Philippa Kaur

1.
Estes, J. A. et al. Trophic downgrading of planet Earth. Science 333, 301–306 (2011).
ADS  CAS  PubMed  Article  Google Scholar 
2.
Estes, J. A., Tinker, M. T., Williams, T. M. & Doak, D. F. Killer whale predation on sea otters linking oceanic and nearshore ecosystems. Science 282, 473–476 (1998).
ADS  CAS  PubMed  Article  Google Scholar 

3.
Hamilton, S. L. & Caselle, J. E. Exploitation and recovery of a sea urchin predator has implications for the resilience of southern California kelp forests. Proc. R. Soc. B Biol. Sci. 282, 20141817 (2015).
Article  Google Scholar 

4.
Power, M. E. Effects of fish in river food webs. Science 250, 811–814 (1990).
ADS  CAS  PubMed  Article  Google Scholar 

5.
Saleem, M. Loss of microbiome ecological niches and diversity by global change and trophic downgrading. Microbiome Commun. Ecol. 20, 89–113 (2015).
Google Scholar 

6.
Risch, A. C. et al. Size-dependent loss of aboveground animals differentially affects grassland ecosystem coupling and functions. Nat. Commun. 9, 1–11 (2018).
CAS  Article  Google Scholar 

7.
Eisaguirre, J. H. et al. Trophic redundancy and predator size class structure drive differences in kelp forest ecosystem dynamics. Ecology 101, e02993 (2020).
PubMed  PubMed Central  Article  Google Scholar 

8.
Stromayer, K. A. & Warren, R. J. Are overabundant deer herds in the eastern United States creating alternate stable states in forest plant communities?. Wildl. Soc. Bul. 25, 227–234 (1997).
Google Scholar 

9.
Steneck, R. S. et al. Kelp forest ecosystems: Biodiversity, stability, resilience and future. Environ. Cons. 29, 436–459 (2002).
Article  Google Scholar 

10.
Strickland, M. S., Hawlena, D., Reese, A., Bradford, M. A. & Schmitz, O. J. Trophic cascade alters ecosystem carbon exchange. Proc. Natl. Acad. Sci. 110, 11035–11038 (2013).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

11.
Atwood, T. B. et al. Predator-induced reduction of freshwater carbon dioxide emissions. Nat. Geosci. 6, 191–194 (2013).
ADS  CAS  Article  Google Scholar 

12.
Edwards, M. S. et al. Marine deforestation leads to widespread loss of ecosystem function. PLoS One https://doi.org/10.1371/journal.pone.0226173 (2020).
Article  PubMed  PubMed Central  Google Scholar 

13.
Ripple, W. J. & Becshta, R. L. Hardwood tree decline following large carnivore loss on the Great Plains, USA. Front. Ecol. Environ. 5, 241–246 (2004).
Article  Google Scholar 

14.
Ripple, W. J. Wolves and the ecology of fear: Can predation risk structure ecosystems. Bioscience 54, 55–766 (2004).
Article  Google Scholar 

15.
Beschta, R. L. & Ripple, W. J. Recovering riparian plant communities with wolves in northern Yellowstone, USA. Rest. Ecol. 18, 380–389 (2010).
Article  Google Scholar 

16.
Metzger, J. R., Konar, B. & Edwards, M. S. Assessing a macroalgal foundation species: Community variation with shifting algal assemblages. Mar. Biol. 166, 156 (2019).
Article  Google Scholar 

17.
Gabara, S, Konar, B. & Edwards, M. Biodiversity loss leads to reductions in community-wide trophic complexity. Ecosphere. (in press).

18.
Hamilton, S. L., Caselle, J. E., Malone, D. P. & Carr, M. H. Incorporating biogeography into evaluations of the Channel Islands marine reserve network. Proc. Natl. Acad. Sci. 107, 18272–18277 (2010).
ADS  CAS  PubMed  Article  Google Scholar 

19.
Bauer, H. et al. Lion (Panthera leo) populations are declining rapidly across Africa, except in intensively managed areas. Proc. Natl. Acad. Sci. 112, 14894–14899 (2015).
ADS  CAS  PubMed  Article  Google Scholar 

20.
Mellin, C., MacNeil, A. M., Cheal, A. J., Emslie, M. J. & Caley, J. M. Marine protected areas increase resilience among coral reef communities. Ecol. Lett. 19, 629–637 (2016).
PubMed  Article  Google Scholar 

21.
Levin, S. A. The problem of pattern and scale in ecology. Ecology 73, 1943–1967 (1992).
Article  Google Scholar 

22.
Bengtsson, J., Baillie, S. R. & Lawton, J. Community variability increases with time. Oikos 78, 249–256 (1997).
Article  Google Scholar 

23.
Connell, J. H., Hughes, T. P. & Wallace, C. C. A 30-year study of coral abundance, recruitment, and disturbance at several scales in space and time. Ecol. Monogr. 67, 461–488 (1997).
Article  Google Scholar 

24.
Deutschman, D. H., Levin, S. A., Devine, C. & Buttel, L. A. Scaling from trees to forests: Analysis of a complex simulation model. Science 277, 1688 (1997).
Article  Google Scholar 

25.
Brown, B. L. Spatial heterogeneity reduces temporal variability in stream insect communities. Ecol. Lett. 6, 316–325 (2003).
Article  Google Scholar 

26.
Hughes, T. P. et al. Patterns of recruitment and abundance of corals along the Great Barrier Reef. Nature 397, 59–63 (1999).
ADS  CAS  Article  Google Scholar 

27.
Edwards, M. S. & Estes, J. A. Catastrophe, recovery, and range limitation in NE Pacific kelp forests: A large-scale perspective. Mar. Ecol. Prog. Ser. 320, 79–87 (2006).
ADS  Article  Google Scholar 

28.
Parepa, M., Fischer, M. & Bossdorf, O. Environmental variability priomotes plant invasion. Nat. Commun. 4, 1604 (2013).
ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

29.
Dunstan, P. K. & Johnson, C. R. Linking richness, community variability, and invasion resisteance with patch size. Ecology 87, 2842–2850 (2006).
PubMed  Article  PubMed Central  Google Scholar 

30.
Prevéy, J. S., Germino, M. J., Huntly, N. J. & Inouye, R. S. Exotic plants increase and native plants decrease with loss of foundation species in sagebrush steppe. Plant Ecol. 207, 39–51 (2010).
Article  Google Scholar 

31.
Marks, L. M., Reed, D. C. & Obaza, A. K. Assessment of control methods for the invasive seaweed Sargassum horneri in California, USA. Manag. Biol. Invasions 8, 205–213 (2017).
Article  Google Scholar 

32.
Wiens, J. A. Spatial scaling in ecology. Funct. Ecol. 3, 385–397 (1989).
Article  Google Scholar 

33.
Edwards, M. S. Estimating scale-dependency in disturbance impacts: El Niños and giant kelp forests in the northeast Pacific. Oecologia 138, 436–447 (2004).
ADS  PubMed  Article  Google Scholar 

34.
Dayton, P. K. & Tegner, M. J. The importance of scale in community ecology: A kelp forest example with terrestrial analogs. In A New Ecology: Novel Approaches To Interactive Systems (eds Price, P. W. et al.) (Wiley, New York, 1984).
Google Scholar 

35.
Jenkinson, R. S., Hovel, K. A., Dunn, R. P. & Edwards, M. S. Biogeographical variation in the distribution, abundance, and interactions among key species on rocky reefs of the northeast Pacific. Mar. Ecol. Prog. Ser. 648, 51–65 (2020).
ADS  Article  Google Scholar 

36.
Mann, K. H. Seaweeds: Their productivity and strategy for growth: The role of large marine algae in coastal productivity is far more important than has been suspected. Science 182, 975–981 (1973).
ADS  CAS  PubMed  Article  Google Scholar 

37.
Leith, H. & Whittaker, R. H. Primary Productivity of the Biosphere (Springer, Berin, 1975).
Google Scholar 

38.
Reed, D. C. & Brzezinski, M. A. Kelp forests. In The Management of Natural Coastal Carbon Sinks (eds Laffoley, D. & Grimsditch, G.) 31 (Springer, Gland, 2009).
Google Scholar 

39.
Spector, M. & Edwards, M. S. Modelling the impacts of kelp deforestation on benthic primary production on temperate rocky reefs. Algae 35, 1–16 (2020).
Article  Google Scholar 

40.
Dayton, P. K. Ecology of kelp communities. Ann. Rev. Ecol. Syst. 16, 215–245 (1985).
Article  Google Scholar 

41.
Krumhansl, K. A. & Scheibling, R. E. Production and fate of kelp detritus. Mar. Ecol. Prog. Ser. 467, 281–302 (2012).
ADS  Article  Google Scholar 

42.
Estes, J. A. et al. Complex trophic interactions in kelp forest ecosystems. Bull. Mar. Sci. 74, 621–638 (2004).
ADS  Google Scholar 

43.
Krumhansl, K. A. et al. Global patterns of kelp forest change over the past half-century. Proc. Natl. Acad. Sci. 113, 13785–13790 (2016).
CAS  PubMed  Article  Google Scholar 

44.
Kriegisch, N., Reeves, S. E., Johnson, C. R. & Ling, S. D. Top-down sea urchin overgrazing overwhelms bottom-up stimulation of kelp beds despite sediment enhanncement. J. Exp. Mar. Biol. Ecol. 514(515), 48–58 (2019).
Article  Google Scholar 

45.
Schiebling, R. E., Hennigar, A. W. & Balch, T. Destructive grazing, epiphytism, and disease: The dynamics of sea urchin—kelp interactions in Nova Scotia. Can. J. Fish. Sci. Aquat. 56, 2300–2314 (1999).
Article  Google Scholar 

46.
Fagerli, C. W., Norderhaug, K. M. & Christie, H. C. Lack of sea urchin settlement may explain kelp forest recovery in overgrazed areas in Norway. Mar. Ecol. Prog. Ser. 488, 119–132 (2012).
ADS  Article  Google Scholar 

47.
Filbee-Dexter, K. & Scheibling, R. E. Sea urchin barrens as alternative stable states of collapsed kelp ecosystems. Mar. Ecol. Prog. Ser. 495, 1–25 (2014).
ADS  Article  Google Scholar 

48.
Ling, S. D., Johnson, C. R., Frusher, S. D. & Ridgway, K. R. Overfishing reduces resilience of kelp beds to climate-drivebn catastrophic phase shift. Proc. Natl. Acad. Sci. 106, 22341–22345 (2009).
ADS  CAS  PubMed  Article  Google Scholar 

49.
Simenstad, C. A., Estes, J. A. & Kenyon, K. W. Aleuts, sea otters, and alternate stable state communities. Science 200, 403–411 (1978).
ADS  CAS  PubMed  Article  Google Scholar 

50.
Christie, H., Norderhaug, K. M. & Fredriksen, S. Macrophytes as habitat for fauna. Mar. Ecol. Prog. Ser. 396, 221–233 (2009).
ADS  Article  Google Scholar 

51.
Greig-Smith, P. Pattern in vegetation. J. Ecol. 67, 755–779 (1979).
Article  Google Scholar 

52.
Clark, W. C. Scales of climate impacts. Clim. Change 7, 5–27 (1985).
ADS  Article  Google Scholar 

53.
Woodward, F. I. Climate and Plant Distribution (Cambridge University Press, Cambridge, 1987).
Google Scholar 

54.
Levin, S. A. Multiple scales and the maintenance of biodiversity. Ecosystems 3, 498–506 (2000).
Article  Google Scholar 

55.
Edwards, M.S. Scale-dependent patterns of community regulation in giant kelp forests. Ph.D. dissertation, University of California Santa Cruz (2001).

