Philippa Kaur

1.Forney, K. A. & Wade, P. R. Worldwide distribution and abundance of killer whales. In Whales, Whaling, and Ocean Ecosystems (eds Estes, J. A. et al.) 145–162 (University of California Press, 2006).
Google Scholar 
2.Reeves, R. R. & Mitchell, E. Distribution and seasonality of killer whales in the eastern Canadian Arctic. Rit Fiskideildar 11, 136–160 (1988).
Google Scholar 
3.Mitchell, E. & Reeves, R. R. Records of killer whales in the western North Atlantic, with emphasis on eastern Canadian waters. Rit Fiskideildar 11, 161–193 (1988).
Google Scholar 
4.Katona, S. K., Beard, J. A., Girton, P. E. & Wenzel, F. Killer whales (Orcinus orca) from the Bay of Fundy to the equator, including the Gulf of Mexico. Rit Fiskideildar 11, 205–224 (1988).
Google Scholar 
5.Higdon, J. W. & Ferguson, S. H. Sea ice declines causing punctuated change as observed with killer whale (Orcinus orca) sightings in the Hudson Bay region over the past century. Ecolog. Appl. 19, 1365–1375 (2009).Article 

Google Scholar 
6.Lawson, J. W. & Stevens, T. S. Historic and seasonal distribution patterns and abundance of killer whales (Orcinus orca) in the northwest Atlantic. J. Mar. Biol. Assoc. 94, 1253–1265 (2014).Article 

Google Scholar 
7.Jourdain, E. et al. North Atlantic killer whale Orcinus orca populations: a review of current knowledge and threats to conservation. Mammal Rev. 49, 384–400 (2019).Article 

Google Scholar 
8.Breed, G. A. et al. Sustained disruption of habitat use and behavior of narwhals in the presence of Arctic killer whales. Proc. Natl. Acad. Sci. U.S.A. 114, 2628–2633. https://doi.org/10.1073/pnas.1611707114 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
9.Matthews, C. J. D., Breed, G. A., Leblanc, B. & Ferguson, S. H. Killer whale presence drives bowhead whale selection for sea ice in Arctic seascapes of fear. Proc. Natl. Acad. Sci. U.S.A. 117, 6590–6598. https://doi.org/10.1073/pnas.1911761117 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
10.Matthews, C. J. D., Luque, S. L., Petersen, S. D., Andrews, R. D. & Ferguson, S. H. Satellite tracking of a killer whale (Orcinus orca) in the eastern Canadian Arctic documents ice avoidance and rapid, long-distance movement into the North Atlantic. Polar Biol. 34, 1091–1096 (2011).Article 

Google Scholar 
11.Lefort, K. J. et al. A review of Canadian Arctic killer whale (Orcinus orca) ecology. Can. J. Zool. 98, 245–253 (2020).Article 

Google Scholar 
12.Lien, J., Stenson, G. B. & Jones, P. W. Killer whales (Orcinus orca) in waters off Newfoundland and Labrador, 1978–1986. Rit Fiskideildar 11, 194–201 (1988).
Google Scholar 
13.Øien, N. The distribution of killer whales (Orcinus orca) in the North Atlantic based on Norwegian catches, 1938–1981, and incidental sightings, 1967–1987. Rit Fiskideildar 11, 65–78 (1988).
Google Scholar 
14.Reeves, R. R. & Mitchell, E. Killer whale sightings and takes by American pelagic whalers in the North Atlantic. Rit Fiskideildar 11, 7–23 (1988).
Google Scholar 
15.Higdon, J.W. Status of knowledge on killer whales (Orcinus orca) in the Canadian Arctic. Fisheries and Oceans Canada. Canadian Science Advisory Secretariat Research Document 2007/048 (2007)16.Young, B. G., Higdon, J. W. & Ferguson, S. H. Killer whale (Orcinus orca) photo-identification in the eastern Canadian Arctic. Polar Res. 30, 7203 (2011).Article 

Google Scholar 
17.Matthews, C. J. D., Ghazal, M., Lefort, K. J. & Inuarak, E. Epizoic barnacles on Arctic killer whales indicate residency in warm waters. Mar. Mamm. Sci. 36, 1–5 (2020).Article 

Google Scholar 
18.Hobson, K. A. Tracing origins and migration of wildlife using stable isotopes: a review. Oecologia 120, 314–326 (1999).PubMed 
Article 
ADS 

Google Scholar 
19.McMahon, K. W., Hamady, L. L. & Thorrold, S. R. A review of ecogeochemistry approaches to estimating movements of marine animals. Limnol. Oceanogr. 58, 697–714 (2013).CAS 
Article 
ADS 

Google Scholar 
20.Magozzi, S., Yool, A., Vander Zanden, H. B., Wunder, M. B. & Trueman, C. N. Using ocean models to predict spatial and temporal variation in marine carbon isotopes. Ecosphere 8(5), e01673 (2017).Article 

Google Scholar 
21.Yoshida, N. & Miyazaki, N. Oxygen isotope correlation of cetacean bone phosphate with environmental water. J. Geophys. Res. 96, 815–820 (1991).Article 
ADS 

Google Scholar 
22.Matthews, C. J. D., Longstaffe, F. J. & Ferguson, S. H. Dentine oxygen isotopes (δ18O) as a proxy for odontocete distributions and movements. Ecol. Evol. 6, 4643–4653. https://doi.org/10.1002/ece3.2238 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
23.LeGrande, A. N. & Schmidt, G. A. Global gridded data set of the oxygen isotopic composition in seawater. Geophys. Res. Lett. 33, L12604 (2006).Article 
ADS 
CAS 

Google Scholar 
24.Hui, C. A. Seawater consumption and water flux in the common dolphin Delphinus delphis. Physiol. Zool. 54, 430–440 (1981).CAS 
Article 

Google Scholar 
25.Andersen, S. H. & Nielsen, E. Exchange of water between the harbor porpoise, Phocoena phocoena, and the environment. Experientia 39, 52–53 (1983).CAS 
PubMed 
Article 

Google Scholar 
26.Kohn, M. J. Predicting animal δ18O: accounting for diet and physiological adaptation. Geochim. Cosmochim. Acta 60, 4811–4829 (1996).CAS 
Article 
ADS 

Google Scholar 
27.Longinelli, A. Oxygen isotopes in mammal bone phosphate: A new tool for paleohydrological and paleoclimatological research?. Geochim. Cosmochim. Acta 48, 385–390 (1984).CAS 
Article 
ADS 

Google Scholar 
28.Luz, B., Kolodny, Y. & Horowitz, M. Fractionation of oxygen isotopes between mammalian bone-phosphate and environmental drinking water. Geochim. Cosmochim. Acta 48, 1689–1693 (1984).CAS 
Article 
ADS 

Google Scholar 
29.Barrick, R. E., Fisher, A. G., Kolodny, Y., Luz, B. & Bohasha, D. Cetacean bone oxygen isotopes as proxies for Miocene ocean composition and glaciation. Palaios 7, 521–531 (1992).Article 
ADS 

Google Scholar 
30.Borrell, A. et al. Stable isotopes provide insight into population structure and segregation in eastern North Atlantic sperm whales. PLoS ONE 8, e82398 (2013).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
31.Vighi, M., Borrell, A. & Aguilar, A. Stable isotope analysis and fin whale subpopulation structure in the eastern North Atlantic. Mar. Mamm. Sci. 32, 535–551 (2015).Article 

Google Scholar 
32.R Core Team R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2020).33.Matthews, C. J. D., Raverty, S. A., Noren, D. P., Arragutainaq, L. & Ferguson, S. H. Ice entrapment mortality may slow expanding presence of Arctic killer whales. Polar Biol. 42, 639–644 (2019).Article 

Google Scholar 
34.Goldberg, M., Kulkarni, A. B., Young, M. & Boskey, A. Dentin: structure, composition and mineralization. Front. Biosci. 3, 711–735. https://doi.org/10.2741/e281 (2011).Article 

Google Scholar 
35.Firsching, F. H. Precipitation of silver phosphate from homogeneous solution. Analyt. Chem. 33, 873–874 (1961).CAS 
Article 

Google Scholar 
36.Stuart-Williams, H. & Schwarcz, H. P. Oxygen isotope analysis of silver orthophosphate using a reaction with bromine. Geochim. Cosmochim. Acta 59, 3837–3841 (1995).CAS 
Article 
ADS 

Google Scholar 
37.Flanagan, L. B. & Farquhar, G. D. Variation in the carbon and oxygen isotope composition of biomass and its relationship to water-use efficiency at the leaf- and ecosystem-scales in a northern Great Plains grassland. Plant Cell Environ. 37, 425–438 (2014).CAS 
PubMed 
Article 

Google Scholar 
38.Webb, E. C., White, C. D. & Longstaffe, F. J. Investigating inherent differences in isotopic composition between human bone and enamel bioapatite: implications for reconstructing residential histories. J. Archaeol. Sci. 50, 97–107 (2014).CAS 
Article 

Google Scholar 
39.Lécuyer, C., Amiot, R., Touzeau, A. & Trotter, J. Calibration of the phosphate δ18O thermometer with carbonate-water isotope fractionation equations. Chem. Geol. 347, 217–226 (2013).Article 
ADS 
CAS 

Google Scholar 
40.Snoeck, C. & Pellegrini, M. Comparing bioapatite carbonate pre-treatments for isotopic measurements: Part 1—Impact on structure and chemical composition. Chem. Geol. 417, 394–403 (2015).CAS 
Article 
ADS 

Google Scholar 
41.Pellegrini, M. & Snoeck, C. Comparing bioapatite carbonate pre-treatments for isotopic measurements: Part 2—Impact on carbon and oxygen isotope compositions. Chem. Geol. 420, 88–96 (2016).CAS 
Article 
ADS 

Google Scholar 
42.Sonnerup, R. E. et al. Reconstructing the oceanic 13C Suess effect. Global Biogeochem. Cycles 13, 857–872 (1999).CAS 
Article 
ADS 

Google Scholar 
43.Quay, P., Sonnerup, R., Westsby, T., Stutsman, J. & McNichol, A. Changes in the 13C/12C of dissolved inorganic carbon in the ocean as a tracer of anthropogenic CO2 uptake. Global Biogeochem. Cycles 17, 1004 (2003).Article 
ADS 
CAS 

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

Google Scholar 
45.Ogle, D. H., Wheeler, P. & Dinno, A. FSA: Fisheries Stock Analysis. R package version 0.8.30, https://github.com/droglenc/FSA (2020).46.Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M. & Hornik, K. cluster: Cluster Analysis Basics and Extensions. R package version 2.1.0 (2019).47.Galili, T. dendextend: an R package for visualizing, adjusting, and comparing trees of hierarchical clustering (2015).48.Spiess, A.-N. propagate: Propagation of Uncertainty. R package version 1.0–6. http://CRAN.R-project.org/package=propagate (2018).49.Schmidt, G. A., Bigg, G. R. & Rohling, E. J. “Global Seawater Oxygen-18 Database – v1.21” http://data.giss.nasa.gov/o18data/ (1999).50.Lee-Thorp, J. A., Sealy, J. C. & van der Merwe, N. J. Stable carbon isotope ratio differences between bone collagen and bone apatite, and their relationship to diet. J. Archaeol. Sci. 16, 585–599 (1989).Article 

Google Scholar 
51.Matthews, C. J. D. & Ferguson, S. H. Spatial segregation and similar trophic-level diet among eastern Canadian Arctic/north-west Atlantic killer whales inferred from bulk and compound specific isotopic analysis. J. Mar. Biol. Assoc. 94, 1343–1355 (2014).CAS 
Article 

