott, D. L. & O’Connor, N. J. Herbivory in crabs: Adaptations and ecological considerations. Am. Zool. 32, 370–381 (1992).
Article  Google Scholar 

84.
Mattson, W. J. Jr. Herbivory in relation to plant nitrogen content. Annu. Rev. Ecol. Evol. Syst. 11, 119–161 (1980).
Article  Google Scholar 

85.
Griffen, B. D., Cannizzo, Z. J. & Gül, M. R. Ecological and evolutionary implications of allometric growth in stomach size of brachyuran crabs. PLoS ONE 13, e0207416 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

86.
Gül, M. R. & Griffen, B. D. Diet, energy storage, and reproductive condition in a bioindicator species across beaches with different levels of human disturbance. Ecol. Indic. 117, 106636 (2020).
Article  Google Scholar 

87.
Vogt, G. Functional cytology of the hepatopancreas of decapod crustaceans. J. Morphol. 280, 1405–1444 (2019).
CAS  PubMed  Google Scholar 

88.
Kyomo, J. Analysis of the relationship between gonads and hepatopancreas in males and females of the crab Sesarma intermedia, with reference to resource use and reproduction. Mar. Biol. 97, 87–93 (1988).
Article  Google Scholar 

89.
Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A., Smith, G. M. Zero-truncated and zero-inflated models for count data. in Mixed Effects Models and Extensions in Ecology with R 261–293. (Springer, New York, 2009).

90.
Mente, E. Effect of ration level on individual food consumption, growth and protein synthesis in the shore crab Carcinus maenas. In Nutrition, Physiology and Metabolism of Crustaceans 53–67 (Science Publishers, Enfield, 2003).
Google Scholar  More