56.
Estes, J. A. Serendipity: An Ecologists Quest to understand Nature (University of California Press, California, 2016) ((ISBN-13:978-0520285033)).
Google Scholar 

57.
Doroff, A. M. et al. Sea otter population declines in the Aleutian archipelago. J. Mammal. 84, 55–64 (2003).
Article  Google Scholar 

58.
Konar, B. K., Edwards, M. S. & Estes, J. A. Biological interactions maintain the boundaries between kelp forests and urchin barrens in the Aleutian Archipelago. Hydrobiol. 724, 91–107 (2014).
Article  Google Scholar 

59.
Graham, M. H. & Edwards, M. S. Statistical significance versus factor fit: Estimating the importance of individual factor in ecological analysis of variance. Oikos 93, 505–513 (2001).
Article  Google Scholar 

60.
Anderson, M. J., Gorley, R. N. & Clarke, K. R. PERMANOVA+ for PRIMER: Guide to Software and Statistical Methods (K PRIMER-E, Plymouth, 2006).
Google Scholar 

61.
Estes, J. A. & Duggins, D. O. Sea otters and kelp forests in Alaska: Generality and variation in a community ecological paradigm. Ecol. Monogr. 65, 75–100 (1995).
Article  Google Scholar 

62.
Levin, S. A. Challenges in the development of a theory of ecosystem structure and function. In Perspectives in Ecological Theory (eds Roughgarden, J. et al.) 242–255 (Princeton, Princeton University Press, 1989).
Google Scholar 

63.
Tegner, M. J., Dayton, P. K., Edwards, P. B. & Riser, K. L. Large-scale, low-frequency oceanographic effects on kelp forest successions: A tale of two cohorts. Mar. Ecol. Prog. Ser. 146, 17–134 (1997).
Article  Google Scholar 

64.
Karlson, R. H. & Cornell, H. V. Scale-dependent variation in local vs regional effects on coral species richness. Ecol. Monogr. 68, 259–274 (1998).
Article  Google Scholar 

65.
Reed, R. K. & Stabeno, P. J. The recent return of the Alaskan Stream to Near Strait. J. Mar. Res. 51, 515–527 (1993).
Article  Google Scholar 

66.
Ladd, C., Hunt, G. L., Mordy, C. W., Salo, S. A. & Stabeno, P. J. Marine environment of the eastern and central Aleutian Islands. Fish. Oceanogr. 14, 22–38 (2005).
Article  Google Scholar 

67.
Reed, R. K. & Stabeno, P. J. The Aleutian North slope current. In Dynamics of the Bering Sea 177–191 (University of Alaska Sea Grant, Alaska, 1999).
Google Scholar 

68.
Stabeno, P. J. & Reed, R. K. A major circulation anomaly in the western Bering Sea. Geophys. Res. Let. 19, 1671–1674 (1992).
ADS  Article  Google Scholar 

69.
Hunt, G. L. & Stabeno, P. J. Oceanography and ecology of the Aleutian Archipelago: Spatial and temporal variation. Fish. Oceanogr. 14, 292–306 (2005).
Article  Google Scholar 

70.
Konar, et al. A swath across the great divide: Kelp forests across the Samalga Pass biogeographic break. Cont. Shelf Res. 143, 78–88 (2017).
ADS  Article  Google Scholar 

71.
Wilkinson, C. R. & Cheshire, A. C. Patterns in the distribution of sponge populations across the central Great Barrier Reef. Coral Reefs 8, 127–134 (1989).
ADS  Article  Google Scholar 

72.
Wilkinson, C. R. & Cheshire, A. C. Comparisons of sponge populations across the Barrier Reefs of Australia and Belize: Evidence for higher productivity in the Caribbean. Mar. Ecol. Prog. Ser. 67, 285–294 (1990).
ADS  Article  Google Scholar 

73.
Dayton, P. K., Tegner, M. J., Edwards, P. B. & Riser, K. L. Temporal and spatial scales of kelp demography: The role of oceanographic climate. Ecol. Monogr. 69, 219–250 (1999).
Article  Google Scholar 

74.
Konar, B., Edwards, M. & Efird, T. Local habitat and regional oceanographic influence on fish distribution patterns in the diminishing kelp forests across the Aleutian Archipelago. Environ. Biol. Fish. 98, 1935–1951 (2015).
Article  Google Scholar 

75.
Blanchette, C. A., Broitman, B. R. & Gaines, S. D. Intertidal community structure and oceanographic patterns around Santa Cruz Island, CA, USA. Mar. Biol. 149, 689–701 (2006).
Article  Google Scholar 

76.
García-Charton, J. A. et al. Multi-scale spatial heterogeneity, habitat structure, and the effect of marine reserves on Western Mediterranean rocky reef fish assemblages. Mar. Biol. 144, 161–182 (2004).
Article  Google Scholar 

77.
Hewitt, J. E., Thrush, S. F. & Dayton, P. D. Habitat variation, species diversity and ecological functioning in a marine system. J. Exp. Mar. Biol. Ecol. 366, 116–122 (2008).
Article  Google Scholar 

78.
Bland, A., Konar, B. & Edwards, M. Spatial trends and environmental drivers of epibenthic shelf community structure across the Aleutian Islands. Cont. Shelf Res. 175, 12–29 (2019).
ADS  Article  Google Scholar 

79.
Bruno, J. F., Petes, L. E., Drew Harvell, C. & Hettinger, A. Nutrient enrichment can increase the severity of coral diseases. Ecol. Lett. 6, 1056–1061 (2003).
Article  Google Scholar 

80.
Halpern, B. S., Selkoe, K. A., Micheli, F. & Kappel, C. V. Evaluating and ranking the vulnerability of global marine ecosystems to anthropogenic threats. Cons. Biol. 21, 1301–1315 (2007).
Article  Google Scholar 

81.
Hoffman, G. et al. High-frequency dynamics of ocean pH: A multi-ecosystem comparison. PLoS One 20, 20 (2011).
Google Scholar 

82.
Svenning, J. C. et al. Science for a wilder Anthropocene: Synthesis and future directions for trophic rewilding research. Proc. Natl. Acad. Sci. 113, 898–906 (2016).
ADS  CAS  PubMed  Article  Google Scholar 

83.
Stewart, N. & Konar, B. Kelp forests versus urchin barrens: Alternate stable states and their effect on sea otter prey quality in the Aleutian Islands. J. Mar. Sci. https://doi.org/10.1155/2012/492308 (2012).
Article  Google Scholar 

84.
Rogachev, K. A. & Shlyk, N. V. The role of the Aleutian eddies in the Kamchatka current warming. Russ. Meteorol. Hydrol. 43, 43–48 (2018).
Article  Google Scholar 

85.
Scheibling, R. E. & Hennigar, A. W. Recurrent outbreaks of disease in sea urchins Strongylocentrotus droebachiensis in Nova Scotia: Evidence for a link with large-scale meterologic and oceanographic events. Mar. Ecol. Prog. Ser. 152, 155–165 (1997).
ADS  Article  Google Scholar 

86.
Girard, D., Clemente, S., Toledo-Guedes, K., Brito, A. & Hernández, J. C. A mass mortality of subtropical intertidal populations of the sea urchin Paracentrotus lividus: Analysis of potential links with environmental conditions. Mar. Ecol. 33, 377–385 (2012).
ADS  Article  Google Scholar 

87.
Feehan, C. J. & Scheibling, R. E. Disease as a control of sea urchin populations in Nova Scotian kelp beds. Mar. Ecol. Prog. Ser. 500, 149–158 (2014).
ADS  Article  Google Scholar 

88.
Hagen, N. T. Sea urchin outbreaks and nematode epizootics in Vestfjorden, northern Norway. Sarsia 72, 213–229 (1987).
Article  Google Scholar 

89.
Shimizu, M. & Nagakura, K. Acid phosphatase activity in the body wall of the sea urchin, Strongylocentrotus intermedius, cultured at varying water temperatures. Comp. Biochem. Physiol. 106B, 303–307 (1993).
CAS  Google Scholar 

90.
Wang, Y. et al. Isolation and characterization of bacteria associated with a syndrome disease of sea urchin Strongylocentrotus intermedius in North China. Aquacult. Res. 44, 691–700 (2013).
CAS  Article  Google Scholar 

91.
Behrens, M. D. & Lafferty, K. D. Effects of marine reserves and urchin disease on southern Californian rocky reef communities. Mar. Ecol. Prog. Ser. 279, 129–139 (2004).
ADS  Article  Google Scholar 

92.
Feehan, C. J. & Scheibling, R. E. A mass mortality of subtropical intertidal populations of the sea urchin Paracentrotus lividus: Analysis of potential links with environmental conditions. Mar. Biol. 161, 1467–1485 (2014).
CAS  Article  Google Scholar 

93.
Stabeno, P. J., Kachel, D. G., Kachel, N. B. & Sullivan, M. E. Observations from moorings in the Aleutian Passes: Temperature, salinity and transport. Fish. Oceanogr. 14, 39–54 (2005).
Article  Google Scholar 

94.
Favorite, F. Flow into the Bering Sea through the Aleutian Passes. In Oceanography of the Bering Sea with Emphasis on Renewable Resources (eds Hood, D. W. & Kelly, E. J.) 3–37 (Institute of Marine Science University of Alaska, Fairbanks, 1974).
Google Scholar  More

1.
Clark, T. D. et al. Ocean acidification does not impair the behaviour of coral reef fishes. Nature 577, 370–375 (2020).
ADS  CAS  PubMed  PubMed Central  Google Scholar 
2.
Munday, P. L., Jarrold, M. D. & Nagelkerken, I. in Fish Physiology: Carbon Dioxide Vol. 37 (eds Grosell, M. et al.) 323–368 (Elsevier, 2019).

3.
Munday, P. L. et al. Ocean acidification impairs olfactory discrimination and homing ability of a marine fish. Proc. Natl Acad. Sci. USA 106, 1848–1852 (2009).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

4.
Munday, P. L. et al. Replenishment of fish populations is threatened by ocean acidification. Proc. Natl Acad. Sci. USA 107, 12930–12934 (2010).
ADS  CAS  Google Scholar 

5.
Dixson, D. L., Munday, P. L. & Jones, G. P. Ocean acidification disrupts the innate ability of fish to detect predator olfactory cues. Ecol. Lett. 13, 68–75 (2010).
PubMed  PubMed Central  Google Scholar 

6.
Munday, P. L. et al. Effects of elevated CO2 on predator avoidance behaviour by reef fishes is not altered by experimental test water. PeerJ 4, e2501 (2016).
PubMed  PubMed Central  Google Scholar 

7.
Jarrold, M. D., Humphrey, C., McCormick, M. I. & Munday, P. L. Diel CO2 cycles reduce severity of behavioural abnormalities in coral reef fish under ocean acidification. Sci. Rep. 7, 10153 (2017).
ADS  PubMed  PubMed Central  Google Scholar 

8.
McMahon, S. J., Donelson, J. M. & Munday, P. L. Food ration does not influence the effect of elevated CO2 on antipredator behaviour of a reef fish. Mar. Ecol. Prog. Ser. 586, 155–165 (2018).
ADS  CAS  Google Scholar 

9.
Munday, P. L., Cheal, A. J., Dixson, D. L., Rummer, J. L. & Fabricius, K. E. Behavioural impairment in reef fishes caused by ocean acidification at CO2 seeps. Nat. Clim. Change 4, 487–492 (2014).
ADS  CAS  Article  Google Scholar 

10.
Ferrari, M. C. O. et al. Predation in high CO2 waters: prey fish from high-risk environments are less susceptible to ocean acidification. Integr. Comp. Biol. 57, 55–62 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

11.
Ferrari, M. C. O. et al. Intrageneric variation in antipredator responses of coral reef fishes affected by ocean acidification: implications for climate change projections on marine communities. Glob. Change Biol. 17, 2980–2986 (2011).
ADS  Google Scholar 

12.
Welch, M. J., Watson, S.-A., Welsh, J. Q., McCormick, M. I. & Munday, P. L. Effects of elevated CO2 on fish behaviour undiminished by transgenerational acclimation. Nat. Clim. Change 4, 1086–1089 (2014).
ADS  CAS  Google Scholar 

13.
Ferrari, M. C. O., Wisenden, B. D. & Chivers, D. P. Chemical ecology of predator–prey interactions in aquatic ecosystems: a review and prospectus. Can. J. Zool. 88, 698–724 (2010).
Google Scholar 

14.
Ferrari, M. C. O. et al. Interactive effects of ocean acidification and rising sea temperatures alter predation rate and predator selectivity in reef fish communities. Glob. Change Biol. 21, 1848–1855 (2015).
ADS  Google Scholar 

15.
Kats, L. B. & Dill, L. M. The scent of death: chemosensory assessment of predation risk by prey animals. Ecoscience 5, 361–394 (1998).
Google Scholar 

16.
Roggatz, C. C., Lorch, M., Hardege, J. D. & Benoit, D. M. Ocean acidification affects marine chemical communication by changing structure and function of peptide signalling molecules. Glob. Change Biol. 22, 3914–3926 (2016).
ADS  Google Scholar 

17.
Jutfelt, F., Sundin, J., Raby, G. D., Krång, A.-S. & Clark, T. D. Two-current choice flumes for testing avoidance and preference in aquatic animals. Methods Ecol. Evol. 8, 379–390 (2017).
Google Scholar 

18.
Domenici, P., Allan, B., McCormick, M. I. & Munday, P. L. Elevated carbon dioxide affects behavioural lateralization in a coral reef fish. Biol. Lett. 8, 78–81 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

19.
Domenici, P., Allan, B. J. M., Watson, S.-A., McCormick, M. I. & Munday, P. L. Shifting from right to left: the combined effect of elevated CO2 and temperature on behavioural lateralization in a coral reef fish. PLoS ONE 9, e87969 (2014).
ADS  PubMed  PubMed Central  Google Scholar 

20.
Nilsson, G. E. et al. Near-future carbon dioxide levels alter fish behaviour by interfering with neurotransmitter function. Nat. Clim. Change 2, 201–204 (2012).
ADS  CAS  Google Scholar 

21.
Ferrari, M. C. O. et al. Effects of ocean acidification on visual risk assessment in coral reef fishes. Funct. Ecol. 26, 553–558 (2012).
Google Scholar 

22.
Chung, W. S., Marshall, N. J., Watson, S.-A., Munday, P. L. & Nilsson, G. E. Ocean acidification slows retinal function in a damselfish through interference with GABAA receptors. J. Exp. Biol. 217, 323–326 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

23.
Welch, M. & Munday, P. L Raw Data for Olfactory Response of Acanthochromis polyacanthus in a Y-maze Flume (dataset). https://doi.org/10.4225/28/5add60af3a267 (James Cook University, 2018).