Google Scholar 
52.Matthews, C. J. D., Lawson, J. W. & Ferguson, S. H. Amino acid δ15N patterns consistent with killer whale ecotypes in the Arctic and Northwest Atlantic. Submitted September 2020.53.Pebesma, E. J. & Bivand, R. S. Classes and methods for spatial data in R. R News 5 (2), https://cran.r-project.org/doc/Rnews/ (2005).54.Bivand, R. S., Pebesma, E. & Gomez-Rubio, V. Applied spatial data analysis with R, Second edition. Springer, NY. https://asdar-book.org/ (2013).55.Hijmans, R. J. raster: Geographic Data Analysis and Modeling. R package version 3.1–5. https://CRAN.R-project.org/package=raster (2020).56.Pierce, D. ncdf4: Interface to Unidata netCDF (Version 4 or Earlier) Format Data Files. R package version 1.17. https://CRAN.R-project.org/package=ncdf4 (2019).57.Bivand, R., Keitt, T. & Rowlingson, B. rgdal: Bindings for the ‘Geospatial’ Data Abstraction Library. R package version 1.4–8. https://CRAN.R-project.org/package=rgdal) (2019).58.Ford, J. K. B. et al. Dietary specialization in two sympatric populations of killer whales (Orcinus orca) in coastal British Columbia and adjacent waters. Can. J. Zool. 76, 1456–1471 (1998).Article 

Google Scholar 
59.Baird, R. W. & Dill, L. M. Occurrence and behaviour of transient killer whales: seasonal and pod-specific variability, foraging behavior, and prey handling. Can. J. Zool. 73, 1300–1311 (1995).Article 

Google Scholar 
60.Higdon, J. W., Hauser, D. D. W. & Ferguson, S. H. Killer whales (Orcinus orca) in the Canadian Arctic: distribution, prey items, group sizes, and seasonality. Mar. Mamm. Sci. 28, E93–E109 (2011).Article 

Google Scholar 
61.Courtiol, A. et al. Isoscape computation and inference of spatial origins with mixed models using the R package IsoriX. In Tracking Animal Migration with Stable Isotopes 2nd edn (eds Hobson, K. A. & Wassenaar, L. I.) (Academic Press, 2019).
Google Scholar 
62.Tan, F. C. & Strain, P. M. The distribution of sea ice meltwater in the eastern Canadian Arctic. J. Geophys. Res. 85, 1925–1932 (1980).CAS 
Article 
ADS 

Google Scholar 
63.Tan, F. C. & Strain, P. M. Sea ice and oxygen isotopes in Foxe Basin, Hudson Bay and Hudson Strait, Canada. J. Geophys. Res. 101, 20869–20876 (1996).CAS 
Article 
ADS 

Google Scholar 
64.Bédard, P., Hillaire-Marcel, C. & Pagé, P. 18O modelling of freshwater inputs in Baffin Bay and Canadian Arctic coastal waters. Nature 293, 287–289 (1981).Article 
ADS 

Google Scholar 
65.Bolaños-Jiménez, J. et al. Distribution, feeding habits and morphology of killer whales Orcinus orca in the Caribbean Sea. Mamm. Rev. 44, 177–189 (2014).Article 

Google Scholar 
66.Clementz, M. T. & Koch, P. L. Differentiating aquatic mammal habitat and foraging ecology with stable isotopes in tooth enamel. Oecologia 129, 461–472 (2001).PubMed 
Article 
ADS 

Google Scholar 
67.Hobson, K. A. et al. A multi-isotope (δ13C, δ15N, δ2H) feather isoscape to assign Afrotropical migrant birds to origins. Ecosphere 3(5), 44 (2012).Article 

Google Scholar 
68.Bowen, G. J., Liu, Z., Vander Zanden, H. B., Zhao, L. & Takahashi, G. Geographic assignment with stable isotopes in IsoMAP. Methods Ecol. Evol. 5, 201–206 (2014).Article 

Google Scholar 
69.Ambrose, S. H. & Norr, L. Experimental evidence for the relationship of the carbon isotope ratios of whole diet and dietary protein to those of bone collagen and carbonate. In Prehistoric Human Bone (eds Lambert, J. B. & Grupe, G.) (Springer, 1993). https://doi.org/10.1007/978-3-662-02894-0_1.
Google Scholar 
70.Tieszen, L. L. & Fagre, T. Effect of diet quality and composition on the isotopic composition of respiratory CO2, bone collagen, bioapatite, and soft tissues. In Prehistoric Human Bone (eds Lambert, J. B. & Grupe, G.) (Springer, 1993). https://doi.org/10.1007/978-3-662-02894-0_5.
Google Scholar 
71.Myrick, A. C., Yochem, P. K. & Cornell, L. H. Toward calibrating dentinal layers in captive killer whales by use of tetracycline labels. Rit Fiskideildar 11, 285–296 (1988).
Google Scholar 
72.Klevezal, G. A. Layers in the hard tissues of mammals as a record of growth rhythms of individuals. Reports of the International Whaling Commission. Special Issue 3, 89–94 (1980)73.Klevezal, G. A. Recording structures of mammals: determination of age and reconstruction of life history. A.A. Balkema, Rotterdam. xi + 274 p (1996)74.Stern, R. A., Outridge, P. M., Davis, W. J. & Stewart, R. E. A. Reconstructing lead isotope exposure histories preserved in the growth layers of walrus teeth using the SHRIMP II ion microprobe. Environ. Sci. Technol. 33, 1771–1775 (1999).CAS 
Article 
ADS 

Google Scholar 
75.Foote, A. D. et al. Genetic differentiation among North Atlantic killer whale populations. Mol. Ecol. 20, 629–641 (2011).PubMed 
Article 

Google Scholar 
76.Lefort, K. J. The demography of Canadian Arctic killer whales. M.Sc. Thesis. University of Manitoba. 100 pp. (2020)77.Foote, A. D., Newton, J., Piertney, S. B., Willerslev, E. & Gilbert, M. T. P. Ecological, morphological and genetic divergence of sympatric North Atlantic killer whale populations. Mol. Ecol. 18, 5207–5217 (2009).CAS 
PubMed 
Article 

Google Scholar 
78.Vennemann, T. W., Hegner, E., Cliff, G. & Benz, G. W. Isotopic composition of recent shark teeth as a proxy for environmental conditions. Geochim. Cosmochim. Acta 65, 1583–1599 (2001).CAS 
Article 
ADS 

Google Scholar 
79.Towers, J. R. et al. Movements and dive behavior of a toothfish-depredating killer and sperm whale. ICES J. Mar. Sci. 76, 298–311 (2019).Article 

Google Scholar 
80.Bigg, M. A., Olesiuk, P. K., Ellis, G. M., Ford, J. K. B. & Balcomb, K. C. Social organization and genealogy of resident killer whales (Orcinus orca) in the coastal waters of British Columbia and Washington State. Rep. Int. Whal. Comm. 12, 383–406 (1990).
Google Scholar 
81.Reisinger, R. R., Beukes, C., Hoelzel, R. A. & Nico de Bruyn, P. J. Kinship and association in a highly social apex predator population, killer whales at Marion Island. Behav. Ecol. 28, 750–759 (2017).Article 

Google Scholar 
82.Higdon, J. W., Westdal, K. H. & Ferguson, S. H. Distribution and abundance of killer whales (Orcinus orca) in Nunavut, Canada: an Inuit knowledge survey. J. Mar. Biol. Assoc. 94, 1293–1304 (2014).Article 

Google Scholar 
83.Wassmann, P., Duarte, C. M., Agustí, S. & Sejr, M. K. Footprints of climate change in the Arctic marine ecosystem. Glob. Change Biol. 17, 1235–1249 (2011).Article 
ADS 

Google Scholar 
84.Clementz, M. T., Fordyce, R. E., Peek, S. L. & Fox, D. L. Ancient marine isoscapes and isotopic evidence of bulk-feeding by Oligocene cetaceans. Palaeogeogr. Palaeoclimatol. Palaeoecol. 400, 28–40 (2014).Article 

Google Scholar  More

1.Elton, C. S. Animal Ecology (Sidgwick and Jackson, 1927).
Google Scholar 
2.Curio, E. The Ethology of Predation (Springer, 1976).
Google Scholar 
3.Stephens, D. W., Brown, J. S. & Ydenberg, R. C. Foraging: Behavior and Ecology (The University of Chicago Press, 2007).
Google Scholar 
4.Holling, C. S. The components of predation as revealed by a study of small mammal predation of the European pine sawfly. Can. Entomol. 91, 293–320 (1959).Article 

Google Scholar 
5.Hassell, M. P. & Varley, G. C. New inductive population model for insect parasites and its bearing on biological control. Nature 223, 1133–1137 (1969).CAS 
PubMed 
Article 
ADS 

Google Scholar 
6.Beddington, J. R. Mutual interference between parasites or predators and its effect on searching efficiency. J. Anim. Ecol. 44, 331–340 (1975).Article 

Google Scholar 
7.DeAngelis, D. L., Goldstein, R. A. & O’Neill, R. V. A model for tropic interaction. Ecology 56, 881–892 (1975).Article 

Google Scholar 
8.Stephens, D. W. & Krebs, J. R. Foraging Theory (Princeton University Press, 1986).
Google Scholar 
9.Murdoch, W. W., Avery, S. & Smyth, M. E. B. Switching in predatory fish. Ecology 56, 1094–1105 (1975).Article 

Google Scholar 
10.Akre, B. G. & Johnson, D. M. Switching and sigmoid functional response curves by damselfly naiads with alternative prey available. J. Anim. Ecol. 48, 703–720 (1979).Article 

Google Scholar 
11.Benhadi-Marín, J., Pereira, J. A., Sousa, J. P. & Santos, S. A. P. Functional responses of three guilds of spiders: comparing single- and multiprey approaches. Ann. Appl. Biol. 175, 202–214 (2019).Article 

Google Scholar 
12.Tschanz, B., Bersier, L. F. & Bacher, S. Functional responses: a question of alternative prey and predator density. Ecology 88, 1300–1308 (2007).PubMed 
Article 

Google Scholar 
13.Sih, A. & Christensen, B. Optimal diet theory: when does it work, and when and why does it fail?. Anim. Behav. 61, 379–390 (2001).Article 

Google Scholar 
14.Nakano, S., Fausch, K. D. & Kitano, S. Flexible niche partitioning via a foraging mode shift: a proposed mechanism for coexistence in stream-dwelling charrs. J. Anim. Ecol. 68, 1079–1092 (1999).Article 

Google Scholar 
15.Kullberg, C. Strategy of the Pygmy Owl while hunting avian and mammalian prey. Ornis Fenn. 72, 72–78 (1995).
Google Scholar 
16.Oaten, A. & Murdoch, W. W. Switching, functional response, and stability in predator-prey systems. Am. Nat. 109, 299–318 (1975).Article 

Google Scholar 
17.Abrams, P. A. The adaptive dynamics of consumer choice. Am. Nat. 153, 83–97 (1999).PubMed 
Article 

Google Scholar 
18.Abrams, P. A. & Kawecki, T. J. Adaptive host preference and the dynamics of host–parasitoid interactions. Theor. Popul. Biol. 56, 307–324 (1999).CAS 
PubMed 
MATH 
Article 

Google Scholar 
19.van Baleen, M., Krivan, V., van Rijn, P. & Sabelis, M. Alternative food, switching predators and the persistence of predator-prey systems. Am. Nat. 157, 512–524 (2001).Article 

Google Scholar 
20.Formanowicz, D. R. & Bradley, P. J. Fluctuations in prey density: effects on the foraging tactics of scolopendrid centipedes. Anim. Behav. 35, 453–461 (1987).Article 

Google Scholar 
21.Hirvonen, H. Shifts in foraging tactics of larval damselflies: effects of prey density. Oikos 86, 443–452 (1999).Article 

Google Scholar 
22.Hassell, M. P. The Dynamics of Arthropod Predator–Prey Systems (Princeton University Press, 1978).
Google Scholar 
23.Arditi, R. & Akçakaya, H. R. Underestimation of mutual interference of predators. Oecologia 83, 358–361 (1990).CAS 
PubMed 
Article 
ADS 

Google Scholar 
24.Abrams, P. A. & Ginzburg, L. R. The nature of predation: prey dependent, ratio dependent or neither?. Trends Ecol. Evol. 15, 337–341 (2000).CAS 
PubMed 
Article 

Google Scholar 
25.Arditi, R. & Ginzburg, L. R. How Species Interact: Altering the Standard View of Trophic Ecology (Oxford University Press, 2012).
Google Scholar 
26.Chan, K. et al. Improving the assessment of predator functional responses by considering alternate prey and predator interactions. Ecology 98, 1787–1796 (2017).CAS 
PubMed 
Article 

Google Scholar 
27.Tyutyunov, Y. V. & Titova, L. I. From Lotka-Volterra to Arditi-Ginzbug: 90 years of evolving trophic functions. Biol. Bull. Rev. 10, 167–185 (2020).Article 

Google Scholar 
28.Novak, M. & Stouffer, D. B. Systematic bias in studies of consumer functional responses. Ecol. Lett. 24, 580–593 (2020).PubMed 
Article 

Google Scholar 
29.Schenk, D., Bersier, L. F. & Bacher, S. An experimental test of the nature of predation: neither prey- nor ratio-dependent. J. Anim. Ecol. 74, 86–91 (2005).Article 

Google Scholar 
30.Hossie, T. J. & Murray, D. L. Spatial arrangement of prey affects the shape of ratio-dependent functional responses in strongly antagonistic predators. Ecology 97, 834–841 (2016).PubMed 
Article 

Google Scholar 
31.Pulliam, H. R. On the theory of optimal diets. Am. Nat. 108, 59–74 (1974).Article 

Google Scholar 
32.Charnov, E. L. Optimal foraging: attack strategy of a mantid. Am. Nat. 110, 141–151 (1976).Article 

Google Scholar 
33.Baudrot, V., Perasso, A., Fritsch, C., Giraudoux, P. & Raoul, F. The adaptation of generalist predators’ diet in a multi-prey context: insights from new functional responses. Ecology 97, 1832–1841 (2016).PubMed 
Article 

Google Scholar 
34.Palma, L., Beja, P., Pais, M. & Da Fonseca, L. C. Why do raptors take domestic prey? The case of Bonelli’s eagles and pigeons. J. Appl. Ecol. 43, 1075–1086 (2006).Article 

Google Scholar 
35.Hossie, T. J. & Murray, D. L. You can’t run but you can hide: refuge use in frog tadpoles elicits density-dependent predation by dragonfly larvae. Oecologia 163, 395–404 (2010).PubMed 
Article 
ADS 

Google Scholar 
36.Hossie, T. J. & Murray, D. L. Assessing behavioural and morphological responses of frog tadpoles to temporal variability in predation risk. J. Zool. 288, 275–282 (2012).Article 

Google Scholar 
37.Relyea, R. A. Morphological and behavioral plasticity of larval anurans in response to different predators. Ecology 82, 541–554 (2001).Article 

Google Scholar 
38.Hossie, T. J., Landolt, K. & Murray, D. L. Determinants and co-expression of anti-predator responses in amphibian tadpoles: a meta-analysis. Oikos 126, 20. https://doi.org/10.1111/oik.03305 (2017).Article 

Google Scholar 
39.Relyea, R. A. The relationship between predation risk and antipredator responses in larval anurans. Ecology 82, 541–554 (2001).Article 

Google Scholar 
40.Shine, R. The ecological impact of invasive cane toads (Bufo marinus) in Australia. Quart. Rev. Biol. 85, 253–291 (2010).PubMed 
Article 

Google Scholar 
41.Üveges, B. et al. Age- and environment-dependent changes in chemical defences of larval and post-metamorphic toads. BMC Evol. Biol. 17, 137 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
42.Jeschke, J. M. Density-dependent effect of prey defences and predator offences. J. Theor. Biol. 242, 900–907 (2006).MathSciNet 
PubMed 
MATH 
Article 

Google Scholar 
43.Holt, R. D. Predation, apparent competition, and the structure of prey communities. Theor. Popul. Biol. 12, 197–229 (1977).MathSciNet 
CAS 
PubMed 
Article 

Google Scholar 
44.Chaneton, E. J. & Bonsall, M. B. Enemy-mediated apparent competition: empirical patterns and the evidence. Oikos 88, 380–394 (2000).Article 

Google Scholar 
45.Holt, R. D. & Kotler, B. P. Short-term apparent competition. Am. Nat. 130, 412–430 (1987).Article 

Google Scholar 
46.Abrams, P. A. Effect of increased productivity on the abundances of trophic levels. Am. Nat. 141, 351–371 (1993).Article 

Google Scholar 
47.Jara, F. G. & Perotti, M. G. Toad tadpole responses to predator risk: ontogenetic change between constitutive and inducible defenses. J. Herpetol. 43, 82–88 (2009).Article 

Google Scholar 
48.Murdoch, W. W. Switching in general predators: experiments on predator specificity and stability of prey populations. Ecol. Monogr. 39, 335–354 (1969).Article 

Google Scholar 
49.Chesson, P. L. Variable predators and switching behavior. Theor. Popul. Biol. 26, 1–26 (1984).MathSciNet 
MATH 
Article 

Google Scholar 
50.Gende, S. M., Quinn, T. P. & Willson, M. F. Consumption choice by bears feeding on salmon. Oecologia 127, 372–382 (2001).CAS 
PubMed 
Article 
ADS 

Google Scholar 
51.Skelhorn, J. & Rowe, C. Predator avoidance learning of prey with secreted or stored defences and the evolution of insect defences. Anim. Behav. 72, 827–834 (2006).Article 

Google Scholar 
52.Vucetich, J. A., Vucetich, L. M. & Peterson, R. O. The causes and consequences of partial prey consumption by wolves preying on moose. Behav. Ecol. Sociobiol. 66, 295–303 (2012).Article 

Google Scholar 
53.Sih, A. Optimal foraging: partial consumption of prey. Am. Nat. 116, 281–290 (1980).Article 

Google Scholar 
54.Lucas, J. R. & Grafen, A. Partial prey consumption by ambush predators. Theor. Popul. Biol. 113, 455–473 (1985).MathSciNet 
Article 

Google Scholar 
55.Halliday, D. C. et al. Cane toad toxicity: an assessment of extracts from early developmental stages and adult tissues using MDCK cell culture. Toxicon 53, 385–391 (2009).CAS 
PubMed 
Article 

Google Scholar 
56.Toledo, R. C. & Jared, C. Cutaneous granular glands and amphibian venoms. Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 111, 1–29 (1995).Article 

Google Scholar 
57.Parrott, M. L., Doody, J. S., McHenry, C. & Clulow, S. Eat your heart out: choice and handling of novel toxic prey by predatory water rats. Aust. Mammal. 42, 235–239 (2019).Article 

Google Scholar 
58.Ruxton, G. D., Allen, W. L., Sherratt, T. N. & Speed, M. P. Avoiding Attack: The Evolutionary Ecology of Crypsis, Aposematism, and Mimicry 2nd edn. (Oxford University Press, 2018).
Google Scholar 
59.Sherratt, T. N. The optimal strategy for sampling unfamiliar prey. Evolution 65, 2114–2025 (2011).Article 

Google Scholar 
60.Skelhorn, J. & Rowe, C. Predators’ toxin burdens influence their strategic decisions to eat toxic prey. Curr. Biol. 17, 1479–1483 (2007).CAS 
PubMed 
Article 

Google Scholar 
61.Barnett, C. A., Skelhorn, J., Bateson, M. & Rowe, C. Educated predators make strategic decisions to eat defended prey according to their toxin content. Behav. Ecol. 23, 418–424 (2012).Article 

Google Scholar 
62.Nonacs, P. Foraging in a dynamic mimicry complex. Am. Nat. 126, 165–180 (1985).Article 

Google Scholar 
63.Sherratt, T. N. State-dependent risk-taking by predators in systems with defended prey. Oikos 103, 93–100 (2003).Article 

Google Scholar 
64.Jeschke, J. M., Kopp, M. & Tollrian, R. Consumer-food systems: why type I functional responses are exclusive to filter feeders. Biol. Rev. 79, 337–349 (2004).PubMed 
Article 

Google Scholar 
65.Wilbur, H. M. Density-dependent aspects of growth and metamorphosis in Bufo americanus. Ecology 58, 196–200 (1977).Article 

Google Scholar 
66.Loman, J. Density regulation in tadpoles of Rana temporaria: a full pond experiment. Ecology 85, 1611–1618 (2004).Article 

Google Scholar 
67.Yagi, K. T. & Green, D. M. Mechanisms of denity-dependent growth and survival in tadpoles of Fowler’s Toad, Anaxyrus fowleri: volume vs. abundance. Copeia 104, 942–951 (2016).Article 

Google Scholar 
68.Marshal, J. P. & Boutin, S. Power analysis of wolf-moose functional responses. J. Wild. Manag. 63, 396–402 (1999).Article 

Google Scholar 
69.Novak, M. & Stouffer, D. B. Systematic bias of consumer functional responses. Ecol. Lett. 24, 580–593 (2020).PubMed 
Article 

Google Scholar 
70.Hossie, T. J. & Murray, D. L. Effects of structural refuge and density on foraging behaviour and mortality of hungry tadpoles subject to predation risk. Ethology 117, 777–785 (2011).Article 

Google Scholar 
71.Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, 2002).
Google Scholar 
72.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2019). More

1.Preston, D. et al. Solid cancer incidence in atomic bomb survivors: 1958–1998. Radiat. Res. 168, 1–64 (2007).ADS 
CAS 
Article 

Google Scholar 
2.Neriishi, K. et al. Postoperative cataract cases among atomic bomb survivors: Radiation dose response and threshold. Radiat. Res. 168, 404–408 (2007).ADS 
CAS 
Article 

Google Scholar 
3.Sasaki, H., Wong, F. L., Yamada, M. & Kodama, K. The effects of aging and radiation exposure on blood pressure levels of atomic bomb survivors. J. Clin. Epidemiol. 55, 974–981 (2002).Article 

Google Scholar 
4.Shimizu, Y. et al. Radiation exposure and circulatory disease risk: Hiroshima and Nagasaki atomic bomb survivor data, 1950–2003. BMJ 340, b5349 (2010).Article 

Google Scholar 
5.Yamada, M., Naito, K., Kasagi, F., Masunari, N. & Suzuki, G. Prevalence of atherosclerosis in relation to atomic bomb radiation exposure: An RERF Adult Health Study. Int. J. Radiat. Biol. 81, 821–826 (2005).CAS 
Article 

Google Scholar 
6.Hayashi, T. et al. Evaluation of systemic markers of inflammation in atomic-bomb survivors with special reference to radiation and age effects. FASEB J. 26, 4765–4773 (2012).CAS 
Article 

Google Scholar 
7.Sun, L. et al. Dose-dependent decrease in anti-oxidant capacity of whole blood after irradiation: A novel potential marker for biodosimetry. Sci. Rep. 8, 1–8 (2018).
Google Scholar 
8.Zitka, O. et al. Redox status expressed as GSH: GSSG ratio as a marker for oxidative stress in paediatric tumour patients. Oncol. Lett. 4, 1247–1253 (2012).CAS 
Article 

Google Scholar 
9.Chen, J., Small-Howard, A., Yin, A. & Berry, M. J. The responses of Ht22 cells to oxidative stress induced by buthionine sulfoximine (BSO). BMC Neurosci. 6, 1–8 (2005).CAS 
Article 

Google Scholar 
10.Díaz-Hung, M.-L. et al. Transient glutathione depletion in the substantia nigra compacta is associated with neuroinflammation in rats. Neuroscience 335, 207–220 (2016).Article 

Google Scholar 
11.Mitchell, J. & Russo, A. The role of glutathione in radiation and drug induced cytotoxicity. Br. J. Cancer Suppl. 8, 96 (1987).CAS 
PubMed 
PubMed Central 

Google Scholar 
12.Preston, D. L., Shimizu, Y., Pierce, D. A., Suyama, A. & Mabuchi, K. Studies of mortality of atomic bomb survivors. Report 13: Solid cancer and noncancer disease mortality: 1950–1997. Radiat. Res. 160, 381–407. https://doi.org/10.1667/rr3049 (2003).13.Yamada, M., Wong, F. L., Fujiwara, S., Akahoshi, M. & Suzuki, G. Noncancer disease incidence in atomic bomb survivors, 1958–1998. Radiat. Res. 161, 622–632. https://doi.org/10.1667/rr3183 (2004).ADS 
CAS 
Article 
PubMed 

Google Scholar 
14.Stewart, F. et al. ICRP publication 118: ICRP statement on tissue reactions and early and late effects of radiation in normal tissues and organs–threshold doses for tissue reactions in a radiation protection context. Ann. ICRP 41, 1–322 (2012).CAS 
Article 