24.
Schunter, C. et al. Molecular signatures of transgenerational response to ocean acidification in a species of reef fish. Nat. Clim. Change 6, 1014–1018 (2016).
ADS  CAS  Google Scholar 

25.
Allan, B. J. M., Miller, G. M., McCormick, M. I., Domenici, P. & Munday, P. L. Parental effects improve escape performance of juvenile reef fish in a high-CO2 world. Proc. R. Soc. Lond. B 281, 20132179 (2014).
Google Scholar 

26.
Welch, M. J. & Munday, P. L. Heritability of behavioural tolerance to high CO2 in a coral reef fish is masked by nonadaptive phenotypic plasticity. Evol. Appl. 10, 682–693 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

27.
Stiasny, M. H. et al. Ocean acidification effects on Atlantic cod larval survival and recruitment to the fished population. PLoS ONE 11, e0155448 (2016).
PubMed  PubMed Central  Google Scholar 

28.
Murray, C. S., Wiley, D. & Baumann, H. High sensitivity of a keystone forage fish to elevated CO2 and temperature. Conserv. Physiol. 7, coz084 (2019).
PubMed  PubMed Central  Google Scholar 

29.
Munday, P. L. et al. Elevated CO2 affects the behavior of an ecologically and economically important coral reef fish. Mar. Biol. 160, 2137–2144 (2013).
CAS  Google Scholar 

30.
Allan, B. J. M., Domenici, P., McCormick, M. I., Watson, S.-A. & Munday, P. L. Elevated CO2 affects predator–prey interactions through altered performance. PLoS ONE 8, e58520 (2013).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

31.
Benítez, S. et al. Intertidal pool fish Girella laevifrons (Kyphosidae) shown strong physiological homeostasis but shy personality: the cost of living in hypercapnic habitats. Mar. Pollut. Bull. 118, 57–63 (2017).
PubMed  PubMed Central  Google Scholar 

32.
Borges, F. O. et al. Ocean warming and acidification may challenge the riverward migration of glass eels. Biol. Lett. 15, 20180627 (2019).
PubMed  PubMed Central  Google Scholar 

33.
Castro, J. M. et al. Painted goby larvae under high-CO2 fail to recognize reef sounds. PLoS ONE 12, e0170838 (2017).
PubMed  PubMed Central  Google Scholar 

34.
Chivers, D. P. et al. Impaired learning of predators and lower prey survival under elevated CO2: a consequence of neurotransmitter interference. Glob. Change Biol. 20, 515–522 (2014).
ADS  Google Scholar 

35.
Ferrari, M. C. O. et al. Putting prey and predator into the CO2 equation—qualitative and quantitative effects of ocean acidification on predator–prey interactions. Ecol. Lett. 14, 1143–1148 (2011).
PubMed  PubMed Central  Google Scholar 

36.
Forsgren, E., Dupont, S., Jutfelt, F. & Amundsen, T. Elevated CO2 affects embryonic development and larval phototaxis in a temperate marine fish. Ecol. Evol. 3, 3637–3646 (2013).
PubMed  PubMed Central  Google Scholar 

37.
Goldenberg, S. U. et al. Ecological complexity buffers the impacts of future climate on marine consumers. Nat. Clim. Change 8, 229–233 (2018).
ADS  Google Scholar 

38.
Green, L. & Jutfelt, F. Elevated carbon dioxide alters the plasma composition and behaviour of a shark. Biol. Lett. 10, 20140538 (2014).
PubMed  PubMed Central  Google Scholar 

39.
Hamilton, T. J., Holcombe, A. & Tresguerres, M. CO2-induced ocean acidification increases anxiety in rockfish via alteration of GABAA receptor functioning. Proc. R. Soc. B 281, 20132509 (2014).
PubMed  PubMed Central  Google Scholar 

40.
Heuer, R. M., Welch, M. J., Rummer, J. L., Munday, P. L. & Grosell, M. Altered brain ion gradients following compensation for elevated CO2 are linked to behavioural alterations in a coral reef fish. Sci. Rep. 6, 33216 (2016).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

41.
Hurst, T. P. et al. Elevated CO2 alters behavior, growth, and lipid composition of Pacific cod larvae. Mar. Environ. Res. 145, 52–65 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

42.
Jiahuan, R. et al. Ocean acidification impairs foraging behavior by interfering with olfactory neural signal transduction in black sea bream, Acanthopagrus schlegelii. Front. Physiol. 9, 1592 (2018).
PubMed  PubMed Central  Google Scholar 

43.
Jutfelt, F., Bresolin de Souza, K., Vuylsteke, A. & Sturve, J. Behavioural disturbances in a temperate fish exposed to sustained high-CO2 levels. PLoS ONE 8, e65825 (2013).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

44.
Lai, F., Jutfelt, F. & Nilsson, G. E. Altered neurotransmitter function in CO2-exposed stickleback (Gasterosteus aculeatus): a temperate model species for ocean acidification research. Conserv. Physiol. 3, cov018 (2015).
PubMed  PubMed Central  Google Scholar 

45.
Laubenstein, T. D., Rummer, J. L., McCormick, M. I. & Munday, P. L. A negative correlation between behavioural and physiological performance under ocean acidification and warming. Sci. Rep. 9, 4265 (2019).
ADS  PubMed  PubMed Central  Google Scholar 

46.
Lopes, A. F. et al. Behavioural lateralization and shoaling cohesion of fish larvae altered under ocean acidification. Mar. Biol. 163, 243 (2016).
Google Scholar 

47.
Maulvault, A. L. et al. Differential behavioural responses to venlafaxine exposure route, warming and acidification in juvenile fish (Argyrosomus regius). Sci. Total Environ. 634, 1136–1147 (2018).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

48.
McCormick, M. I., Watson, S.-A. & Munday, P. L. Ocean acidification reverses competition for space as habitats degrade. Sci. Rep. 3, 3280 (2013).
PubMed  PubMed Central  Google Scholar 

49.
Munday, P. L. et al. Selective mortality associated with variation in CO2 tolerance in a marine fish. Ocean Acidif. 1, 1–5 (2012).
Google Scholar 

50.
Nadler, L. E., Killen, S. S., McCormick, M. I., Watson, S.-A. & Munday, P. L. Effect of elevated carbon dioxide on shoal familiarity and metabolism in a coral reef fish. Conserv. Physiol. 4, cow052 (2016).
PubMed  PubMed Central  Google Scholar 

51.
Näslund, J., Lindstrom, E., Lai, F. & Jutfelt, F. Behavioural responses to simulated bird attacks in marine three-spined sticklebacks after exposure to high CO2 levels. Mar. Freshw. Res. 66, 877–885 (2015).
Google Scholar 

52.
Ou, M. et al. Responses of pink salmon to CO2-induced aquatic acidification. Nat. Clim. Change 5, 950–955 (2015).
ADS  CAS  Google Scholar 

53.
Paula, J. R. et al. Neurobiological and behavioural responses of cleaning mutualisms to ocean warming and acidification. Sci. Rep. 9, 12728 (2019).
ADS  PubMed  PubMed Central  Google Scholar 

54.
Paula, J. R. et al. The past, present and future of cleaner fish cognitive performance as a function of CO2 levels. Biol. Lett. 15, 20190618 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

55.
Porteus, C. S. et al. Near-future CO2 levels impair the olfactory system of a marine fish. Nat. Clim. Change 8, 737–743 (2018).
ADS  CAS  Google Scholar 

56.
Pistevos, J. C. A., Nagelkerken, I., Rossi, T., Olmos, M. & Connell, S. D. Ocean acidification and global warming impair shark hunting behaviour and growth. Sci. Rep. 5, 16293 (2015).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

57.
Regan, M. D. et al. Ambient CO2, fish behaviour and altered GABAergic neurotransmission: exploring the mechanism of CO2-altered behaviour by taking a hypercapnia dweller down to low CO2 levels. J. Exp. Biol. 219, 109–118 (2016).
PubMed  PubMed Central  Google Scholar 

58.
Rossi, T., Nagelkerken, I., Pistevos, J. C. A. & Connell, S. D. Lost at sea: ocean acidification undermines larval fish orientation via altered hearing and marine soundscape modification. Biol. Lett. 12, 20150937 (2016).
PubMed  PubMed Central  Google Scholar 

59.
Rossi, T., Pistevos, J. C. A., Connell, S. D. & Nagelkerken, I. On the wrong track: ocean acidification attracts larval fish to irrelevant environmental cues. Sci. Rep. 8, 5840 (2018).
ADS  PubMed  PubMed Central  Google Scholar 

60.
Schmidt, M. et al. Impact of ocean warming and acidification on the behaviour of two co-occurring gadid species, Boreogadus saida and Gadus morhua, from Svalbard. Mar. Ecol. Prog. Ser. 571, 183–191 (2017).
ADS  CAS  Google Scholar 

61.
Schunter, C. et al. An interplay between plasticity and parental phenotype determines impacts of ocean acidification on a reef fish. Nat. Ecol. Evol. 2, 334–342 (2018).
PubMed  PubMed Central  Google Scholar 

62.
Simpson, S. D. et al. Ocean acidification erodes crucial auditory behaviour in a marine fish. Biol. Lett. 7, 917–920 (2011).
CAS  PubMed  PubMed Central  Google Scholar 

63.
Sundin, J. & Jutfelt, F. 9–28 d of exposure to elevated pCO2 reduces avoidance of predator odour but had no effect on behavioural lateralization or swimming activity in a temperate wrasse (Ctenolabrus rupestris). ICES J. Mar. Sci. 73, 620–632 (2016).
Google Scholar 

64.
Sundin, J. & Jutfelt, F. Effects of elevated carbon dioxide on male and female behavioural lateralization in a temperate goby. R. Soc. Open Sci. 5, 171550 (2018).
ADS  PubMed  PubMed Central  Google Scholar 

65.
Devine, B. M. & Munday, P. L. Habitat preferences of coral-associated fishes are altered by short-term exposure to elevated CO2. Mar. Biol. 160, 1955–1962 (2013).
CAS  Google Scholar 

66.
Velez, Z., Roggatz, C. C., Benoit, D. M., Hardege, J. D. & Hubbard, P. C. Short- and medium-term exposure to ocean acidification reduces olfactory sensitivity in gilthead seabream. Front. Physiol. 10, 731 (2019).
PubMed  PubMed Central  Google Scholar 

67.
Williams, C. R. et al. Elevated CO2 impairs olfactory-mediated neural and behavioral responses and gene expression in ocean-phase coho salmon (Oncorhynchus kisutch). Glob. Change Biol. 25, 963–977 (2019).
ADS  Google Scholar  More

1.
Dixson, D. L., Munday, P. L. & Jones, G. P. Ocean acidification disrupts the innate ability of fish to detect predator olfactory cues. Ecol. Lett. 13, 68–75 (2010).
Article  Google Scholar 
2.
Munday, P. L. et al. Replenishment of fish populations is threatened by ocean acidification. Proc. Natl Acad. Sci. USA 107, 12930–12934 (2010).
ADS  CAS  Article  Google Scholar 

3.
Clements, J. C. & Hunt, H. L. Marine animal behaviour in a high CO2 ocean. Mar. Ecol. Prog. Ser. 536, 259–279 (2015).
ADS  CAS  Article  Google Scholar 

4.
Watson, S.-A. et al. Marine mollusc predator-escape behaviour altered by near-future carbon dioxide levels. Proc. R. Soc. B 281, 20132377 (2014).
Article  Google Scholar 

5.
Clark, T. D. et al. Ocean acidification does not impair the behaviour of coral reef fishes. Nature 577, 370–375 (2020).
ADS  CAS  Article  Google Scholar 

6.
Munday, P. L. et al. Methods matter in repeating ocean acidification studies. Nature https://doi.org/10.1038/s41586-020-2803-x (2020).
Article  Google Scholar 

7.
Nosek, B. A. & Errington, T. M. What is replication? PLoS Biol. 18, e3000691 (2020).
CAS  Article  Google Scholar 

8.
Munday, P. L. et al. Elevated CO2 affects the behavior of an ecologically and economically important coral reef fish. Mar. Biol. 160, 2137–2144 (2013).
CAS  Article  Google Scholar 

9.
Munday, P. L., Cheal, A. J., Dixson, D. L., Rummer, J. L. & Fabricius, K. E. Behavioural impairment in reef fishes caused by ocean acidification at CO2 seeps. Nat. Clim. Change 4, 487–492 (2014).
ADS  CAS  Article  Google Scholar 

10.
Munday, P. L. et al. Effects of elevated CO2 on predator avoidance behaviour by reef fishes is not altered by experimental test water. PeerJ 4, e2501 (2016).
Article  Google Scholar 

11.
Ioannidis, J. P. A. Why science is not necessarily self-correcting. Perspect. Psychol. Sci. 7, 645–654 (2012).
Article  Google Scholar 

12.
Browman, H. I. Applying organized scepticism to ocean acidification research. ICES J. Mar. Sci. 73, 529–536 (2016).
Article  Google Scholar 

13.
Nissen, S. B., Magidson, T., Gross, K. & Bergstrom, C. T. Publication bias and the canonization of false facts. eLife 5, e21451 (2016).
Article  Google Scholar  More

1.
Monteiro, P. J. M., Miller, S. A. & Horvath, A. Towards sustainable concrete. Nat. Mater. 16, 698–699 (2017).
ADS  CAS  Article  Google Scholar 
2.
Schneider, M., Romer, M., Tschudin, M. & Bolio, H. Sustainable cement production—Present and future. Cem. Concr. Res. 41, 642–650 (2011).
CAS  Article  Google Scholar 

3.
Worrell, E., Price, L., Martin, N., Hendriks, C. & Meida, L. O. Carbon dioxide emissions from the global cement industry. Annu. Rev. Energy Environ. 26, 303–329 (2001).
Article  Google Scholar 

4.
Mehta, P. K. & Monteiro, P. J. M. Concrete: Microstructure, Properties, and Materials (McGraw-Hill, New York, 2005).
Google Scholar 

5.
Chen, C., Habert, G., Bouzidi, Y. & Jullien, A. Environmental impact of cement production: Detail of the different processes and cement plant variability evaluation. J. Clean. Prod. 18, 478–485 (2010).
CAS  Article  Google Scholar 