Google Scholar 
15.Stewart, F. A. et al. ICRP PUBLICATION 118: ICRP statement on tissue reactions and early and late effects of radiation in normal tissues and organs: Threshold doses for tissue reactions in a radiation protection context. Ann. ICRP 41, 1–322. https://doi.org/10.1016/j.icrp.2012.02.001 (2012).CAS 
Article 
PubMed 

Google Scholar 
16.Carey, J. W., Pinarci, E. Y., Penugonda, S., Karacal, H. & Ercal, N. In vivo inhibition of l-buthionine-(S, R)-sulfoximine-induced cataracts by a novel antioxidant, N-acetylcysteine amide. Free Radical Biol. Med. 50, 722–729 (2011).CAS 
Article 

Google Scholar 
17.Rodríguez-Gómez, I. et al. Role of sympathetic tone in BSO-induced hypertension in mice. Am. J. Hypertens. 23, 882–888 (2010).Article 

Google Scholar 
18.Rosenblat, M., Coleman, R. & Aviram, M. Increased macrophage glutathione content reduces cell-mediated oxidation of LDL and atherosclerosis in apolipoprotein E-deficient mice. Atherosclerosis 163, 17–28 (2002).CAS 
Article 

Google Scholar 
19.Rajasekaran, N. S., Sathyanarayanan, S., Devaraj, N. S. & Devaraj, H. Chronic depletion of glutathione (GSH) and minimal modification of LDL in vivo: its prevention by glutathione mono ester (GME) therapy. Biochim. Biophys. Acta (BBA)-Mol. Basis Dis. 1741, 103–112 (2005).20.Gokce, G. et al. Glutathione depletion by buthionine sulfoximine induces oxidative damage to DNA in organs of rabbits in vivo. Biochemistry 48, 4980–4987 (2009).CAS 
Article 

Google Scholar 
21.Richie, J. P., Komninou, D. & Albino, A. P. Induction of colon tumorigenesis by glutathione depletion in p53-knock-out mice. Int. J. Oncol. 30, 1539–1543 (2007).CAS 
PubMed 

Google Scholar 
22.Beatty, A. et al. Metabolite profiling reveals the glutathione biosynthetic pathway as a therapeutic target in triple-negative breast cancer. Mol. Cancer Ther. 17, 264–275 (2018).CAS 
Article 

Google Scholar 
23.Otsuki, Y. et al. Vasodilator oxyfedrine inhibits aldehyde metabolism and thereby sensitizes cancer cells to xCT-targeted therapy. Cancer Sci. 111, 127–136 (2020).CAS 
Article 

Google Scholar 
24.Rivina, L., Davoren, M. J. & Schiestl, R. H. Mouse models for radiation-induced cancers. Mutagenesis 31, 491–509. https://doi.org/10.1093/mutage/gew019 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
25.Neriishi, K., Nakashima, E. & Delongchamp, R. Persistent subclinical inflammation among A-bomb survivors. Int. J. Radiat. Biol. 77, 475–482 (2001).CAS 
Article 

Google Scholar 
26.Wong, F. L., Yamada, M., Sasaki, H., Kodama, K. & Hosoda, Y. Effects of radiation on the longitudinal trends of total serum cholesterol levels in the atomic bomb survivors. Radiat. Res. 151, 736–746 (1999).ADS 
CAS 
Article 

Google Scholar 
27.Kurokawa, Y. The late effects of atomic bomb injuries in Hiroshima and Nagasaki. Nagoya J. Med. Sci. 82, 187–202 (1955).
Google Scholar 
28.Chua, H. L. et al. Long-term hematopoietic stem cell damage in a murine model of the hematopoietic syndrome of the acute radiation syndrome. Health Phys. 103, 356–366. https://doi.org/10.1097/HP.0b013e3182666d6f (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
29.Robbins, M. E. & Zhao, W. Chronic oxidative stress and radiation-induced late normal tissue injury: A review. Int. J. Radiat. Biol. 80, 251–259. https://doi.org/10.1080/09553000410001692726 (2004).CAS 
Article 
PubMed 

Google Scholar 
30.Robbins, M. E., Zhao, W., Davis, C. S., Toyokuni, S. & Bonsib, S. M. Radiation-induced kidney injury: A role for chronic oxidative stress?. Micron 33, 133–141 (2002).CAS 
Article 

Google Scholar 
31.Kang, S. K. et al. Overexpression of extracellular superoxide dismutase protects mice from radiation-induced lung injury. Int. J. Radiat. Oncol. Biol. Phys. 57, 1056–1066. https://doi.org/10.1016/s0360-3016(03)01369-5 (2003).CAS 
Article 
PubMed 

Google Scholar 
32.Yin, Z., Yang, G., Deng, S. & Wang, Q. Oxidative stress levels and dynamic changes in mitochondrial gene expression in a radiation-induced lung injury model. J. Radiat. Res. 60, 204–214 (2019).ADS 
CAS 
Article 

Google Scholar 
33.Volkova, P. Y., Geras’kin, S. A. & Kazakova, E. A. Radiation exposure in the remote period after the Chernobyl accident caused oxidative stress and genetic effects in Scots pine populations. Sci. Rep. 7, 1–9 (2017).34.Urushihara, Y. et al. Analysis of plasma protein concentrations and enzyme activities in cattle within the ex-evacuation zone of the Fukushima Daiichi nuclear plant accident. PLoS ONE 11, e0155069 (2016).Article 

Google Scholar 
35.Malekirad, A. A. et al. Oxidative stress in radiology staff. Environ. Toxicol. Pharmacol. 20, 215–218 (2005).CAS 
Article 

Google Scholar 
36.Takabatake, M. et al. Differential effect of parity on rat mammary carcinogenesis after pre- or post-pubertal exposure to radiation. Sci Rep 8, 14325. https://doi.org/10.1038/s41598-018-32406-1 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
37.Narendran, N., Luzhna, L. & Kovalchuk, O. Sex difference of radiation response in occupational and accidental exposure. Front. Genet. 10, 260. https://doi.org/10.3389/fgene.2019.00260 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
38.Champion, C. J. & Xu, J. Redox state affects fecundity and insecticide susceptibility in Anopheles gambiae. Sci. Rep. 8, 1–11 (2018).CAS 
Article 

Google Scholar  More

1.Thresher, R. E., Colin, P. L. & Bell, L. J. Planktonic duration, distribution and population structure of western and Central Pacific Damselfishes (Pomacentridae). Copeia 420–434, 1989. https://doi.org/10.2307/1445439 (1989).Article 

Google Scholar 
2.Leis, J. M. Behaviour as input for modelling dispersal of fish larvae: Behaviour, biogeography, hydrodynamics, ontogeny, physiology and phylogeny meet hydrography. Mar. Ecol. Prog. Ser. 347, 185–194. https://doi.org/10.3354/meps06977 (2007).Article 
ADS 

Google Scholar 
3.Fisher, R., Leis, J. M., Clark, D. L. & Wilson, S. K. Critical swimming speeds of late-stage coral reef fish larvae: Variation within species, among species and between locations. Mar. Biol. 147, 1201–1212. https://doi.org/10.1007/s00227-005-0001-x (2005).Article 

Google Scholar 
4.Stobutzki, I. & Bellwood, D. Sustained swimming abilities of the late pelagic stages of coral reef fishes. Mar. Ecol. Prog. Ser. 149, 35–41. https://doi.org/10.3354/meps149035 (1997).Article 
ADS 

Google Scholar 
5.Gerlach, G., Atema, J., Kingsford, M. J., Black, K. P. & Miller-Sims, V. Smelling home can prevent dispersal of reef fish larvae. Proc. Natl. Acad. Sci. 104, 858–863. https://doi.org/10.1073/pnas.0606777104 (2007).CAS 
Article 
PubMed 
ADS 

Google Scholar 
6.Almany, G. R., Berumen, M. L., Thorrold, S. R., Planes, S. & Jones, G. P. Local Replenishment of Coral Reef fish populations in a Marine Reserve. Science 316, 742–744. https://doi.org/10.1126/science.1140597 (2007).CAS 
Article 
PubMed 
ADS 

Google Scholar 
7.Jones, G. P., Planes, S. & Thorrold, S. R. Coral Reef Fish Larvae Settle Close to Home. Curr. Biol. 15, 1314–1318. https://doi.org/10.1016/j.cub.2005.06.061 (2005).CAS 
Article 
PubMed 

Google Scholar 
8.Kingsford, M. J. et al. Sensory environments, larval abilities and local self-recruitment. Bull. Mar. Sci. 70, 309–340 (2002).
Google Scholar 
9.Mouritsen, H., Atema, J., Kingsford, M. J. & Gerlach, G. Sun compass orientation helps coral reef fish larvae return to their natal reef. PLoS ONE. https://doi.org/10.1371/journal.pone.0066039 (2013).10.Dufour, V. & Galzin, R. Colonization patterns of reef fish larvae to the lagoon at Moorea Island, French Polynesia. Mar. Ecol. Prog. Ser. 102, 143–152. https://doi.org/10.3354/meps102143 (1993).Article 
ADS 

Google Scholar 
11.Holbrook, S. & Schmitt, R. Settlement patterns and process in a coral reef damselfish: In situ nocturnal observations using infrared video. In Proceedings of the 8th International Coral Reef Symposium, Vol. 2, 1143–1148 (1997).12.Leis, J. M. & Carson-Ewart, B. M. Complex behaviour by coral-reef fish larvae in open-water and near-reef pelagic environments. Environ. Biol. Fishes 53, 259–266. https://doi.org/10.1023/A:1007424719764 (1998).Article 

Google Scholar 
13.Litsios, G. et al. Mutualism with sea anemones triggered the adaptive radiation of clownfishes. BMC Evol. Biol. 12, 212. https://doi.org/10.1186/1471-2148-12-212 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
14.Bridge, T., Scott, A. & Steinberg, D. Abundance and diversity of anemonefishes and their host sea anemones at two mesophotic sites on the Great Barrier Reef, Australia. Coral Reefs 31, 1057–1062. https://doi.org/10.1007/s00338-012-0916-x (2012).Article 
ADS 

Google Scholar 
15.Mariscal, R. N. Behavior of symbiotic fishes and sea anemones. In Winn, H. E. & Olla, B. L. (eds.) Behavior of Marine Animals, 327–360 (Springer US, 1972). https://doi.org/10.1007/978-1-4684-0910-9_4.16.Tauber, E., Last, K. S., Olive, P. J. & Kyriacou, C. P. Clock gene evolution and functional divergence. J. Biol. Rhythm. 19, 445–458. https://doi.org/10.1177/0748730404268775 (2004).CAS 
Article 

Google Scholar 
17.Emran, F., Rihel, J., Adolph, A. R. & Dowling, J. E. Zebrafish larvae lose vision at night. Proc. Natl. Acad. Sci. 107, 6034–6039. https://doi.org/10.1073/pnas.0914718107 (2010).Article 
PubMed 
ADS 

Google Scholar 
18.Cahill, G. M., Hurd, M. W. & Batchelor, M. M. Circadian rhythmicity in the locomotor activity of larval zebrafish. NeuroReport 9, 3445–3449. https://doi.org/10.1097/00001756-199810260-00020 (1998).CAS 
Article 
PubMed 

Google Scholar 
19.Ceinos, R. M. et al. Mutations in blind cavefish target the light-regulated circadian clock gene, period 2. Sci. Rep. 8, 8754. https://doi.org/10.1038/s41598-018-27080-2 (2018).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
20.Frøland Steindal, I. & Whitmore, D. Circadian clocks in fish—What have we learned so far?. Biology 8, 17. https://doi.org/10.3390/biology8010017 (2019).CAS 
Article 
PubMed Central 

Google Scholar 
21.Vatine, G., Vallone, D., Gothilf, Y. & Foulkes, N. S. It’s time to swim! Zebrafish and the circadian clock. FEBS Lett. 585, 1485–1494. https://doi.org/10.1016/j.febslet.2011.04.007 (2011).CAS 
Article 
PubMed 

Google Scholar 
22.Banaszak, A. T. & Lesser, M. P. Effects of solar ultraviolet radiation on coral reef organisms. Photochem. Photobiol. Sci. 8, 1276. https://doi.org/10.1039/b902763g (2009).CAS 
Article 
PubMed 

Google Scholar 
23.Häder, D.-P., Kumar, H. D., Smith, R. C. & Worrest, R. C. Effects of solar UV radiation on aquatic ecosystems and interactions with climate change. Photochem. Photobiol. Sci. 6, 267–285. https://doi.org/10.1039/B700020K (2007).Article 
PubMed 

Google Scholar 
24.Eckes, M., Siebeck, U., Dove, S. & Grutter, A. Ultraviolet sunscreens in reef fish mucus. Mar. Ecol. Prog. Ser. 353, 203–211. https://doi.org/10.3354/meps07210 (2008).CAS 
Article 
ADS 

Google Scholar 
25.Kienzler, A., Bony, S. & Devaux, A. DNA repair activity in fish and interest in ecotoxicology: A review. Aquat. Toxicol. 134–135, 47–56. https://doi.org/10.1016/j.aquatox.2013.03.005 (2013).CAS 
Article 
PubMed 

Google Scholar 
26.Hoogenboom, I., Daan, S., Dallinga, J. H. & Schoenmakers, M. Seasonal change in the daily timing of behaviour of the common vole, Microtus arvalis. Oecologia 61, 18–31. https://doi.org/10.1007/BF00379084 (1984).27.Tan, M. H. et al. Finding Nemo: Hybrid assembly with Oxford Nanopore and Illumina reads greatly improves the clownfish (Amphiprion ocellaris) genome assembly. GigaScience. https://doi.org/10.1093/gigascience/gix137 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
28.Cavallari, N. et al. A blind circadian clock in cavefish reveals that opsins mediate peripheral clock photoreception. PLoS Biol. https://doi.org/10.1371/journal.pbio.1001142 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
29.Vallone, D., Gondi, S. B., Whitmore, D. & Foulkes, N. S. E-box function in a period gene repressed by light. Proc. Natl. Acad. Sci. 101, 4106–4111. https://doi.org/10.1073/pnas.0305436101 (2004).CAS 
Article 
PubMed 
ADS 

Google Scholar 
30.Vatine, G. et al. Light directs Zebrafish period2 expression via conserved D and E boxes. PLOS Biol. https://doi.org/10.1371/journal.pbio.1000223 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
31.Mracek, P. et al. Regulation of per and cry Genes Reveals a Central Role for the D-Box Enhancer in Light-Dependent Gene Expression. PLOS ONE https://doi.org/10.1371/journal.pone.0051278 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
32.Zhao, H. et al. Modulation of DNA repair systems in blind cavefish during evolution in constant darkness. Curr. Biol. 28, 3229-3243.e4. https://doi.org/10.1016/j.cub.2018.08.039 (2018).CAS 
Article 
PubMed 

Google Scholar 
33.Tolimieri, N., Haine, O., Jeffs, A., McCauley, R. & Montgomery, J. Directional orientation of pomacentrid larvae to ambient reef sound. Coral Reefs 23, 184–191. https://doi.org/10.1007/s00338-004-0383-0 (2004).Article 

Google Scholar 
34.Fisher, R. & Bellwood, D. Undisturbed swimming behaviour and nocturnal activity of coral reef fish larvae. Mar. Ecol. Prog. Ser. 263, 177–188. https://doi.org/10.3354/meps263177 (2003).Article 
ADS 

Google Scholar 
35.Elliott, J. K. & Mariscal, R. N. Ontogenetic and interspecific variation in the protection of anemonefishes from sea anemones. J. Exp. Mar. Biol. Ecol. 208, 57–72. https://doi.org/10.1016/S0022-0981(96)02629-9 (1997).Article 

Google Scholar 
36.Fautin, D. G. The anemonefish symbiosis: What is known and what is not. Symbiosis 10, 23–46 (1991).
Google Scholar 
37.Di Rosa, V., Frigato, E., López-Olmeda, J. F., Sánchez-Vázquez, F. J. & Bertolucci, C. The light wavelength affects the ontogeny of clock gene expression and activity rhythms in zebrafish larvae. PLOS ONE https://doi.org/10.1371/journal.pone.0132235 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
38.Idda, M. L. et al. Chapter 3—Circadian clocks: Lessons from fish. In Kalsbeek, A., Merrow, M., Roenneberg, T. & Foster, R. G. (eds.) The Neurobiology of Circadian Timing, vol. 199 of Progress in Brain Research, 41–57, DOI: https://doi.org/10.1016/B978-0-444-59427-3.00003-4 (Elsevier, 2012).39.Patiño, M. A. L., Rodríguez-Illamola, A., Conde-Sieira, M., Soengas, J. L. & Míguez, J. M. Daily rhythmic expression patterns of Clock1a, Bmal1, and Per1 genes in retina and hypothalamus of the rainbow trout, Oncorhynchus Mykiss. Chronobiol. Int. 28, 381–389. https://doi.org/10.3109/07420528.2011.566398 (2011).CAS 
Article 
PubMed 

Google Scholar 
40.Vera, L. M. et al. Light and feeding entrainment of the molecular circadian clock in a marine teleost (Sparus aurata). Chronobiol. Int. 30, 649–661. https://doi.org/10.3109/07420528.2013.775143 (2013).CAS 
Article 
PubMed 

Google Scholar 
41.Martín-Robles, A. J., Whitmore, D., Sánchez-Vázquez, F. J., Pendón, C. & Muñoz-Cueto, J. A. Cloning, tissue expression pattern and daily rhythms of Period1, Period2, and Clock transcripts in the flatfish Senegalese sole,Solea senegalensis. J. Comp. Physiol. B 182, 673–685. https://doi.org/10.1007/s00360-012-0653-z (2012).CAS 
Article 
PubMed 

Google Scholar 
42.Park, J.-G., Park, Y.-J., Sugama, N., Kim, S.-J. & Takemura, A. Molecular cloning and daily variations of the Period gene in a reef fish Siganus guttatus. J. Comp. Physiol. A 193, 403–411. https://doi.org/10.1007/s00359-006-0194-6 (2007).CAS 
Article 

Google Scholar 
43.Martín-Robles, A. J., Isorna, E., Whitmore, D., Muñoz-Cueto, J. A. & Pendón, C. The clock gene Period3 in the nocturnal flatfish Solea senegalensis: Molecular cloning, tissue expression and daily rhythms in central areas. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 159, 7–15. https://doi.org/10.1016/j.cbpa.2011.01.015 (2011).CAS 
Article 
PubMed 

Google Scholar 
44.Whitmore, D., Foulkes, N. S., Strähle, U. & Sassone-Corsi, P. Zebrafish Clock rhythmic expression reveals independent peripheral circadian oscillators. Nat. Neurosci. 1, 701–707. https://doi.org/10.1038/3703 (1998).CAS 
Article 
PubMed 

Google Scholar 
45.Yamazaki, S. et al. Resetting central and peripheral circadian oscillators in transgenic rats. Science 288, 682–685. https://doi.org/10.1126/science.288.5466.682 (2000).CAS 
Article 
PubMed 
ADS 

Google Scholar 
46.Yagita, K. et al. Development of the circadian oscillator during differentiation of mouse embryonic stem cells in vitro. Proc. Natl. Acad. Sci. 107, 3846–3851. https://doi.org/10.1073/pnas.0913256107 (2010).Article 
PubMed 
ADS 

Google Scholar 
47.Challet, E. Minireview: Entrainment of the suprachiasmatic clockwork in diurnal and nocturnal mammals. Endocrinology 148, 5648–5655. https://doi.org/10.1210/en.2007-0804 (2007).CAS 
Article 
PubMed 

Google Scholar 
48.del Pozo, A., Montoya, A., Vera, L. M. & Sánchez-Vázquez, F. J. Daily rhythms of clock gene expression, glycaemia and digestive physiology in diurnal/nocturnal European seabass. Physiol. Behav. 106, 446–450. https://doi.org/10.1016/j.physbeh.2012.03.006 (2012).CAS 
Article 
PubMed 

Google Scholar 
49.Job, S. & Shand, J. Spectral sensitivity of larval and juvenile coral reef fishes: Implications for feeding in a variable light environment. Mar. Ecol. Prog. Ser. 214, 267–277. https://doi.org/10.3354/meps214267 (2001).Article 
ADS 

Google Scholar 
50.Buston, P. M. & García, M. B. An extraordinary life span estimate for the clown anemonefish Amphiprion percula. J. Fish Biol. 70, 1710–1719. https://doi.org/10.1111/j.1095-8649.2007.01445.x (2007).Article 

Google Scholar 
51.Godwin, J. & Fautin, D. G. Defense of host actinians by anemonefishes. Copeia 902–908, 1992. https://doi.org/10.2307/1446171 (1992).Article 

Google Scholar 
52.Roopin, M. & Chadwick, N. E. Benefits to host sea anemones from ammonia contributions of resident anemonefish. J. Exp. Mar. Biol. Ecol. 370, 27–34. https://doi.org/10.1016/j.jembe.2008.11.006 (2009).CAS 
Article 

Google Scholar 
53.Cleveland, A., Verde, E. A. & Lee, R. W. Nutritional exchange in a tropical tripartite symbiosis: Direct evidence for the transfer of nutrients from anemonefish to host anemone and zooxanthellae. Mar. Biol. 158, 589–602. https://doi.org/10.1007/s00227-010-1583-5 (2011).Article 

Google Scholar 
54.Verde, E. A., Cleveland, A. & Lee, R. W. Nutritional exchange in a tropical tripartite symbiosis II: Direct evidence for the transfer of nutrients from host anemone and zooxanthellae to anemonefish. Mar. Biol. 162, 2409–2429. https://doi.org/10.1007/s00227-015-2768-8 (2015).CAS 
Article 

Google Scholar 
55.da Silva, K. B. & Nedosyko, A. Sea Anemones and Anemonefish: A Match Made in Heaven. In Goffredo, S. & Dubinsky, Z. (eds.) The Cnidaria, Past, Present and Future, 425–438 (Springer International Publishing, 2016). https://doi.org/10.1007/978-3-319-31305-4_27.56.Vallone, D., Santoriello, C., Gondi, S. B. & Foulkes, N. S. Basic protocols for zebrafish cell lines. In Rosato, E. (ed.) Circadian Rhythms: Methods and Protocols, 429–441. https://doi.org/10.1007/978-1-59745-257-1_35 (Humana Press, 2007).57.Dekens, M. P. S., Foulkes, N. S. & Tessmar-Raible, K. Instrument design and protocol for the study of light controlled processes in aquatic organisms, and its application to examine the effect of infrared light on zebrafish. PLoS ONE 12. https://doi.org/10.1371/journal.pone.0172038 (2017).58.Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675. https://doi.org/10.1038/nmeth.2089 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
59.R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2020).60.Wickham, H. Ggplot2: Elegant Graphics for Data Analysis. Use R! (Springer, 2009).61.Thaben, P. F. & Westermark, P. O. Detecting rhythms in time series with RAIN. J. Biol. Rhythm. 29, 391–400. https://doi.org/10.1177/0748730414553029 (2014).Article 

Google Scholar 
62.Kõressaar, T. et al. Primer3_masker: Integrating masking of template sequence with primer design software. Bioinformatics 34, 1937–1938. https://doi.org/10.1093/bioinformatics/bty036 (2018).CAS 
Article 
PubMed 

Google Scholar 
63.Untergasser, A. et al. Primer3—new capabilities and interfaces. Nucleic Acids Res. 40, e115. https://doi.org/10.1093/nar/gks596 (2012).64.Kõressaar, T. & Remm, M. Enhancements and modifications of primer design program Primer3. Bioinformatics 23, 1289–1291. https://doi.org/10.1093/bioinformatics/btm091 (2007).CAS 
Article 
PubMed 

Google Scholar 
65.Ye, J. et al. Primer-BLAST: A tool to design target-specific primers for polymerase chain reaction. BMC Bioinforma 13, 134. https://doi.org/10.1186/1471-2105-13-134 (2012).CAS 
Article 

Google Scholar 
66.Wit, P. D. et al. The simple fool’s guide to population genomics via RNA-Seq: An introduction to high-throughput sequencing data analysis. Mol. Ecol. Resour. 12, 1058–1067. https://doi.org/10.1111/1755-0998.12003 (2012).CAS 
Article 
PubMed 