6.
Bu, J., Tian, Z., Zheng, S. & Tang, Z. Effect of sand content on strength and pore structure of cement mortar. J. Wuhan Univ. Technol. Mater. Sci. Ed. 32, 382–390 (2017).
CAS  Article  Google Scholar 

7.
Khaliq, W. & Ehsan, M. B. Crack healing in concrete using various bio influenced self-healing techniques. Constr. Build. Mater. 102, 349–357 (2016).
CAS  Article  Google Scholar 

8.
Vijay, K. & Murmu, M. Effect of calcium lactate on compressive strength and self-healing of cracks in microbial concrete. Front. Struct. Civ. Eng. https://doi.org/10.1007/s11709-018-0494-2 (2018).
Article  Google Scholar 

9.
Vahabi, A., Ramezanianpour, A. A. & Noghabi, K. A. A preliminary insight into the revolutionary new line in improving concrete properties using an indigenous bacterial strain Bacillus licheniformis AK01, as a healing agent. Eur. J. Environ. Civ. Eng. 19, 614–627 (2015).
Article  Google Scholar 

10.
Schlangen, E. & Joseph, C. Self-Healing Processes in Concrete. Self-Healing Materials: Fundamentals, Design Strategies, and Applications (WILEY-VCH Verlag Gmbh & Co. KGaA, New York, 2009). https://doi.org/10.1002/9783527625376.ch5.
Google Scholar 

11.
Mehta, P. K. High-performance, high-volume fly ash concrete for sustainable development. Int. Work. Sustain. Dev. Concr. Technol. 31, 3–14 (2008).
Google Scholar 

12.
Achal, V. & Mukherjee, A. A review of microbial precipitation for sustainable construction. Constr. Build. Mater. 93, 1224–1235 (2015).
Article  Google Scholar 

13.
Burne, R. A. & Chen, Y. Y. M. Bacterial ureases in infectious diseases. Microbes Infect. 2, 533–542 (2000).
CAS  Article  Google Scholar 

14.
Dick, J. et al. Bio-deposition of a calcium carbonate layer on degraded limestone by Bacillus species. Biodegradation 17, 357–367 (2006).
CAS  Article  Google Scholar 

15.
De Muynck, W., De Belie, N. & Verstraete, W. Microbial carbonate precipitation in construction materials: A review. Ecol. Eng. 36, 118–136 (2010).
Article  Google Scholar 

16.
Van Tittelboom, K., De Belie, N., De Muynck, W. & Verstraete, W. Use of bacteria to repair cracks in concrete. Cem. Concr. Res. 40, 157–166 (2010).
Article  Google Scholar 

17.
Dhami, N. K., Reddy, M. S. & Mukherjee, M. S. Biomineralization of calcium carbonates and their engineered applications: A review. Front. Microbiol. 4, 1–13 (2013).
Article  Google Scholar 

18.
Ramachandran, S. K., Ramakrishnan, V. & Bang, S. S. Remediation of concrete using micro-organism. Aci Mater. J. 1, 1. https://doi.org/10.14359/10154 (2001).
Article  Google Scholar 

19.
Dhami, N. K., Reddy, M. S. & Mukherjee, A. Bacillus megaterium mediated mineralization of calcium carbonate as biogenic surface treatment of green building materials. World J. Microbiol. Biotechnol. 29, 2397–2406 (2013).
CAS  Article  Google Scholar 

20.
De Muynck, W., Cox, K., De Belie, N. & Verstraete, W. Bacterial carbonate precipitation as an alternative surface treatment for concrete. Constr. Build. Mater. 22, 875–885 (2008).
Article  Google Scholar 

21.
Achal, V., Mukerjee, A. & Reddy, M. S. Biogenic treatment improves the durability and remediates the cracks of concrete structures. Constr. Build. Mater. 48, 1–5 (2013).
Article  Google Scholar 

22.
Chahal, N., Siddique, R. & Rajor, A. Influence of bacteria on the compressive strength, water absorption and rapid chloride permeability of concrete incorporating silica fume. Constr. Build. Mater. 37, 645–651 (2012).
Article  Google Scholar 

23.
van Paassen, L. A. et al. Potential soil reinforcement by biological denitrification. Ecol. Eng. 36, 168–175 (2010).
Article  Google Scholar 

24.
Erşan, Y. Ç, Hernandez-Sanabria, E., Boon, N. & De Belie, N. Enhanced crack closure performance of microbial mortar through nitrate reduction. Cem. Concr. Compos. 70, 159–170 (2016).
Article  Google Scholar 

25.
Glass, C. & Silverstein, J. Denitrification kinetics of high nitrate concentration water: pH effect on inhibition and nitrite accumulation. Water Res. 32, 831–839 (1998).
CAS  Article  Google Scholar 

26.
van Paassen, L. Biogrout: Ground Improvement by Microbially Induced Carbonate Precipitation (Delft University of Technology, Delft, 2009).
Google Scholar 

27.
Li, M., Fu, Q. L., Zhang, Q., Achal, V. & Kawasaki, S. Bio-grout based on microbially induced sand solidification by means of asparaginase activity. Sci. Rep. 5, 1–9 (2015).
Google Scholar 

28.
Jonkers, H. M. & Schlangen, E. A two component bacteria-based self-healing concrete. In Concrete Repair, Rehabilitation and Retroftting II (eds Alexander, M. G. et al.) (CRC Press, Taylor and Francis Group, Boca Raton, 2009).
Google Scholar 

29.
Jonkers, H. M., Thijssen, A., Muyzer, G., Copuroglu, O. & Schlangen, E. Application of bacteria as self-healing agent for the development of sustainable concrete. Ecol. Eng. 36, 230–235 (2010).
Article  Google Scholar 

30.
Jonkers, H. M. Bacteria-based self-healing concrete. Heron 56, 1–12 (2011).
Google Scholar 

31.
Chaurasia, L., Bisht, V., Singh, L. P. & Gupta, S. A novel approach of biomineralization for improving micro and macro-properties of concrete. Constr. Build. Mater. 195, 340–351 (2019).
CAS  Article  Google Scholar 

32.
Seifan, M., Samani, A. K. & Berenjian, A. Induced calcium carbonate precipitation using Bacillus species. Appl. Microbiol. Biotechnol. 100, 9895–9906 (2016).
CAS  Article  Google Scholar 

33.
Wang, J., Ersan, Y. C., Boon, N. & De Belie, N. Application of microorganisms in concrete: A promising sustainable strategy to improve concrete durability. Appl. Microbiol. Biotechnol. 100, 2993–3007 (2016).
CAS  Article  Google Scholar 

34.
Mondal, S. & Ghosh, A. Investigation into the optimal bacterial concentration for compressive strength enhancement of microbial concrete. Constr. Build. Mater. 183, 202–214 (2018).
Article  Google Scholar 

35.
Andalib, R. et al. Optimum concentration of Bacillus megaterium for strengthening structural concrete. Constr. Build. Mater. 118, 180–193 (2016).
CAS  Article  Google Scholar 

36.
Achal, V. & Pan, X. Characterization of urease and carbonic anhydrase producing bacteria and their role in calcite precipitation. Curr. Microbiol. 62, 894–902 (2011).
CAS  Article  Google Scholar 

37.
Sharma, A. & Bhattacharya, A. Enhanced biomimetic sequestration of CO2 into CaCO3 using purified carbonic anhydrase from indigenous bacterial strains. J. Mol. Catal. B Enzym. 67, 122–128 (2010).
CAS  Article  Google Scholar 

38.
Morandeau, A., Thiéry, M. & Dangla, P. Investigation of the carbonation mechanism of CH and C–S–H in terms of kinetics, microstructure changes and moisture properties. Cem. Concr. Res. 56, 153–170 (2014).
CAS  Article  Google Scholar 

39.
Ameri, F., Shoaei, P., Bahrami, N., Vaezi, M. & Ozbakkaloglu, T. Optimum rice husk ash content and bacterial concentration in self-compacting concrete. Constr. Build. Mater. 222, 796–813 (2019).
CAS  Article  Google Scholar 

40.
Syarif, R., Rizki, I. N., Wattimena, R. K. & Chaerun, S. K. Selection of bacteria inducing calcium carbonate precipitation for self-healing concrete application. Curr. Res. Biosci. Biotechnol. 1, 26–30 (2019).
Google Scholar 

41.
SNI 15-2049-2004. Semen Portland. BSN – National Standardization Agency of Indonesia (2004).

42.
Stephen, H. & Stephen, T. Solubilities of Inorganic and Organic Compounds. Binary Systems Part 1 Vol. 1 (Pergamon Press, Oxford, 1979).
Google Scholar 

43.
ASTM C642-13. Standard test method for density, absorption, and voids in hardened concrete. Am. Society Testing Mater. https://doi.org/10.1520/C0642-13.5 (2013).
Article  Google Scholar 

44.
ISRM. Suggested methods for determining the uniaxial compressive strength and deformability of rock materials. Int. J. Rock Mech. Min. Sci. Geomech. 16, 137–140 (1979).
Google Scholar 

45.
ISRM. Suggested methods for determining tensile strength of rock materials. Int. J. Rock Mech. Min. Sci. Geomech. 15, 99–103 (1978).
Article  Google Scholar  More

1.
Hutchinson, G. E. Concluding remarks. Cold Spring Harbor Symp. Quant. Biol. 22, 415–427 (1957).
Article  Google Scholar 
2.
Nelson, J. S., Grande, T. C. & Wilson, M. V. H. Fishes of the World (Wiley, Hoboken, 2016).
Google Scholar 

3.
Song, A. M., Scholtens, J., Stephen, J., Bavinck, M. & Chuenpagdee, R. Transboundary research in fisheries. Mar. Policy 76, 8–18 (2017).
Article  Google Scholar 

4.
Fredston-Hermann, A., Gaines, S. D. & Halpern, B. S. Biogeographic constraints to marine conservation in a changing climate. Ann. N. Y. Acad. Sci. 367, 49–13 (2018).
Google Scholar 

5.
Østhagen, A. Maritime boundary disputes: what are they and why do they matter?. Mar. Policy 120, 104118 (2020).
Article  Google Scholar 

6.
United Nations. United Nations Convention on the Law of the Sea (UNCLOS)—Part V. (1986).

7.
Munro, G., Van Houtte, A. & Willmann, R. The Conservation and Management of Shared Fish Stocks: Legal and Economic Aspects. FAO Fisheries Technical Paper No. 456. Food and Agriculture Organization of the United Nations, Rome (2004).

8.
Miller, K. & Munro, G. Cooperation and Conflicts in the Management of Transboundary Fishery Resources. (Proceeding of the Second World Conference of the Second World Congress of the American; European Associations of Environmental; Resource Economics, 2002).

9.
Englander, G. Property rights and the protection of global marine resources. Nature Sustainability 2, 981–987 (2019).
Article  Google Scholar 

10.
Spijkers, J. & Boonstra, W. J. Environmental change and social conflict: the northeast Atlantic mackerel dispute. Reg. Environ. Change 17, 1835–1851 (2017).
Article  Google Scholar 

11.
Pinsky, M. L. et al. Preparing ocean governance for species on the move. Science 360, 1189–1191 (2018).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

12.
Miller, K. A., Munro, G. R., Sumaila, U. R. & Cheung, W. W. L. Governing marine fisheries in a changing climate: a game-theoretic perspective. Can J Agric Econ 61, 309–334 (2013).
Article  Google Scholar 

13.
United Nations. Agreement for the Implementation of the Provisions of the United Nations Convention on the Law of the Sea of 10 December 1982 Relating to the Conservation and Management of Straddling Fish Stocks and Highly Migratory Fish Stocks. (1995).

14.
Caddy, J. Establishing a consultative mechanism or arrangement for managing shared stocks within the jurisdiction of contiguous states. In Taking stock Defining and Managing Shared Resources (ed. Hancock, D. A.) 80–123 (Australian Society for Fish Biology, Adelaide, 1997).
Google Scholar 

15.
Teh, L. S. L. & Sumaila, U. R. Trends in global shared fisheries. Mar. Ecol. Prog. Ser. 530, 243–254 (2015).
ADS  Article  Google Scholar 

16.
Diario Oficial de la Federación (DOF). Carta Nacional Pesquera. Poder Ejecutivo—Secreataría de Agricultura, Ganadería, Desarrollo Rural, Pesca (SAGARPA). Diario Oficial de la Federación DOF, 1–268 (2018).

17.
MAP. Dictamen de Extracción No Perjudicial (DENP) de la población de “tiburón martillo” Sphyrna zygaena. Oficio N. 1038–2017-PRODUCE/DGPCHDI (Tra. N. 18254–2017). Ministerio del Ambiente, Viceministerio de Desarrollo Estratégico de los Recursos Naturales, Peru (2017).