Google Scholar  More

Collection of crabsMale snow crabs of legal size and with hard shells were caught by the commercial SC vessel Northeastern (Opilio AS) using traditional SC pots in the NEAFC area (N 75° 49.2 E 37° 39.2). SCs were stored live onboard the vessel and subsequently delivered to Nofima’s facilities in Tromsø (N 69° 39). The crabs were caught in June, and September 2016, February, April, and December 2017 and will only be referred to by month. Upon slaughtering, data from individual crabs was obtained by recording the weight of the whole animal, clusters + claws, hemolymph, hepatopancreas, and gills (n = 56 September, n = 45 December, n = 29 February, n = 66 April, n = 50 June). Subsequently, different fractions were pooled and analysed as outlined below.Biochemical- and meat content-analysesThe biochemical analyses were performed on each month of analysis (September, December, February, April, and June) and consisted of water, protein, lipid, and ash contents. All analyses were performed on meat (i.e., main product) and the different co-products divided in the following ways: pooled internal organs (mainly hemolymph, hepatopancreas and gonads) with and without added carapace, hemolymph alone and hepatopancreas alone. Lipid class and fatty acid analyses were performed on the lipid storage organ hepatopancreas. Each biochemical analysis consisted of co-products from 10 randomly selected animals. All biochemical analyses (water-, ash-, lipid and protein-content) were determined by Toslab (9266 Tromsø, Norway), lipid classes and fatty acid identifications were performed by Biolab (5141 Fyllingsdalen, Norway). Both are commercial laboratories accredited according to ISO 17025.Water and dry matter content3–5 g of material was weighed in a marked porcelain crucible. The crucible was placed in a preheated drying cabinet at 103 °C ± 1 °C. After precisely 4 h 30 min, the crucible was allowed to cool down in a desiccator before being weighed. Water and dry matter contents were calculated according to Eqs. (1) and (2) respectively:$$Waterleft(%right)=frac{left(a-bright)}{w}times 100$$
(1)
$$Dry;matter;content left(%right)=frac{left(b-cright)}{w}times 100$$
(2)
where a = weight (g) of crucible with weighed sample; b = weight (g) of crucible with dried sample; c = weight (g) of crucible; w = weight (g) of weighed sample9.Ash content3–5 g of material was weighed in a marked porcelain crucible. The crucible was placed in a preheated muffle furnace at 550 °C ± 20 °C. After 16 h, the crucible was allowed to cool down in a desiccator before being weighed. The ash content was calculated according to Eq. (3):$$Ash left(%right)=frac{left(d-cright)}{{w}^{^{prime}}}times 100$$
(3)
where d = weight (g) of crucible with calcinated sample; c = weight (g) of crucible; w′ =  weight (g) of dry matter sample10.Fat contentThe fat in the samples was extracted with a polar solvent consisting of CHCl3, MeOH and H2O in a mixing ratio of 1:2:0.8 to give a single-phase system. 5–20 g of material was weighed into a 250 ml test tube. H2O was added so that water content plus added material corresponded to 16 ml. MeOH (40 ml) and CHCl3 (20 ml) were added. The mix was homogenized for 60 s. CHCl3 (20 ml) was again added and the mix was homogenized for 30 s H2O (20 ml) was added, and the mix was homogenized again for 30 s. The test tube was sealed and cooled in a water bath with ice. The emulsion was quickly filtered out through a small cotton ball in a funnel. The upper layer of the collected liquid consisting of MeOH and H2O was removed by suction. 5–20 ml of the remaining CHCl3 phase was transferred to a tared evaporation dish with a positive displacement pipette. The solvent was evaporated with an infrared lamp. The dish was cooled in a desiccator and weighed. The fat content was calculated according to the Eq. (4):$$Fat;content left(%right)=frac{dtimes b}{W times left(c-frac{d}{mathrm{0,92}}right)}times 100$$
(4)
where b = ml CHCl3 added; c = ml CHCl3 transferred; d = weight of fat in evaporation dish (g); 0.92 = specific gravity for fat, g/ml; w = weight (g) of the sample11.Protein contentProtein content analysis was performed with a fully automated Kjeltec 8400 (Foss Analytics, Denmark). 0.5–1 g of nitrogen free paper of previously dried sample was allowed to be digested in a digestion unit with concentrated H2SO4 (17.5 ml) and two catalyser tablets for 2 h 20 min at 420 °C. The digested liquid was transferred to the titration unit after cooling and was titrated fully automated by the equipment.Blanking was performed only with nitrogen free paper, titration with standardized HCl solution and 1% (w/w) boric acid solution containing a pH sensitive indicator. The protein content was calculated according to the Eq. (5):$$Protein;content left(%right)=frac{mathrm{14,007}times N times f times left(a-bright)}{wtimes 1000}times 100$$
(5)
where 14.007 = atomic weight of Nitrogen; N = Normality of the titration solution; f = protein factor (6.25); w = weight (g) of weighed sample; a = ml of HCl consumed for sample titration; b = ml of HCl consumed for blank titration12.
Cis-fatty acid and trans-fatty acids compositionThis method was designed to determine the fatty acid composition of marine oils and marine oil esters in relative (area-%) values, and eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) in absolute (g/100 g) values using a bonded polyglycol liquid phase in a flexible fused silica capillary column. C23:0 fatty acid was used as an internal standard.For methyl esterification of oil samples for the analysis of cis-fatty acids, two drops of the oil sample were weighed and transferred to a 15 ml test tube with a screw cap. The test amount should be between 20 and 35 mg. Exactly 900 µl of the internal standard solution was added. The solvent was evaporated by nitrogen on a heating block at 80 °C. NaOH solution (1.5 ml, 0.5 N) was added. The mix was incubated in boiling water for 5 min and cooled in cold water. A 15% BF3-solution (2 ml) was added. The mix was again incubated in boiling water for 30 min and cooled to 30–40 °C. Isooctane (1 ml) was added. A cork was set, and the mixture grated with gentle movements for 30 s. Saturated NaCl (5 ml) was added immediately. A cork was set, and the mixture was again grated with gentle movements for 30 s. The isooctane phase was transferred to a test tube with a lid. The test tube was centrifuged at 3000 rpm if phase separation was difficult to achieve. Another 1 ml of isooctane was added to the test tube. A cork was set, and the mixture grated with gentle movements for 30 s. The isooctane phase was transferred to the same test tube with a lid. 5 µl of this transferred isooctane phase was diluted into a new test tube with 1 ml of isooctane.The procedure for methyl esterification of trans-fatty acids was identical to that for methyl esterification of cis-fatty acids, with one exception: incubation time after the addition of BF3-solution was 5 min.For the GC analysis an analytical capillary column (60 m × 0.25 mm × 0.25 µm-70% Cyanopropyl Polysilphenylene-siloxane) was used. (P/N: 054623, manufacturer: SGE). During the analysis the gas valves on the wall panel for synthetic air and hydrogen were left open.Two different GC programs were used for the analysis (Table 1).Table 1 GC-program for analysis of fatty acids.Full size tableThe identification of the different fatty acid methyl esters was performed by comparing the pattern and relative retention times by chromatography of different standards. Empirical response factor was used in quantifying fatty acids, based on calibration solution analysis with equal amounts of included fatty acid methyl esters (GLC-793, Nu-Chek-Prep Inc. Elysian MN, USA). It was calculated according to the Eq. (6):$${RF}_{em }=frac{{A}_{23:0}}{{A}_{FS}}$$
(6)
The absolute amount of each fatty acid, calculated as fatty acid methyl ester was calculated according to the Eq. (7):$${C}_{FS}(g/100)=left(frac{{A}_{FS }times {IS}_{W} times {RF}_{em} }{ {A}_{23:0} times W}right)times 100$$
(7)
where AFS = Area of the fatty acid A23:0 = Area of internal standard; ISW = Number of milligrams (mg) internal standard added; RFem = Empirical response factor to the fatty acid with reference to 23:0; W = Weighed sample amount in milligrams (mg); 100 = Factor for conversion to g/100 g13,14,15.Lipid classesThe dominant lipid classes were separated by HPLC equipped with a LiChroCART 125-4, diol 5 µm column and a Charged Aerosol Detector (CAD), using a tertiary gradient mobile phase composition. The fat was extracted as previously described (“Fat content”). A suitable amount of CH3Cl was added to the fat sample, the mix was pipetted into a tared test tube and evaporated on a heating block under nitrogen. The temperature of the heating block must be at 60 °C. The test tube with the evaporated sample was weighed and the weight of the fat calculated. The sample was diluted with an appropriate amount of CH3Cl. Prior to injection the CAD detector was programmed with these settings: range = 500, Filter = Med, Offset = 5, T = 30 °C. The gradient profile is shown Table 2.Table 2 Gradient profile for separation of lipid classes.Full size tableThe quantification was based on external standards with a purity ≥ 98%. Triacylglycerols (TAG) in natural marine oils have a large elution range compared to the other lipid classes, therefore a standard control oil (fish oil) for the preparation of the TAG standard curve was used. This provides a better adaptation to real samples compared to a pure TAG compound.Meat contentMeat content was measured on cooked clusters from the middle of the merus on the first walking leg as an area-percentage of meat-to-shell using an elliptic area formula; Internal height and width of shells and external height and width of muscle was measured, width (w) and height (h) were multiplied to each other and π to calculate the elliptical areas (n = 56 September, n = 45 December, n = 29 February, n = 66 April, n = 50 June). The meat content (MC) was defined as the percentage of space occupied by meat according to the Eq. (8) and the hepatopancreas index (HI) was calculated according to the Eq. (9):$$MCleft(%right)=frac{h;muscletimes w;muscletimes pi }{h;shelltimes w;shelltimes pi }times 100$$
(8)
$$HIleft(%right)=frac{{W}_{hept}}{{W}_{live}}times 100$$
(9)
where Whept is the weight of the hepatopancreas and Wlive is the live weight of the crab (n = 56 September, n = 50 December, n = 70 February, n = 70 April, n = 50 June).Graphs, ordinary one-way ANOVA, and linear regressions were made using GraphPad Prism version 7.03 (CA, USA). Meat content data failed the normality test (Shapiro–Wilk) and was analysed using Kruskal–Wallis One Way Analyses of Variance on Ranks and statistical significance was assumed when P  More

1.Gundersen, P. Old-growth forest carbon sinks overestimated. Nature https://doi.org/10.1038/s41586-021-03266-z (2021).2.Luyssaert, S. et al. Old-growth forests as global carbon sinks. Nature 455, 213–215 (2008).ADS 
CAS 
Article 

Google Scholar 
3.Yang, Y., Luo, Y. & Finzi, A. C. Carbon and nitrogen dynamics during forest stand development: a global synthesis. New Phytol. 190, 977–989 (2011).CAS 
Article 

Google Scholar 
4.Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).ADS 
CAS 
Article 

Google Scholar 
5.Fontaine, S. et al. Stability of organic carbon in deep soil layers controlled by fresh carbon supply. Nature 450, 277–280 (2007).ADS 
CAS 
Article 

Google Scholar 
6.Houlton, B. Z. & Dahlgren, R. A. Convergent evidence for widespread rock nitrogen sources in Earth’s surface environment. Science 62, 58–62 (2018).ADS 
Article 

Google Scholar 
7.Anderegg, W. R. L. et al. Climate-driven risks to the climate mitigation potential of forests. Science 368, eaaz7005 (2020).CAS 
Article 

Google Scholar 
8.Hyvönen, R. et al. The likely impact of elevated [CO2], nitrogen deposition, increased temperature and management on carbon sequestration in temperate and boreal forest ecosystems: a literature review. New Phytol. 173, 463–480 (2006).Article 

Google Scholar 
9.Clark, D. A. et al. Net primary production in tropical forests: an evaluation and synthesis of existing field data. Ecol. Appl. 11, 371–384 (2001).Article 

Google Scholar 
10.Wharton, S. & Falk, M. Climate indices strongly influence old-growth forest carbon exchange. Environ. Res. Lett. 11, 044016 (2016).ADS 
Article 

Google Scholar 
11.Campioli, M. et al. Evaluating the convergence between eddy-covariance and biometric methods for assessing carbon budgets of forests. Nat. Commun. 7, 13717 (2016).ADS 
CAS 
Article 