18.
Ramesh, N., Rising, J. A. & Oremus, K. L. The small world of global marine fisheries: The cross-boundary consequences of larval dispersal. Science 364, 1192–1196 (2019).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

19.
Levin, N., Beger, M., Maina, J., McClanahan, T. & Kark, S. Evaluating the potential for transboundary management of marine biodiversity in the Western Indian Ocean. Australas. J. Environ.Manag. 25, 62–85 (2018).
Article  Google Scholar 

20.
Popova, E. et al. Ecological connectivity between the areas beyond national jurisdiction and coastal waters: safeguarding interests of coastal communities in developing countries. Mar. Policy 104, 90–102 (2019).
Article  Google Scholar 

21.
Dunn, D. C. et al. The importance of migratory connectivity for global ocean policy. Proc. R. Soc. B: Biol. Sci. 286, 20191472 (2019).
Article  Google Scholar 

22.
Kaplan, D. M. et al. Uncertainty in empirical estimates of marine larval connectivity. ICES J. Mar. Sci. 74, 1723–1734 (2016).
Article  Google Scholar 

23.
Archambault, B. et al. Adult-mediated connectivity affects inferences on population dynamics and stock assessment of nursery-dependent fish populations. Global Environ. Change 181, 198–213 (2016).
Google Scholar 

24.
Cashion, T. et al. Establishing company level fishing revenue and profit losses from fisheries: A bottom-up approach. Journals Plos.Org 13, e0207768 (2018).
Google Scholar 

25.
FAO. The State of World Fisheries and Aquaculture: Meeting the Sustainable Development Goals. 1–227 (2018).

26.
UNDP. Chile and Peru sign Landmark Agreement to Sustain world’s Largest Single Species Fishery (2016).

27.
NOAA FIsheries. Bilateral Agreement Between the United States and Russia (2019).

28.
Kleisner, K. & Pauly, D. Stock-Status Plots of Fisheries for Regional Seas. in The State of Biodiversity and Fisheries in Regional Seas (eds. Christensen, V., Lai, S., Palomares, M. L. D., Zeller, D. & Pauly, D.) 37–40 (The Fisheries Center, University of British Columbia; Fisheries Centre Research Reports, 2011).

29.
Jensen, F., Frost, H., Thogersen, T., Andersen, P. & Andersen, J. L. Game theory and fish wars: the case of the Northeast Atlantic mackerel fishery. Fisheries 172, 7–16 (2015).
Google Scholar 

30.
Munro, G. R. The management of shared fishery resources under extended jurisdiction. Mar. Resour. Econ. 3, 271–296 (2015).
Article  Google Scholar 

31.
Eide, A., Heen, K., Armstrong, C., Flaaten, O. & Vasiliev, A. Challenges and successes in the management of a shared fish stock—the case of the Russian-Norwegian barents sea cod fishery. Acta Borealia 30, 1–20 (2013).
Article  Google Scholar 

32.
Sumaila, U. R., Ninnes, C. & Oelofsen, B. Management of Shared Hake Stocks in the Benguela Marine Ecosystem. In Papers presented at the norway-fao expert consultation on the management of shared fish stocks, 143–159 (2003).

33.
Clark, C. W. Restricted Access to Common-Property Fishery Resources: A Game-Theoretic Analysis. In Dynamic optimization and mathematical economics, 117–132 (Springer, Boston, MA, 1980).

34.
Spijkers, J. et al. Global patterns of fisheries conflict: forty years of data. Global Environ. Change 57, 101921 (2019).
Article  Google Scholar 

35.
Oremus, K. L. et al. Governance challenges for tropical nations losing fish species due to climate change. Nat. Sustain. 6, 1–4 (2020).
Google Scholar 

36.
Palacios-Abrantes, J., Rashid Sumaila, U. & Cheung, W. W. L. Challenges to transboundary fisheries management in North America under climate change. Ecol. Soc. (in press).

37.
Sumaila, U. R., Palacios-Abrantes, J. & Cheung, W. W. L. Climate change, shifting threat points and the management of transboundary fish stocks. Ecol. Soc. (in press).

38.
Reygondeau, G. Current and future biogeography of marine exploited groups under climate change. In Predicting Future Oceans Sustainability of Ocean and Human Systems Amidst Global Environmental Change (eds. Cheung, W. W. L., Ota, Y. & Cisneros-Montemayor, A. M.) 87–99 (2019).

39.
Schill, S. R. et al. No reef is an island: integrating coral reef connectivity data into the design of regional-scale marine protected area networks. PLoS ONE 10, e0144199 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

40.
Perez, A. U., Schmitter-Soto, J. J., Adams, A. J. & Heyman, W. D. Connectivity mediated by seasonal bonefish (Albula vulpes) migration between the Caribbean Sea and a tropical estuary of Belize and Mexico. Environ. Biol. Fishes 102, 197–207 (2019).
Article  Google Scholar 

41.
Cisneros-Montemayor, A. M., Pauly, D., Weatherdon, L. V. & Ota, Y. A Global estimate of seafood consumption by coastal indigenous peoples. PLoS ONE 11, e0166681 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

42.
Hanich, Q. et al. Small-scale fisheries under climate change in the Pacific Islands region. Mar. Policy 88, 279–284 (2018).
Article  Google Scholar 

43.
Cabral, R. B. & Geronimo, R. C. How important are coral reefs to food security in the Philippines? Diving deeper than national aggregates and averages. Mar. Policy 91, 136–141 (2018).
Article  Google Scholar 

44.
Zeller, D. et al. Still catching attention: Sea Around Us reconstructed global catch data, their spatial expression and public accessibility. Mar. Policy 70, 145–152 (2016).
Article  Google Scholar 

45.
Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M. B. BIOMOD—a platform for ensemble forecasting of species distributions. Ecography 32, 369–373 (2009).
Article  Google Scholar 

46.
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
Article  Google Scholar 

47.
Beaugrand, G., Lenoir, S., Ibanez, F. & Manté, C. A new model to assess the probability of occurrence of a species, based on presence-only data. Mar. Ecol. Prog. Ser. 424, 175–190 (2011).
ADS  Article  Google Scholar 

48.
Asch, R. G., Cheung, W. W. L. & Reygondeau, G. Future marine ecosystem drivers, biodiversity, and fisheries maximum catch potential in Pacific Island countries and territories under climate change. Mar. Policy 88, 285–294 (2018).
Article  Google Scholar 

49.
Close, C. et al. Distribution ranges of commercial fishes and invertebrates. In Fisheries Centre Research Reports. Fishes in Databases and Ecosystems (eds. Palomares, M. D., Stergiou, K. I. & Pauly, D.) 27–37 (2006).

50.
Pauly, D. & Zeller, D. Global Atlas of Marine Fisheries 1–520 (Island Press, Washington, D.C., 2016).
Google Scholar 

51.
Kaschner, K. et al. AquaMaps: Predicted range maps for aquatic species www.aquamaps.org (2016).

52.
Pauly, D. & Zeller, D. Catch reconstructions reveal that global marine fisheries catches are higher than reported and declining. Nat. Commun. 7:10244,1–9 (2019).

53.
Pebesma, E. et al. Package sf; Simple Features for R. R ( >= 3.3.0), (2018).

54.
Tai, T. C., Cashion, T., Lam, V. W. Y. & Sumaila, U. R. Ex-vessel fish price database: disaggregating prices for low-priced species from reduction fisheries. Front. Mar. Sci. 4, 1–10 (2017).
Article  Google Scholar 

55.
Sumaila, U. R., Teh, L., Zeller, D. & Pauly, D. The global ex-vessel fish price database. In Catch Reconstructions: Concepts, Methods and Data Sources (eds. Pauly D., & Zeller, S.) www.searoundus.org (2015).

56.
Grainger, R. J. R. & Garcia, S. M. Chronicles of Marine Fishery Landings (1950 1994) Trend Analysis and Fisheries Potential (1996).

57.
Pauly, D., Hilborn, R. & Branch, T. A. Fisheries: does catch reflect abundance?. Nature 494, 303–306 (2013).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

58.
Branch, T. A. Not all fisheries will be collapsed in 2048. Mar. Policy 32, 38–39 (2008).
Article  Google Scholar 

59.
Dowle, M. et al. Package data.table; Extension of ‘data.frame‘. R ( >= 3.1.0), MPL–2.0 | file LICENSE (2019).

60.
Firke, S., Haid, C., Knight, R. & Denney, B. Package janitor; Simple tools for examining and cleaning dirty data. R ( >= 3.1.2), (2018).

61.
Ram, K., Wickham, H., Richards, C. & Baggett, A. Package wesanderson; A Wes Anderson Palette Generator. R ( >= 3.0), MIT + file LICENSE (2018).

62.
Boettiger, C., Chamberlain, S., Lang, D. T. & Wainwright, P. Package rfishbase; R Interface to ’FishBase’. R ( >= 3.0), (2019).

63.
Bengtsson, H., Jacobson, A. & Riedy, J. Package R.matlab: Read and Write MAT Files and Call MATLAB from Within R. R ( 2.14.0), LGPL–2.1 | LGPL–3 (2018).

64.
Pebesma, E. et al. Package sp; Classes and methods for Spatial Data. R ( 3.0.0), GPL–2 | GPL–3 (2019).

65.
Wickham, H. Package tidyverse; Easily Install and Load the ’Tidyverse’. R (3.5.0), MIT + file LICENSE (2017).

66.
De Queiroz, G. et al. Package tidytext; Text Mining using ’dplyr’, ’ggplot2’, and Other Tidy Tools. R ( 2.10), MIT (2019).

67.
Zeileis, A., Grothendieck, G., Ryan, J. A., Ulrich, J. M. & Andrews, F. Package zoo; S3 Infrastructure for Regular and Irregular Time Series (Z’s Ordered Observations). R ( >= 3.1.0), GPL–2 | GPL–3 (2019).

68.
Chambers, J. M., Freeny, A. E. & Heiberger, R. M. Analysis of Variance; Designed Experiments. In Statistical models in s (eds Chambers, J. M. & Hastie, T. J.) 145–193 (Routledge, London, 1992).
Google Scholar 

69.
Krzanowski, W. J. Principles of Multivariate Analysis (Oxford University Press, Oxford, 1990).
Google Scholar 

70.
Hollander, M. & Wolfe, D. A. Nonparametric Statistical Methods (Wiley, Hoboken, 2013).
Google Scholar 

71.
Moore, B. R. et al. Defining the stock structures of key commercial tunas in the Pacific Ocean I: current knowledge and main uncertainties. Global Environ. Change 230, 105525 (2020).
Google Scholar 

72.
Sepulveda, C. A., Wang, M., Aalbers, S. A. & Alvarado-Bremer, J. R. Insights into the horizontal movements, migration patterns, and stock affiliation of California swordfish. Fish. Oceanogr. 29, 152–168 (2019).
Article  Google Scholar 

73.
Vandeperre, F. et al. Movements of Blue Sharks (Prionace glauca) across Their Life History. PLoS ONE 9, e103538–e103614 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

74.
Chavez, F. P., Ryan, J., Lluch-Cota, S. E. & Niquen, C. M. From anchovies to sardines and back: multidecadal change in the Pacific Ocean. Science 299, 217–221 (2003).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar  More

1.
Fox, L. R. Cannibalism in natural populations. Annu. Rev. Ecol. Syst. 6, 87–106 (1975).
Article  Google Scholar 
2.
Polis, G. A. The evolution and dynamics of intraspecific predation. Annu. Rev. Ecol. Evol. Syst. 12, 225–251 (1981).
Article  Google Scholar 

3.
Elgar, M. A. & Crespi, B. J. Ecology and evolution of cannibalism. In Cannibalism: ecology and evolution among diverse taxa (eds Elgar, M. A. & Crespi, B. J.) 1–12 (Oxford University Press, Oxford, 1992).
Google Scholar 

4.
Richardson, M. L., Mitchell, R. F., Reagel, P. F. & Hanks, L. M. Causes and consequences of cannibalism in noncarnivorous insects. Annu. Rev. Entomol. 55, 39–53 (2010).
CAS  PubMed  Article  Google Scholar 

5.
Vilaça, A. Relations between funerary cannibalism and warfare cannibalism: The question of predation. Ethnos 65, 83–106 (2000).
Article  Google Scholar 

6.
Lopez-Riquelme, G. O. & Fanjul-Moles, M. L. The funeral ways of social insects. Social strategies for corpse disposal. Trends Entomol. 9, 71–129 (2013).
Google Scholar 

7.
Walls, S. C. & Roudebush, R. E. Reduced aggression toward siblings as evidence of kin recognition in cannibalistic salamanders. Am. Nat 138, 1027–1038 (1991).
Article  Google Scholar 

8.
Pfennig, D. W. Cannibalistic tadpoles that pose the greatest threat to kin are most likely to discriminate kin. Proc. R. Soc. Lond. B 266, 57–61 (1999).
Article  Google Scholar 

9.
Bilde, T. & Lubin, Y. Kin recognition and cannibalism in a subsocial spider. J. Evolut. Biol. 14, 959–966 (2001).
Article  Google Scholar 

10.
Santana, A. F. K., Roselino, A. C., Cappelari, F. A. & Zucoloto, F. S. Cannibalism in insects. In Insect bioecology and nutrition for integrated pest management (eds Panizzi, A. R. & Parra, J. R. P.) 177–190 (CRC Press, Boca Raton, 2012).
Google Scholar 

11.
Hölldobler, B. & Wilson, E. O. The ants (The Belknap Press of Harvard University, London, 1990).
Google Scholar 

12.
Schmickl, T. & Crailsheim, K. Cannibalism and early capping: strategy of honeybee colonies in times of experimental pollen shortage. J. Comput. Physiol. A 187, 541–547 (2001).
CAS  Article  Google Scholar 

13.
Sun, Q. & Zhou, X. Corpse management in social insects. Int. J. Biol. Sci. 9, 313–321 (2013).
PubMed  PubMed Central  Article  Google Scholar 

14.
Davis, H. E., Meconcelli, S., Rudek, R. & McMahon, D. P. Termites shape their collective behavioural response based on stage of infection. Sci. Rep. 8, 14433. https://doi.org/10.1038/s41598-018-32721-7 (2018).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