Google Scholar 
12.Luyssaert, S. et al. Toward a consistency cross-check of eddy covariance flux-based and biometric estimates of ecosystem carbon balance. Glob. Biogeochem. Cycles 23, https://doi.org/10.1029/2008GB003377 (2009).13.Nord-Larsen, T., Vesterdal, L., Bentsen, N. S. & Larsen, J. B. Ecosystem carbon stocks and their temporal resilience in a semi-natural beech-dominated forest. For. Ecol. Manage. 447, 67–76 (2019).Article 

Google Scholar 
14.Kwon, H., Law, B. E., Thomas, C. K. & Johnson, B. G. The influence of hydrological variability on inherent water use efficiency in forests of contrasting composition, age, and precipitation regimes in the Pacific Northwest U.S. Agric. For. Meteorol. 249, 488–500 (2018).ADS 
Article 

Google Scholar 
15.Law, B. E. & Berner, L. T. NACP TERRA-PNW: Forest Plant Traits, NPP, Biomass, and Soil Properties 1999–2014 https://doi.org/10.3334/ORNLDAAC/1292 (ORNL DAAC, 2015).16.Falk, M., Wharton, S., Schroeder, M., Ustin, S. L. & Paw, U. K. T. Flux partitioning in an old-growth forest: seasonal and interannual dynamics. Tree Physiol. 28, 509–520 (2008).CAS 
Article 

Google Scholar 
17.FLUXNET2015 Dataset: Data Processing https://fluxnet.fluxdata.org/data/fluxnet2015-dataset/data-processing/ (Fluxnet, accessed 25 April 2020).18.Potapov, P. et al. The last frontiers of wilderness: tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. 3, e1600821 (2017).ADS 
Article 

Google Scholar 
19.Magnani, F. et al. The human footprint in the carbon cycle of temperate and boreal forests. Nature 447, 849–851 (2007).ADS 
CAS 
Article 

Google Scholar 
20.Jiang, M. et al. The fate of carbon in a mature forest under carbon dioxide enrichment. Nature 580, 227–231 (2020).ADS 
CAS 
Article 

Google Scholar 
21.Zhou, G. et al. Old-growth forests can accumulate carbon in soils. Science 314, 1417–1417 (2006).ADS 
CAS 
Article 

Google Scholar 
22.Nabuurs, G.-J. et al. First signs of carbon sink saturation in European forest biomass. Nat. Clim. Chang. 3, 792–796 (2013).ADS 
CAS 
Article 

Google Scholar  More

1.Friedlingstein, P. et al. Global carbon budget 2020. Earth Syst. Sci. Data 12, 3269–3340 (2020).ADS 

Google Scholar 
2.Schimel, D., Stephens, B. B. & Fisher, J. B. Effect of increasing CO2 on the terrestrial carbon cycle. Proc. Natl Acad. Sci. USA 112, 436–441 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
3.Keenan, T. et al. Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake. Nat. Commun. 7, 13428 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Baig, S., Medlyn, B. E., Mercado, L. M. & Zaehle, S. Does the growth response of woody plants to elevated CO2 increase with temperature? A model-oriented meta-analysis. Glob. Change Biol. 21, 4303–4319 (2015).ADS 

Google Scholar 
5.Drake, J. E. et al. Increases in the flux of carbon belowground stimulate nitrogen uptake and sustain the long‐term enhancement of forest productivity under elevated CO2. Ecol. Lett. 14, 349–357 (2011).PubMed 
PubMed Central 

Google Scholar 
6.Norby, R. J. et al. Forest response to elevated CO2 is conserved across a broad range of productivity. Proc. Natl Acad. Sci. USA 102, 18052–18056 (2005).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
7.van Groenigen, K. J., Qi, X., Osenberg, C. W., Luo, Y. & Hungate, B. A. Faster decomposition under increased atmospheric CO2 limits soil carbon storage. Science 344, 508 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
8.Friedlingstein, P. et al. Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J. Clim. 27, 511–526 (2014).ADS 

Google Scholar 
9.Todd-Brown, K. E. O. et al. Changes in soil organic carbon storage predicted by Earth system models during the 21st century. Biogeosciences 11, 2341–2356 (2014).ADS 
CAS 

Google Scholar 
10.Heimann, M. & Reichstein, M. Terrestrial ecosystem carbon dynamics and climate feedbacks. Nature 451, 289–292 (2008).ADS 
CAS 

Google Scholar 
11.Bradford, M. A. et al. Managing uncertainty in soil carbon feedbacks to climate change. Nat. Clim. Chang. 6, 751–758 (2016).ADS 

Google Scholar 
12.Terrer, C. et al. Nitrogen and phosphorus constrain the CO2 fertilization of global plant biomass. Nat. Clim. Chang. 9, 684–689 (2019).ADS 
CAS 

Google Scholar 
13.Reich, P. B., Hungate, B. A. & Luo, Y. Carbon-nitrogen interactions in terrestrial ecosystems in response to rising atmospheric carbon dioxide. Annu. Rev. Ecol. Evol. Syst. 37, 611–636 (2006).
Google Scholar 
14.Norby, R. J. & Zak, D. R. Ecological lessons from free-air CO2 enrichment (FACE) experiments. Annu. Rev. Ecol. 42, 181–203 (2011).
Google Scholar 
15.Terrer, C. et al. Ecosystem responses to elevated CO2 governed by plant–soil interactions and the cost of nitrogen acquisition. New Phytol. 217, 507–522 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
16.Olson, J. S. Energy storage and the balance of producers and decomposers in ecological systems. Ecology 44, 322–331 (1963).
Google Scholar 
17.Hungate, B. A. et al. Assessing the effect of elevated carbon dioxide on soil carbon: a comparison of four meta‐analyses. Glob. Change Biol. 15, 2020–2034 (2009).ADS 

Google Scholar 
18.Kuzyakov, Y., Horwath, W. R., Dorodnikov, M. & Blagodatskaya, E. Review and synthesis of the effects of elevated atmospheric CO2 on soil processes: no changes in pools, but increased fluxes and accelerated cycles. Soil Biol. Biochem. 128, 66–78 (2019).CAS 

Google Scholar 
19.Tian, H. et al. Global patterns and controls of soil organic carbon dynamics as simulated by multiple terrestrial biosphere models: current status and future directions. Glob. Biogeochem. Cycles 29, 775–792 (2015).ADS 
CAS 

Google Scholar 
20.Todd-Brown, K. E. O. et al. Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations. Biogeosciences 10, 1717–1736 (2013).ADS 

Google Scholar 
21.Nie, M., Lu, M., Bell, J., Raut, S. & Pendall, E. Altered root traits due to elevated CO2: a meta‐analysis. Glob. Ecol. Biogeogr. 22, 1095–1105 (2013).
Google Scholar 
22.Kuzyakov, Y. Priming effects: interactions between living and dead organic matter. Soil Biol. Biochem. 42, 1363–1371 (2010).CAS 

Google Scholar 
23.Treseder, K. K. A meta‐analysis of mycorrhizal responses to nitrogen, phosphorus, and atmospheric CO2 in field studies. New Phytol. 164, 347–355 (2004).
Google Scholar 
24.Jastrow, J. D. et al. Elevated atmospheric carbon dioxide increases soil carbon. Glob. Change Biol. 11, 2057–2064 (2005).ADS 

Google Scholar 
25.Carrillo, Y., Dijkstra, F. A., LeCain, D. & Pendall, E. Mediation of soil C decomposition by arbuscular mycorrizhal fungi in grass rhizospheres under elevated CO2. Biogeochemistry 127, 45–55 (2016).CAS 

Google Scholar 
26.Averill, C., Bhatnagar, J. M., Dietze, M. C., Pearse, W. D. & Kivlin, S. N. Global imprint of mycorrhizal fungi on whole-plant nutrient economics. Proc. Natl Acad. Sci. USA 116, 23163–23168 (2019).CAS 

Google Scholar 
27.Cotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K. & Paul, E. The Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: do labile plant inputs form stable soil organic matter? Glob. Change Biol. 19, 988–995 (2013).ADS 

Google Scholar 
28.Cotrufo, M. F., Ranalli, M. G., Haddix, M. L., Six, J. & Lugato, E. Soil carbon storage informed by particulate and mineral-associated organic matter. Nat. Geosci. 12, 989–994 (2019).ADS 
CAS 

Google Scholar 
29.Craig, M. E. et al. Tree mycorrhizal type predicts within-site variability in the storage and distribution of soil organic matter. Glob. Change Biol. 24, 3317–3330 (2018).ADS 

Google Scholar 
30.Schmidt, M. W. I. et al. Persistence of soil organic matter as an ecosystem property. Nature 478, 49–56 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Jobbágy, E. G. & Jackson, R. B. The vertical distribution of soil organic carbon and its relation to climate and vegetation. Ecol. Appl. 10, 423–436 (2000).
Google Scholar 
32.Sokol, N. W., Kuebbing, S. E., Karlsen‐Ayala, E. & Bradford, M. A. Evidence for the primacy of living root inputs, not root or shoot litter, in forming soil organic carbon. New Phytol. 221, 233–246 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
33.Evans, R. D. et al. Greater ecosystem carbon in the Mojave Desert after ten years exposure to elevated CO2. Nat. Clim. Chang. 4, 394–397 (2014).ADS 
CAS 

Google Scholar 
34.Walker, A. P. et al. FACE-MDS Phase 2: Model Output https://www.osti.gov/dataexplorer/biblio/dataset/1480327 (2018).35.Wieder, W. R. et al. Carbon cycle confidence and uncertainty: exploring variation among soil biogeochemical models. Glob. Change Biol. 24, 1563–1579 (2018).ADS 

Google Scholar 
36.Sulman, B. N. et al. Diverse mycorrhizal associations enhance terrestrial C storage in a global model. Glob. Biogeochem. Cycles 33, 501–523 (2019).ADS 
CAS 

Google Scholar 
37.Shi, M., Fisher, J. B., Brzostek, E. R. & Phillips, R. P. Carbon cost of plant nitrogen acquisition: global carbon cycle impact from an improved plant nitrogen cycle in the Community Land Model. Glob. Change Biol. 22, 1299–1314 (2016).ADS 

Google Scholar 
38.Norby, R. J., Warren, J. M., Iversen, C. M., Medlyn, B. E. & McMurtrie, R. E. CO2 enhancement of forest productivity constrained by limited nitrogen availability. Proc. Natl Acad. Sci. USA 107, 19368–19373 (2010).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Jiang, M. et al. The fate of carbon in a mature forest under carbon dioxide enrichment. Nature 580, 227–231 (2020).ADS 
CAS 
PubMed 

Google Scholar 
40.Wieder, W. R., Bonan, G. B. & Allison, S. D. Global soil carbon projections are improved by modelling microbial processes. Nat. Clim. Chang. 3, 909–912 (2013).ADS 
CAS 

Google Scholar 
41.Terrer, C. Report of Mutualistic Associations, Nutrients, and Carbon Under eCO2 (ROMANCE) v1.0 Dataset. https://doi.org/10.6084/m9.figshare.11704491.v7 (2020).42.Dieleman, W. I. J. et al. Simple additive effects are rare: a quantitative review of plant biomass and soil process responses to combined manipulations of CO2 and temperature. Glob. Change Biol. 18, 2681–2693 (2012).ADS 

Google Scholar 
43.Borenstein, M., Hedges, L. V., Higgins, J. P. T. & Rothstein, H. R. in Introduction to Meta‐Analysis 225–238 (John Wiley & Sons, 2009).44.Del Re, A. C. & Hoyt, W. T. MAd: meta-analysis with mean differences. R Package Version 08-2 https://cran.r-project.org/package=MAd (2014).45.Song, J. & Wan, S. A Global Database Of Plant Production And Carbon Exchange From Global Change Manipulative Experiments https://doi.org/10.6084/m9.figshare.7442915.v9 (2020).46.Viechtbauer, W. Conducting meta-analyses in R with the metafor Package. J. Stat. Softw. 36, https://doi.org/10.18637/jss.v036.i03 (2010).47.Osenberg, C. W., Sarnelle, O., Cooper, S. D. & Holt, R. D. Resolving ecological questions through meta-analysis: goals, metrics, and models. Ecology 80, 1105–1117 (1999).
Google Scholar 
48.Rubin, D. B. & Schenker, N. Multiple imputation in health‐are databases: an overview and some applications. Stat. Med. 10, 585–598 (1991).CAS 
PubMed 
PubMed Central 