15.
Mabelis, A. A. Wood ant wars: the relationship between aggression and predation in the red wood ant (Formica polyctena Först.). Neth. J. Zool. 29, 451–620 (1979).
Article  Google Scholar 

16.
Driessen, G. J. J., Van Raalte, ATh. & De Bruyn, G. Cannibalism in the red wood ant, Formica polyctena (Hymenoptera: Formicidae). Oecologia 63, 13–22 (1984).
ADS  PubMed  Article  Google Scholar 

17.
Yao, M. et al. The ancient chemistry of avoiding risks of predation and disease. Evol. Biol. 36, 267–281 (2009).
Article  Google Scholar 

18.
Visscher, P. K. The honey bee way of death: Necrophoric behaviour in Apis mellifera colonies. Anim. Behav. 31, 1070–1076 (1983).
Article  Google Scholar 

19.
Oi, D. H. & Pereira, R. M. Ant behavior and microbial pathogens (Hymenoptera: Formicidae). Florida Entomol. 76, 63–74 (1993).
Article  Google Scholar 

20.
Nazzi, F., Della Vedova, G. & D’Agaro, M. A semiochemical from brood cells infested by Varroa destructor triggers hygienic behaviour in Apis mellifera. Apidologie 35, 65–70 (2004).
CAS  Article  Google Scholar 

21.
Renucci, M., Tirrard, A. & Provost, E. Complex undertaking behavior in Temnothorax lichtensteini ant colonies: From corpse-burying behavior to necrophoric behavior. Insect. Soc. 58, 9–16 (2011).
Article  Google Scholar 

22.
Diez, L., Le Borgne, H., Lejeune, P. & Detrain, C. Who brings out the dead? Necrophoresis in the red ant Myrmica rubra. Anim. Behav. 6, 1259–1264 (2013).
Article  Google Scholar 

23.
Baracchi, D., Fadda, A. & Turillazzi, S. Evidence for antiseptic behaviour towards sick adult bees in honey bee colonies. J. Insect. Physiol. 58, 1589–1596 (2012).
CAS  PubMed  Article  Google Scholar 

24.
Pull, Ch. D. et al. Destructive disinfection of infected brood prevents systemic disease spread in ant colonies. eLife 7, e32073. https://doi.org/10.7554/eLife.32073 (2018).
Article  PubMed  PubMed Central  Google Scholar 

25.
Leclerc, J.-B. & Detrain, C. Ants detect but do not discriminate diseased workers within their nest. Sci. Nat. 103, 70. https://doi.org/10.1007/s00114-016-1394-8 (2016).
CAS  Article  Google Scholar 

26.
Williams, T. & Hernandez, O. Costs of cannibalism in the presence of an iridovirus pathogen of Spodoptera frugiperda. Ecol. Entomol. 31, 106–113 (2006).
Article  Google Scholar 

27.
Rudolf, V. H. W. & Antonovics, J. Disease transmission by cannibalism: rare event or common occurrence?. Proc. R. Soc. Lond. B 274, 1205–1210 (2007).
Google Scholar 

28.
Sadeh, A. & Rosenheim, J. A. Cannibalism amplifies the spread of vertically transmitted pathogens. Ecology 97, 1994–2002 (2016).
PubMed  Article  Google Scholar 

29.
Claessen, D., de Roos, A. M. & Persson, L. Population dynamic theory of size-dependent cannibalism. Proc. R. Soc. Lond. B 271, 333–340 (2004).
Article  Google Scholar 

30.
Pfennig, D. W., Ho, S. G. & Hoffman, E. A. Pathogen transmission as a selective force against cannibalism. Anim. Behav. 55, 1255–1261 (1998).
CAS  PubMed  Article  Google Scholar 

31.
Loreto, R. G. & Hughes, D. P. Disease in the society: infectious cadavers result in collapse of ant sub-colonies. PLoS ONE 11, e0160820 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

32.
Hughes, W. H., Eilenberg, J. & Boomsmal, J. J. Trade-offs in group living: Transmission and disease resistance in leaf-cutting ants. Proc. R. Soc. Lond. B 269, 1811–1819 (2002).
Article  Google Scholar 

33.
Cremer, S. & Sixt, M. Analogies in the evolution of individual and social immunity. Proc. R. Soc. Lond. B 364, 129–142 (2009).
Google Scholar 

34.
Konrad, M. et al. Social transfer of pathogenic fungus promotes active immunisation in ant colonies. PLoS ONE 10, 1–15 (2012).
Google Scholar 

35.
Liu, L., Ganghua, L., Pengdong, S., Chaoliang, L. & Quiying, H. Experimental verification and molecular basis of active immunization against fungal pathogens in termites. Sci. Rep. 5, 15106. https://doi.org/10.1038/srep15106 (2015).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

36.
Marikovsky, P. I. On some features of behaviour of the ants Formica rufa L. infected with fungus disease. Insect. Soc. 2, 173–179 (1962).
Article  Google Scholar 

37.
Rutkowski, T. et al. Ants trapped for years in an old bunker; survival by cannibalism and eventual escape. J. Hymenopt. Res. 72, 177–184 (2019).
Article  Google Scholar 

38.
Seifert, B. Die Ameisen Mittel- und Nordeuropas (Lutra-Verlags-und Vertriebsgesellschaft, Görlitz, 2007).
Google Scholar 

39.
Czechowski, W., Radchenko, A., Czechowska, W. & Vepsäläinen, K. The ants of Poland with reference to the myrmecofauna of Europe. Fauna Poloniae (n.s.) 4. (Natura Optima Dux Foundation, 2012).

40.
Meyling, N. V. & Eilenberg, J. Ecology of the entomopathogenic fungi Beauveria bassiana and Metarhizium anisopliae in temperate agroecosystems: Potential for conservation biological control. Biol. Control 43, 145–155 (2007).
Article  Google Scholar 

41.
Reber, A. & Chapuisat, M. Diversity, prevalence and virulence of fungal entomopathogens in colonies of the ant Formica selysi. Insect. Soc. 59, 231–239 (2012).
Article  Google Scholar 

42.
Hajek, A. E. & St. Leger, R. J. Interactions between fungal pathogens and insect hosts. Annu. Rev. Entomol. 39, 293–322 (1994).
Article  Google Scholar 

43.
Maák, I. et al. Cues or meaningless objects? Differential responses of the ant Formica cinerea to corpses of competitors and enslavers. Anim. Behav. 91, 53–59 (2014).
Article  Google Scholar 

44.
Csata, E. & Dussutour, A. Nutrient regulation in ants (Hymenoptera: Formicidae): A review. Myrmecol. News 29, 111–124 (2019).
Google Scholar 

45.
Nonacs, P. Death in the distance: Mortality risk as information for foraging ants. Behaviour 112, 23–35 (1990).
Article  Google Scholar 

46.
Roces, F. & Núṅez, J. A. Information about food quality influences load-size selection in recruited leaf-cutting ants. Anim. Behav. 45, 135–143 (1993).
Article  Google Scholar 

47.
Song, D., Hu, X. P. & Su, N.-Y. Survivorship, cannibalism, body weight loss, necrophagy, and entombement in laboratory groups of the Formosan subterranean termite, Coptotermes formosanus under starvation (Isoptera: Rhinotermitidae). Sociobiology 47, 27–39 (2006).
Google Scholar 

48.
Heifig, I., Lima, J. T., Janei, V. & Costa-Leonardo, A. M. Effects of group size and starvation on survival of the Asian subterranean termite Coptotermes gestroi (Isoptera: Rhinotermitidae). Austral Entomol. 57, 279–284 (2017).
Article  Google Scholar 

49.
Pompilio, L., Kacelnik, A. & Behmer, S. T. State-dependent learned valuation drives choice in an invertebrate. Science 311, 1613–1615 (2006).
ADS  CAS  PubMed  Article  Google Scholar 

50.
Akino, T. & Yamaoka, R. Origin of oleic acid: Corpse recognition signal in the ant Formica japonica Motschlsky (Hymenoptera: Formicidae). Jpn. J. Appl. Entomol. Z. 40, 265–271 (1996).
CAS  Article  Google Scholar 

51.
Chouvenc, T., Robert, A., Sémon, E. & Bordereau, C. Burial behaviour by dealates of the termite Pseudacanthotermes spiniger (Termitidae, Macrotermitinae) induced by chemical signals from termite corpses. Insect. Soc. 59, 119–125 (2012).
Article  Google Scholar 

52.
Kok-Boon, N., Beng-Keok, Y., Kunio, T., Tsuyoshi, Y. & Chow-Yang, L. Do termites avoid carcasses? Behavioral responses depend on the nature of the carcasses. PLoS ONE 7, 1–11 (2012).
Google Scholar 

53.
Diez, L., Moquet, L. & Detrain, C. Post-mortem changes in chemical profile and their influence on corpse removal in ants. J. Chem. Ecol. 39, 1424–1432 (2013).
CAS  PubMed  Article  Google Scholar 

54.
Bignell, D. E., Roisin, Y. & Lo, N. Biology of Termites: A modern synthesis (Springer, Berlin, 2010).
Google Scholar 

55.
Dlusskij, G. M. Ants of the genus Formica (Hymenoptera, Formicidae, g. Formica) (Nauka, Moscow, 1967) (in Russian).
Google Scholar 

56.
Czechowski, W. Ants cemeteries. Przegląd Zoologiczny 20, 417–427 (1976) (in Polish with English summary).
Google Scholar 

57.
Czechowski, W. Around nest cemeteries of Myrmica schencki Em. (Hymenoptera: Formicidae): their origin and a possible significance. Pol. J. Ecol. 56, 359–363 (2008).
Google Scholar 

58.
Gibb, H. Experimental evidence for mediation of competition by habitat succession. Ecology 92, 1871–1878 (2011).
CAS  PubMed  Article  Google Scholar 

59.
Chouvenc, T., Su, N.-Y. & Elliott, M. L. Interaction between the subterranean termite Reticulitermes flavipes (Isoptera: Rhinotermitidae) and the entomopathogenic fungus Metarhizium anisopliae in foraging arenas. J. Econ. Entomol. 101, 885–893 (2008).
CAS  PubMed  Article  Google Scholar 

60.
Yanagawa, A., Yokohari, F. & Shimizu, S. The role of antennae in removing entomopathogenic fungi from cuticle of the termite Coptotermes formosanus. . J. Insect Sci. 9, 1–9 (2009).
Article  Google Scholar 

61.
Tranter, Ch., LeFevre, L., Evison, S. E. F. & Hughes, W. O. H. Threat detection: Contextual recognition and response to parasites by ants. Behav. Ecol. 26, 396–405 (2015).
Article  Google Scholar 

62.
Bonadies, E., Wcislo, W. T., Gálvez, D., Hughes, W. O. H. & Fernández-Marin, H. Hygiene defense behaviors used by a fungus-growing ant depend on the fungal pathogen stages. Insects 10, 130 (2019).
PubMed Central  Article  PubMed  Google Scholar 

63.
Simone-Finstrom, M. D. & Spivak, M. Increased resin collection after parasite challenge: A case of self-medication in honey bees?. PLoS ONE 7, e34601. https://doi.org/10.1371/journal.pone.0034601 (2012).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

64.
Brütsch, T. & Chapuisat, M. Wood ants protect their brood with tree resin. Anim. Behav. 93, 157–161 (2014).
Article  Google Scholar 

65.
Ormond, E. L., Thomas, A. P. M., Pell, J. K., Freeman, S. N. & Roy, H. E. Avoidance of a generalist entomopathogenic fungus by the ladybird Coccinella septempunctata. FEMS Microbiol. Ecol. 77, 229–237 (2011).
CAS  PubMed  Article  Google Scholar 

66.
Fernández-Marín, H., Zimmerman, J. K., Rehner, S. A. & Wcislo, W. T. Active use of the metapleural glands by ants in controlling fungal infection. Proc. R. Soc. Lond. B 273, 1689–1695 (2006).
Google Scholar 

67.
Tragust, S. et al. Ants disinfect fungus-exposed brood by oral uptake and spread of their poison. Curr. Biol. 23, 1–7 (2013).
Article  CAS  Google Scholar 

68.
Tragust, S., Herrmann, C., Häfner, J., Braasch, R., Tilgen, Ch., Hoock, M., Milidakis, M. A., Gross, R. & Feldhaar, H. Formicine ants swallow their highly acidic poison for gut microbial selection and control. bioRxiv preprint https://doi.org/10.1101/2020.02.13.947432 (2020).

69.
Cremer, S., Pull, Ch. D. & Fürst, M. A. Social immunity: emergence and evolution of colony-level disease protection. Annu. Rev. Entomol. 63, 105–123 (2018).
CAS  PubMed  Article  Google Scholar 

70.
Rosengaus, R. B., Jordan, C., Lefebvre, M. L. & Traniello, J. F. A. Pathogen alarm behavior in a termite: A new form of communication in social insects. Naturwissenschaften 86, 544–548 (1999).
ADS  CAS  PubMed  Article  Google Scholar 

71.
Hernandez-Lopez, J., Reissberger-Gallé, U., Crailsheim, K. & Schuehly, W. Cuticular hydrocarbon cues of immune-challenged workers elicit immune activation in honeybee queens. Mol. Ecol. 26, 3062–3073 (2017).
CAS  PubMed  Article  Google Scholar 

72.
Chouvenc, T. & Su, N.-Y. When subterranean termites challenge the rules of fungal epizootics. PLoS ONE 7, 84. https://doi.org/10.1371/journal.pone.0034484 (2012).
CAS  Article  Google Scholar 

73.
Csata, E., Erős, K. & Markó, B. Effects of the ectoparasitic fungus Rickia wasmannii on its ant host Myrmica scabrinodis: changes in host mortality and behavior. Insect. Soc. 61, 247–252 (2014).
Article  Google Scholar 

74.
Diez, L., Urbain, L., Lejeune, Ph. & Detrain, C. Emergency measures: adaptive response to pathogen intrusion in the ant nest. Behav. Process. 116, 80–86 (2015).
Article  Google Scholar 

75.
Qui, H.-L. et al. Differential necrophoric behaviour of the ant Solenopsis invicta towards fungal infected corpses of workers and pupae. Bull. Entomol. Res. 105, 607–614 (2015).
Article  CAS  Google Scholar 

76.
Pereira, H. & Detrain, C. Pathogen avoidance and prey discrimination in ants. R. Soc. Open Sci. 7, 191705 (2020).
ADS  PubMed  PubMed Central  Article  Google Scholar 

77.
Cremer, S., Armitage, S. A. O. & Schmid-Hempel, P. Social immunity. Curr. Biol. 17, 693–702 (2007).
Article  CAS  Google Scholar 

78.
Pull, Ch. D. & Cremer, S. Co-founding ant queens prevent disease by performing prophylactic undertaking behaviour. BMC Evol. Biol. 219, 17. https://doi.org/10.1186/s12862-017-1062-4 (2017).
Article  Google Scholar 

79.
Kramm, K. R., West, D. F. & Rockenbach, P. G. Pathogens of termites: transfer of the entomopathogen Metarhizium anisopliae between the termites of Reticulitermes sp.. J. Invertebr. Pathol. 40, 1–6 (1982).
Article  Google Scholar 

80.
Kesäniemi, J., Koskimäki, J. J. & Jurvansuu, J. Corpse management of the invasive Argentine ant inhibits growth of pathogenic fungi. Sci. Rep. 9, 7593. https://doi.org/10.1038/s41598-019-44144-z (2019).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

81.
Greenwald, E. E., Baltiansky, L. & Feinerman, O. Individual crop loads provide local control for collective food intake in ant colonies. eLife 7, e31730 (2018).
PubMed  PubMed Central  Article  Google Scholar 

82.
Horstmann, K. Untersuchungen über den Nahrungserwerb der Waldameisen (Formica polyctena Foerster) im Eichenwald. Oecologia 5, 138–157 (1970).
ADS  PubMed  Article  Google Scholar 

83.
Bhatkar, A. & Whitcomb, W. H. Artificial diet for rearing various species of ants. Florida Entomol. 53, 229–232 (1970).
Article  Google Scholar 

84.
Choe, D. H. & Rust, M. K. Horizontal transfer of insecticides in laboratory colonies of the argentine ant (Hymenoptera: Formicidae). J. Econ. Entomol. 101, 1397–1405 (2008).
CAS  PubMed  Article  Google Scholar 

85.
Pereira, R. M. & Stimac, J. L. Transmission of Beauveria bassiana within nests of Solenopsis invicta (Hymenoptera: Formicidae) in the laboratory. Environ. Entomol. 21, 1427–1432 (1992).
Article  Google Scholar 

86.
Liu, H., Skinner, M., Parker, B. L. & Brownbridge, M. Pathogenicity of Beauveria bassiana, Metarhizium anisopliae (Deuteromycotina: Hyphomycetes), and other entomopathogenic fungi against Lygus lineolaris (Hemiptera: Miridae). J. Econ. Entomol. 95, 675–681 (2002).
PubMed  Article  Google Scholar 

87.
Loreto, R. G. & Hughes, D. P. Disease dynamics in ants. Adv. Genet. 94, 287–306. https://doi.org/10.1016/bs.adgen.2015.12.005 (2016).
CAS  Article  PubMed  Google Scholar 

88.
R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2017). https://www.R-project.org/.

89.
Therneau, T. coxme: Mixed Effects Cox Models. R package version 2.2-5. https://CRAN.R-project.org/package=coxme (2015).

90.
Bates, D., Maechler, M., Bolker, B. & Walker, S. lme4: Linear mixed-effects models using Eigen and S4. R package version 1.0-5. https://CRAN.R-project.org/package=lme4 (2013).

91.
Bartoń, K. MuMIn: Multi-model inference. R package version 1.9.13. https://CRAN.R-project.org/package=MuMIn (2013).

92.
Grueber, C. E., Nakagawa, S., Laws, R. J. & Jamieson, I. G. Multimodel inference in ecology and evolution: Challenges and solutions. J. Evol. Biol. 24, 699–711 (2011).
CAS  PubMed  Article  Google Scholar  More

The nature of the slug movement data collected in our field experiment (i.e. position on or beneath the soil surface observed at discrete moments of time) makes it possible to analyse the slug locomotory track in terms of the discrete-time random movement framework12,16,35,36. Within this framework, a curvilinear movement path is approximated by a broken line (see Fig. 2) and the movement of an individual slug is parameterized by the following frequency distributions:
1.
The distribution of the step sizes along the movement path (i.e. the distance between sequential pairs of recorded positions; Fig. 2) or the corresponding average speed

2.
The distribution of turning angle (the angle between the straight lines drawn between sequential pairs of recorded positions; Fig. 2).

Figure 2

A sketch of animal movement path and its discretization (adapted from36). (a) The original movement path is normally curvilinear. (b) Due to the limitations of the radio-tracking technique, position of the animal is only known at certain discrete moments of time; correspondingly, the curve is approximated by a broken line. (c) The movement path as a broken line is fully described by the sequence of the step sizes (lengths) along the path, i.e. the distances travelled between any two sequential recorded positions, and the sequence of the corresponding turning angles.

Full size image

Once all the information is available, it is possible to calculate the mean squared displacement as a function of time12,37. Additionally, in case the movement consists of alternating periods of active movement and immobility (periods with no recorded displacement resulting from feeding or inactivity, hereafter referred to as“resting time”), one should also consider the distribution of the corresponding periods.
Speed, squared displacements and the straightness index
It is apparent from the data that slug movement is intermittent, with periods of locomotion interspersed between periods in which they remain motionless. Tables 1 and 2 show, for the sparse and dense releases respectively, the number of ‘active’ time intervals when the slugs were moving. Periods during which slugs were motionless are marked by the zeros in Tables 1 and 2, but all these individuals resumed their movement during the following hours, confirming that they were alive throughout the assessment period. We therefore retain the zeros in the data for the subsequent analysis.
Table 1 Slug mean speed (averaged over the whole movement path), the mean SSD (see Eqs. (3) and (5), respectively) and the straightness index in the case of sparse release for each of 17 slugs used in the experiment. Here the straightness index is calculated using Eq. (4) where the values of the step size are immediately available from our field data.
Full size table

Table 2 Slug mean speed (averaged over the whole movement path), the mean SSD (see Eqs. (3) and (5), respectively) and the straightness index in the case of dense release for each of 11 slugs used in the experiment. Here the straightness index is calculated using Eq. (4) where the values of the step size are immediately available from our field data.
Full size table

The baseline discrete-time framework considers animal position at equidistant moments of time. However, in the field experiment (as described in the previous section), time taken to locate slugs at each assessment resulted in the time interval varying between measurements (sparse release treatment: 27–87 mins; dense release treatment: 20–103 mins). The step size, i.e. the displacement during one time interval, depends in part on the duration of that interval, hence risking bias in the results. We address this issue by scaling the step size by the duration of the corresponding time interval, i.e. by considering the average speed during the step:

$$begin{aligned} v_k(i)=, & {} frac{|Delta {mathbf{r}|_k(i)}}{Delta {t}_k(i)}, quad i=1,2,ldots ,N, end{aligned}$$
(1)

where

$$begin{aligned} |Delta {mathbf{r}|_k(i)}=, & {} |mathbf{r}_k(t_i)-mathbf{r}_k(t_{i-1})|, end{aligned}$$
(2)

is the displacement of the kth slug during the ith time interval, i.e. the distance between the two sequential positions in the field. Here N is the total number of steps made by the given slug during the full period of the experiment (in our field data, for all slugs (N=10)).
For each individual slug, we then calculate the mean speed over all steps along the movement path:

$$begin{aligned} _k=, & {} frac{1}{N} sum ^{N}_{i=1} v_k(i). end{aligned}$$
(3)

The results for the sparse and dense releases are shown in Tables 1 and 2, respectively; see also Fig. 3a.
The mean speed of slug movement, although being an important factor for slug dispersal, does not provide enough information about the rate at which the slug increases its linear distance from the point of release, because it does not provide information on the frequency of turning or the turning angle. In order to take that into account, we calculate the straightness index35, i.e. the ratio of the total displacement (distance between the point of release and the final position at the end of the experiment) to the total distance travelled along the path:

$$begin{aligned} s_k= & {} |mathbf{r}_k(t_N)-mathbf{r}_k(t_0)|/left( sum ^{N}_{i=1}|Delta {mathbf{r}}|_k(i) right) , end{aligned}$$
(4)

where (t_0) is the time of slug release and (t_N) is the time of the final observation. The actual distance travelled is approximated by the length of the corresponding broken line (see the dark solid line in Fig. 2).
Figure 3

(a) Slug mean spead and (b) slug mean SSD, black diamonds for the sparse release and red circles for the dense release.

Full size image

The straightness index quantifies the amount of turning (a combination of the frequency and angles of turns) along the whole movement path, i.e. over the whole observation time, but it says nothing about the rate of turning on the shorter time scale of a single ‘step’ along the movement path. To account for this, along with the mean speed we calculate the mean scaled squared displacement (SSD):

$$begin{aligned} langle sigma ^2 rangle _k=, & {} frac{1}{N} sum ^{N}_{i=1} sigma ^2_k(i) qquad text{ where }qquad sigma ^2_k(i)~=~frac{|Delta {mathbf{r}|^2_k(i)}}{Delta {t}_k(i)}, end{aligned}$$
(5)

see Tables 1 and 2 and Fig. 3b. For the same value of mean speed, a larger value of the SSD corresponds to a straighter movement on the timescale of a single step, with a smaller turning rate.
An immediate observation from visual analysis of the data shown in Fig. 3 is that both slug speed and the SSD are smaller in the case of dense release than in the sparse release. Therefore, a preliminary conclusion can be drawn that average slug movement is slower in the dense release compared to the sparse release treatment.
Turning angles
Figure 4

Frequency distribution of the turning angle in the case of (a) sparse and (b) dense releases of slugs. In calculating the turning angle, the periods of no movement were disregarded. The red curve shows the best-fitting of the data with the exponential function; see details in the text.

Full size image

We now proceed to analyse the distribution of turning angles. The histogram of different values of the angle is shown in Fig. 4. Let us consider first the case of sparse release (see Fig. 4a). We readily observe that the distribution is roughly symmetrical and has a clear maximum at (theta _T=0). The latter indicates that, on this timescale, slug movement is better described as the CRW than the standard diffusion1,16. Indeed, the standard diffusion (also known as the simple random walk) assumes that there is no bias in the movement direction, in particular there is no correlation in the movement direction in the intervals before and after the recorded position, which means that the turning angle is uniformly distributed over the whole circle. On the contrary, in the case where a correlation between the movement directions exists (hence resulting in the CRW), the distribution of the turning angle becomes hump-shaped. This is in agreement with the results of previous studies on animal movement (in particular, invertebrates12,38) as well as a general theoretical argument13.
In order to provide a more quantitative insight, we look for a functional description of the turning angle distribution using several distributions that are commonly used in movement ecology. The results are shown in Table 3. We establish that the turning angle data are best described by the exponential distribution. Somewhat unexpectedly, it outperforms the Von Mises distribution, although the latter is often regarded as a benchmark and its use has some theoretical justification1. However, the exponential distribution of the turning angle has previously been observed in movement data on some other species, e.g. on swimming invertebrates39.
Table 3 The (r^2) values for the turning angle movement data (in case of sparsely released slugs) described by different standard frequency distributions. The corresponding data are shown in Fig. 4a.
Full size table

The distribution of turning angle obtained in the case of dense release exhibit different features; see Fig. 4b. However, in this case, the distribution is not symmetric and has a clear bias towards positive values: the mean turning angle corresponding to the data shown in Fig. 4b is (langle theta _T rangle = 0.772approx pi /4). Since the slugs used in the dense release are from the same cohort as those used in the sparse release, we consider this bias as an effect of the slug density: the movement pattern of an individual slug is affected by the presence of con-specifics. We discuss possible specific mechanisms for the responsiveness to this factor in the Discussion.
An attempt to describe the turning angle data from the dense release by a symmetric distribution returns low values of (r^2) (see Suppl. Appendix A.1). However, the accuracy of data fitting comparable with the sparse release can be achieved by using an asymmetric distribution, i.e. where the corresponding function has different parameters for the positive and negative values of the angle. The results are shown in Table 4.
Table 4 The (r^2) values for the turning angle movement data (in case of densely released slugs) described by asymmetric frequency distributions. In calculating the turning angle, the periods of no movement were disregarded; the corresponding data are shown in Fig. 4b.
Full size table

The turning angle data shown in Fig. 4 were obtained using all active steps along the movement paths. However, since periods of slug movement alternate with periods of resting, it may raise the question of the relevance of the turning angle at the locations where slugs remained motionless for some time. In order to check the robustness of our results, we now repeat the analysis to calculate the turning angle differently by omitting the segments adjoined with the rest position. The results are shown in Fig. 5. In this case, a reliable fit may not be possible due to there being insufficient data. However, a visual inspection of the corresponding histograms suggests that the main properties of the turning angle distribution agree with those observed above for the bigger data set. Namely, in both cases the distribution has a clear maximum at (theta _T=0) (this is seen particularly well in the case of sparse release). In the case of sparse release the distribution is approximately symmetric, while in the case of dense release there is a clear bias towards positive values. We therefore conclude that the properties of the turning angle distribution are robust with regard to the details of its definition.
Figure 5

Frequency distribution of the turning angle in case of (a) sparse release, (b) dense release. The turning angle is only calculated for consecutive movements, i.e. if a slug does not move during a time step then its previous angle of movement is not used.

Full size image

Movement and resting times
Figure 6

Distribution of the proportion of the total time spent in movement in case of (a) sparse release and (b) dense release. The red curve shows the best-fit of the data with the normal distribution; see details in the text.

Full size image

Our field data shows that, while foraging, slugs do not move continuously but alternate periods of movement and rest; see the second column in Tables 1 and 2. Such behaviour is typical of many animal species38,40. In this section, we analyse the proportion of time that slugs spend moving, in particular to reveal the differences, if any, between the sparse and dense release.
Figure 6 shows the corresponding data where for the convenience of analysis the slugs are renumbered in a hierarchical order, so that slug 1 spends the highest proportion of time moving, slug 2 has the second highest, etc. We readily observe that the sparse release slugs tend to move more frequently than those from the dense release treatment: slugs that move for more than half of the total observation time constitute about 50% of the group in the case of sparse release but less than 30% in the case of dense release.
Figure 7

Distribution of the movement frequencies in case of (a) sparse release and (b) dense release.

Full size image

In order to make a more quantitative insight, we endeavour to describe the data using several standard distributions; see Tables 5 and 6. We find that the normal distribution performs better than others both in sparse and dense release treatments. Importantly, however, the parameters of the distribution are significantly different between the two cases; in particular, the standard deviation appears to be approximately twice as large in the case of sparse release. Arguably, it confirms the above conclusion that slugs move more frequently or for longer in the case of sparse release. Slugs released as a group tend to spend considerably more time at rest compared to the slugs released individually.
Table 5 The (r^2) values for the proportion of movement time described by different standard frequency distributions in the case of sparse release.
Full size table

To avoid a possible bias due to the different group size (17 slugs in the sparse release and 11 in the dense release), we now rearrange the data in terms of the proportion of the group that moves with a given frequency. The results are shown in Fig. 7. Although the amount of data in this case does not allow us to describe them using a particular function, the two cases clearly exhibit distributions with different properties. In particular, the average movement frequency is 0.467 for the sparse release and 0.264 for the dense release, and the corresponding variances are 0.090 and 0.065, respectively.
Table 6 The (r^2) values for the proportion of movement time described by different standard frequency distributions in the case of dense release.
Full size table

To further quantify the differences, Fig. 8 shows the number of slugs moving in each observation interval. Once again, we observe that the graph exhibits essentially different properties between the two releases. In particular, over the first interval, the majority of slugs (14 out of 17) move in the case of sparse release but none of the slugs move in the case of dense release. In the second half of the observation time (intervals 6–10) on average about 50% of slugs (8 out of 17) move in the case of sparse release but only about 25% of slugs (2–3 out of 11) move in the case of dense release.
Based on the differences between the two releases, we conclude that the presence of con-specifics is the factor that affects the distribution of slug movement time. Thus, along with the results of the previous sections, it suggests that slug movement is density dependent.
Figure 8

The number of moving slugs at each observation moment in case of (a) sparse release and (b) dense release.

Full size image More

Definitions of terms
GPP is defined here as ‘the sum of gross carbon fixation (carboxylation minus photorespiration) by autotrophic carbon-fixing tissues per unit area and time54. GPP is expressed as mass of organic carbon produced per unit area and time, over at least one year. NPP consists of all organic carbon that is fixed, but not respired over a given time period54:

$${mathrm{NPP}} = {mathrm{GPP}}-R_{mathrm{a}} = {Delta}B + L + F + H + O = {mathrm{BP}} + O$$
(3)

with all terms expressed in unit of mass of carbon per unit area and time. Ra is autotrophic respiration (composed of growth and maintenance respiration components); ΔB is the annual change in standing biomass carbon; litter production (roots, leaves and woody debris) is L; fruit production is F; the loss to herbivores is H, which was not accounted here because of the very limited number of observations available. BP is biomass production4. Symbol O represents occult, carbon flows, i.e. all other allocations of assimilated carbon, including changes in the nonstructural carbohydrate pool, root exudates, carbon subsidies to symbiotic fungi (mycorrhizae) or bacteria (e.g. nitrogen fixers), and BVOCs emissions (Supplementary Fig. 1). These ‘occult’ components are often ignored or unaccounted when estimating NPP, hence this bias is necessarily propagated into the Ra estimate when Ra is calculated as the difference between GPP and NPP55.
Estimation methods
We grouped the ‘methods’ into four categories:

biometric: direct tree stock measurements, or proxy data together with biomass expansion factors, allometric equations and the stock change as a BP component. If not otherwise stated, we assumed that the values included both above- and below-ground plant parts (n = 13 for GPP; n = 200 for NPP or BP).

micrometeorological: micrometeorological flux measurements using the eddy-covariance technique to measure CO2 flux and partitioning methods to estimate ecosystem respiration and GPP (n = 98 for GPP; n = 4 for NPP or BP).

model: model applications ranging from single mathematical equations (for canopy photosynthesis and whole-tree respiration) to more complex mechanistic process-based models to estimate GPP and Ra, with NPP as the net difference between them (n = 53 for GPP; n = 24 for NPP or BP).

scaling: upscaling of chamber-based measurements of assimilation and respiration (GPP and Ra) fluxes at the organ scale, or the entire stand (n = 73 for GPP; n = 9 for NPP or BP).

The difference between ‘scaling’ and ‘modelling’ lies in the data used. In the case of ‘scaling’ the data were derived from measurements at the site. ‘Model’ means that a dynamic process-based model was used, but with parameters calibrated and optimized at the site, based on either biometric or micrometeorological measurements.
Data selection
The data were obtained from more than 300 peer-reviewed articles (see also ref. 5), adding, merging and extending published works worldwide on CUE or BPE4,9,11,23,25,56,57. Data were extracted from the text, Tables or directly from Figures using the Unix software g3data (version 1.5.2, Jonas Frantz). In most studies, NPP, BP and GPP were estimated for the tree stand only. However, GPP estimated from CO2 flux by micrometeorological methods applies to the entire stand including ground vegetation. We therefore included only those micrometeorological studies where the forest stand was the dominant primary producer. The database contains 244 records (197 for BPE and 47 for CUE) from >100 forest sites (including planted, managed, recently burned, N-fertilized, irrigated and artificially CO2-fertilized forests; Supplementary Information, Supplementary Fig. 3 and online Materials; https://doi.org/10.5281/zenodo.3953478), representing 89 different tree species. Globally, 170 records out of the total data are from temperate sites, 51 from boreal, and 23 for tropical sites, corresponding to 79 deciduous broad-leaf (DBF), 14 evergreen broad-leaf (EBF), 132 evergreen needle-leaf (ENF) and 19 mixed-forests records (MX). The majority of the data (∼93%) cover the time-span from 1995 to 2015. We assume that when productivity data came from biometric measurements the reported NPP would have to be considered as BP because ‘occult’, nonstructural and secondary carbon compounds (e.g. BVOCs or exudates) are not included. In some cases, multiple datasets from the same site were included, covering different years or published by different authors. We considered only those values where either NPP (or BP) and GPP referred to the same year. From studies where data were available from more than 1 year, mean values across years were calculated. When the same reference for data was found in different papers or collected in different databases, where possible, we used data from the original source. When different authors described the same values for the same site, one single reference (and value) was used (in principle the oldest one). By using only commonly available environmental drivers to analyse the spatial variability in CUE and BPE, we were able to include almost all of the data that we found in the literature. We examined as potential predictors site-level effects of: average stand age (n = 204; range from 5 to ∼500 years), mean annual temperature (MAT; n = 230; range −6.5 to 27.1 °C) and total annual precipitation (TAP; n = 232; range from ∼125 to ∼3500 mm yr−1), method of determination (n = 237), geographic location (latitude and longitude; n = 241, 64°07′N to −42°52′S and 155°70′W to −173°28′E), elevation (n = 217; 5–2800 m, above sea level), leaf area index (LAI, n = 117; range from 0.4 to 13 m2 m−2), treatment (e.g.: ambient or artificially increased atmospheric CO2 concentration; n = 34), disturbance type (e.g.: fire n = 6; management n = 55), and the International Geosphere-Biosphere Programme (IGBP) vegetation classification and biomes (n = 244), as reported in the published articles (online Materials). The methods by which GPP, NPP, BP (and Ra) were determined were included as random effects in a number of possible mixed-effects linear regression models (Supplementary Table 4).
We excluded from statistical analysis all data where GPP and NPP were determined based on assumptions (e.g. data obtained using fixed fractions of NPP or Ra of GPP). In just one case GPP was estimated as the sum of upscaled Ra and NPP58; however, this study was excluded from the statistical analysis. NPP or Ra estimates obtained by process-based models (n = 23) were also not included in the statistical analysis. No information was available on prior natural disturbance events (biotic and abiotic, e.g. insect herbivore and pathogen outbreaks, and drought) that could in principle modify production efficiency, apart from fire. The occurrence of fire was reported by only a few studies59,60,61. These data were included in the database but fire, as an explanatory factor, was not considered due to the small number of samples in which it was reported (n = 6).
Data uncertainty
Uncertainties of GPP, NPP and BP data were all computed following the method based on expert judgment as described in Luyssaert et al.55. First, ‘gross’ uncertainty in GPP (gC m−2 yr−1) was calculated as 500 + 7.1 × (70−|lat|) gC m−2 yr−1 and gross uncertainties in NPP and BP (gC m−2 yr−1) were calculated as 350 + 2.9 × (70−|lat|). The absolute value of uncertainty thus decreases linearly with increasing latitude for GPP and for NPP and BP, because we assumed that the uncertainty is relative to the magnitude of the flux, which also decreases with increasing |lat|. Subsequently, as in Luyssaert et al.55, uncertainty was further reduced considering the methodology used to obtain each variable, by a method-specific factor (from 0 to 1, final uncertainty (δ) = gross uncertainty × method-specific factor). Luyssaert et al.55 reported for GPP-Micromet a method-specific factor of 0.3 (i.e. gross uncertainty is reduced by 70% for micrometeorological measurements); and for GPP-Model, 0.6. GPP-Scaling and GPP-Biometric were not explicitly considered in ref. 55 for GPP. We we used values of 0.8 and 0.3, respectively. For BP-Biometric and NPP-Micromet we used a reduction factor of 0.3; for NPP-Model, 0.6; and for NPP-Scaling (as obtained from chamber-based Ra measurements), 0.8. When GPP and/or NPP or BP methods were not known (n = 7), a factor of 1 (i.e. no reduction of uncertainty for methods used, hence maximum uncertainty) was used. The absolute uncertainties on CUE (δCUE) and BPE (δBPE) were considered as the weighted means62 by error propagation of each single variable (δNPP or δBP and δGPP) as follows:

$$delta {mathrm{CUE}} = sqrt {left( {frac{{delta {mathrm{NPP}}}}{{{mathrm{GPP}}}}} right)^2 + left( {delta {mathrm{GPP}}frac{{{mathrm{NPP}}}}{{{mathrm{GPP}}^2}}} right)^2}$$
(4)

and similarly for δBPE, by substituting NPP with BP and CUE with BPE.
Data and model selection
The CUE and BPE data were combined into a single variable, as sites for which both types of estimates existed did not show any significant differences between these entities (Supplementary Fig. 2). CUE values based on modelling were excluded (in our database we do not have BPE data from modelling). Tests showed that the CUE value was systematically higher when GPP was estimated with micrometeorological methods, compared to values based on biometric or scaling methods. Only data with complete information on CUE, MAT, age, TAP, and latitude were used. Altogether, 142 observations were selected.
In order to use the most complete information possible, a full additive model was constructed first (Eq. (1)). The method used for estimation of GPP (GPPmeth) was specified as a random effect on the intercept, as visual inspection suggested that CUE values were smaller where ‘scaling’ was used to estimate GPP compared to cases where ‘micromet’ was used to estimate GPP.
In Eq. (1) the variable ‘age’ represents the development status of the vegetation, i.e. either average age of the canopy forming trees or the period since the last major disturbance. The other three parameters represent different aspects of the climate. The absolute latitude, |lat|, was chosen as a proxy of radiation climate, i.e. day length and the seasonality of daily radiation. The term ηZGPPMeth represents the random effect on the intercept due to the different methods of estimating GPP.
These variables were not independent (Supplementary Table 1). If the different driver variables contain information that is not included in any of the other driver variables, multiple linear regression is nonetheless able to separate the individual effects. If, on the contrary, two variables exert essentially the same effect on the response variable (CUE) this can be seen in an ANOVA based model comparison. These considerations led us to the selection procedure in which we started with the full model (Eq. (1)) and compared it with all possible reduced models (Supplementary Table 2). The result of this analysis is the model with the smallest number of parameters that does not significantly differ from the full model.
We also examined, whether there were any significant interactions of predictor variables. There were not.
We used the R function lmer from the R-package lme463 to fit the mixed and ordinary multiple linear models to the data. We checked for potential problems of multicollinearity using the variance inflation factor (VIF)64. All predictors had VIF  More