Google Scholar 
49.Lajeunesse, M. J. Facilitating systematic reviews, data extraction and meta‐analysis with the METAGEAR package for R. Methods Ecol. Evol. 7, 323–330 (2016).
Google Scholar 
50.Van Lissa, C. J. MetaForest: exploring heterogeneity in meta-analysis using random forests. Preprint at https://psyarxiv.com/myg6s/ (2017).51.Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, https://doi.org/10.18637/jss.v028.i05 (2008).52.Calcagno, V. & de Mazancourt, C. glmulti: an R package for easy automated model selection with (generalized) linear models. J. Stat. Softw. 34, https://doi.org/10.18637/jss.v034.i12 (2010).53.van Groenigen, K. J. et al. Element interactions limit soil carbon storage. Proc. Natl Acad. Sci. USA 103, 6571–6574 (2006).ADS 

Google Scholar 
54.Wang, B. & Qiu, Y. L. Phylogenetic distribution and evolution of mycorrhizas in land plants. Mycorrhiza 16, 299–363 (2006).CAS 

Google Scholar 
55.Maherali, H., Oberle, B., Stevens, P. F., Cornwell, W. K. & McGlinn, D. J. Mutualism persistence and abandonment during the evolution of the mycorrhizal symbiosis. Am. Nat. 188, E113–E125 (2016).
Google Scholar 
56.Terrer, C., Vicca, S., Hungate, B. A., Phillips, R. P. & Prentice, I. C. Mycorrhizal association as a primary control of the CO2 fertilization effect. Science 353, 72–74 (2016).ADS 
CAS 

Google Scholar 
57.Medlyn, B. E. et al. Using ecosystem experiments to improve vegetation models. Nat. Clim. Chang. 5, 528–534 (2015).ADS 

Google Scholar 
58.Zaehle, S. et al. Evaluation of 11 terrestrial carbon–nitrogen cycle models against observations from two temperate Free‐Air CO2 Enrichment studies. New Phytol. 202, 803–822 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
59.De Kauwe, M. G. et al. Where does the carbon go? A model-data intercomparison of vegetation carbon allocation and turnover processes at two temperate forest free-air CO2 enrichment sites. New Phytol. 203, 883–899 (2014).PubMed 
PubMed Central 

Google Scholar 
60.Walker, A. P. et al. Comprehensive ecosystem model‐data synthesis using multiple data sets at two temperate forest free‐air CO2 enrichment experiments: model performance at ambient CO2 concentration. J. Geophys. Res. Biogeosci. 119, 937–964 (2014).ADS 
CAS 

Google Scholar 
61.Walker, A. P. et al. Decadal biomass increment in early secondary succession woody ecosystems is increased by CO2 enrichment. Nat. Commun. 10, 454 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
62.Schlesinger, W. et al. in Managed Ecosystems and CO2 197–212 (2006).63.Hungate, B. A. et al. Cumulative response of ecosystem carbon and nitrogen stocks to chronic CO2 exposure in a subtropical oak woodland. New Phytol. 200, 753–766 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
64.Jordan, D. N. et al. Biotic, abiotic and performance aspects of the Nevada Desert Free-Air CO2 Enrichment (FACE) Facility. Glob. Change Biol. 5, 659–668 (1999).ADS 

Google Scholar 
65.Carrillo, Y., Dijkstra, F., LeCain, D., Blumenthal, D. & Pendall, E. Elevated CO2 and warming cause interactive effects on soil carbon and shifts in carbon use by bacteria. Ecol. Lett. 21, 1639–1648 (2018).
Google Scholar 
66.Mueller, K. E. et al. Impacts of warming and elevated CO2 on a semi‐arid grassland are non‐additive, shift with precipitation, and reverse over time. Ecol. Lett. 19, 956–966 (2016).CAS 

Google Scholar 
67.Zak, D. R., Pregitzer, K. S., Kubiske, M. E. & Burton, A. J. Forest productivity under elevated CO2 and O3: positive feedbacks to soil N cycling sustain decade‐long net primary productivity enhancement by CO2. Ecol. Lett. 14, 1220–1226 (2011).
Google Scholar 
68.Oleson, K. et al. Technical Description of Version 4.5 of the Community Land Model (CLM) Report NCAR/TN-503+STR, https://doi.org/10.5065/D6RR1W7M (2013).69.Clark, D. B. et al. The Joint UK Land Environment Simulator (JULES), model description—Part 2: Carbon fluxes and vegetation dynamics. Geosci. Model Dev. 4, 701–722 (2011).ADS 

Google Scholar 
70.Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Glob. Biogeochem. Cycles 19, https://doi.org/10.1029/2003GB002199 (2005).71.Haverd, V. et al. A new version of the CABLE land surface model (subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation-based approach to plant coordination of photosynthesis. Geosci. Model Dev. 11, 2995–3026 (2018).ADS 
CAS 

Google Scholar 
72.Lawrence, D. M. et al. The Community Land Model Version 5: description of new features, benchmarking, and impact of forcing uncertainty. J. Adv. Model. Earth Syst. 11, 4245–4287 (2019).ADS 

Google Scholar 
73.Meiyappan, P., Jain, A. K. & House, J. I. Increased influence of nitrogen limitation on CO2 emissions from future land use and land use change. Glob. Biogeochem. Cycles 29, 1524–1548 (2015).ADS 
CAS 

Google Scholar 
74.Smith, B. et al. Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model. Biogeosciences 11, 2027–2054 (2014).ADS 

Google Scholar 
75.Goll, D. S. et al. A representation of the phosphorus cycle for ORCHIDEE (revision 4520). Geosci. Model Dev. 10, 3745–3770 (2017).ADS 
CAS 

Google Scholar 
76.Friedlingstein, P. et al. Global carbon budget 2019. Earth Syst. Sci. Data 11, 1783–1838 (2019).ADS 

Google Scholar 
77.Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high‐resolution grids of monthly climatic observations—the CRU TS3.10 dataset. Int. J. Climatol. 34, 623–642 (2014).
Google Scholar 
78.Soudzilovskaia, N. A. et al. Global mycorrhizal plant distribution linked to terrestrial carbon stocks. Nat. Commun. 10, 5077 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
79.Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS One 12, e0169748 (2017).PubMed 
PubMed Central 

Google Scholar 
80.Batjes, N. H. Harmonized soil property values for broad-scale modelling (WISE30sec) with estimates of global soil carbon stocks. Geoderma 269, 61–68 (2016).ADS 
CAS 

Google Scholar 
81.Shangguan, W., Dai, Y., Duan, Q., Liu, B. & Yuan, H. A global soil data set for earth system modeling. J. Adv. Model. Earth Syst. 6, 249–263 (2014).ADS 

Google Scholar  More

In a paper in Nature, Terrer et al.1 reveal an unexpected trade-off between the effects of rising atmospheric carbon dioxide levels on plant biomass and on stocks of soil carbon. Contrary to the assumptions encoded in most computational models of terrestrial ecosystems, the accrual of soil carbon is not positively related to the amount of carbon taken up by plants for biomass growth when CO2 concentrations increase. Instead, the authors show that carbon accumulates in soils when there is a small boost in plant biomass growth in response to CO2, and declines when the growth of biomass is high. Terrer et al. propose that associations of plants with mycorrhizal soil fungi are a key factor in this relationship between the above- and below-ground responses to elevated CO2 levels.
Read the paper: A trade-off between plant and soil carbon storage under elevated CO2
Rising levels of atmospheric CO2 are thought to have driven an increase in the amount of carbon absorbed globally by land ecosystems over the past few decades, a phenomenon known as the CO2 fertilization effect2. This occurs because, at the scale of leaves, higher CO2 levels enhance photosynthesis and the efficiency with which resources (water, light and nutrients such as nitrogen) are used to assimilate CO2 and support biomass growth3. Evidence supporting the existence of the CO2 fertilization effect has been observed in experiments in which the atmosphere around plants or plant communities is enriched with CO2. But at the level of whole ecosystems, responses to CO2 enrichment are more difficult to track, because the effects are diluted throughout a chain of connected processes. Constraining estimates of the response of the global land carbon sink to rising CO2 levels therefore remains a major challenge (see go.nature.com/3vgvhj).Changes in soil carbon are inherently difficult to detect, and studies that assess the effects of elevated CO2 levels on soil-carbon stocks have been equivocal4. Terrer and colleagues set out to investigate these effects by carrying out a meta-analysis of 108 CO2-enrichment experiments. The authors estimate that, in these studies, soil-carbon stocks increased in non-forest sites but remained almost unchanged in forests. By evaluating the effects of multiple environmental variables, the authors found that, surprisingly, the best explanation for the observed patterns is that the changes in soil carbon stocks are inversely related to the changes in above-ground plant biomass: high accumulation of carbon in biomass was associated with soil-carbon loss, whereas low biomass accumulation was associated with soil-carbon gain. This relationship was evident only in experiments in which no nutrients had been added to the studied systems, leading the authors to propose that plant nutrient-acquisition strategies are responsible — which, in turn, depend on the mycorrhizal soil fungi associated with the plants.
Soils linked to climate change
A previous study reported5 that only a small increase in above-ground biomass occurs in CO2-enriched plants that associate with a particular family of mycorrhizae (arbuscular mycorrhizae; AM). AM-associated plants benefit from the fungi’s extensive network of hyphae (branching filaments that aid vegetative growth), which support the plants’ uptake of nitrogen from the soil solution. However, AM have only a limited ability to ‘mine’ nitrogen from organic matter in the soil. The availability of soil nitrogen therefore limits the increase of biomass growth of AM-associated plants in response to elevated CO2 levels. By contrast, plant species that associate with a different group of soil fungi (the ectomycorrhizae; ECM) exhibit a greater increase in above-ground biomass in CO2-enrichment studies, because some of their carbon is allocated to ECM that can mine for nitrogen5. Mining for nutrients by ECM is, however, thought to accelerate the decomposition of organic matter in soil.Terrer et al. now find that AM-associated plants produce a bigger increase in soil-carbon stocks in CO2-enrichment experiments than do ECM-associated plants. The authors suggest that this is because AM-associated plants allocate more carbon to fine roots and to compounds exuded by the roots, resulting in soil-carbon accrual (Fig. 1a). By contrast, nutrient acquisition by ECM-associated plants results in increased turnover — and therefore loss — of soil organic matter (Fig. 1b). Overall, this would lead to an ecosystem-scale trade-off between the amount of carbon sequestered in plants and that sequestered in soil, in a CO2-enriched atmosphere.

Figure 1 | Proposed effects of elevation of atmospheric carbon dioxide levels. Terrer et al.1 suggest that associations of plants with different types of mycorrhizal soil fungi affect plant and soil responses to increases in atmospheric carbon dioxide levels. a, Plants that associate with arbuscular mycorrhizal fungi (grasses and some trees, in this study) do not ‘mine’ nitrogen (N, a nutrient) from the soil, and therefore do not produce much extra above-ground biomass when CO2 levels rise. Instead, they allocate carbon to fine roots and to root-exuded substances, resulting in soil-carbon accrual. Carbon dioxide produced from the respiration of soil microorganisms returns carbon to the atmosphere. b, Plants that associate with ectomycorrhizal fungi (only trees in this study) mine the soil for nitrogen, the uptake of which supports a bigger increase in biomass growth than in a. However, nutrient mining increases the rate of decomposition of organic matter in soil. The amount of carbon in the soil therefore decreases in response to elevated CO2 levels; microbial soil respiration is greater than in a.

Most Earth-system models that account for land carbon-cycling processes assume that rising levels of atmospheric CO2 will increase plant growth, thus producing more plant litter and thereby increasing stocks of soil carbon6. The authors compared the changes in soil carbon and above-ground plant biomass predicted by various models, both in simulations of six open-air CO2-enrichment experiments, and in global simulations of historical and future increases in atmospheric CO2. None of the models reproduced the negative relationship between carbon sequestration by soil and growth in plant biomass that was observed in the current study.Terrer and co-workers’ findings thus provide another urgent warning that current climate models overestimate the amount of carbon that will be sequestered by land ecosystems as atmospheric CO2 levels increase — not only because the models largely ignore the effects of nutrient limitations, but also because they overestimate the amount of carbon that could be sequestered in soil, particularly in forest ecosystems7. But the new study also reveals that grasslands, shrublands and other ecosystems that already have high soil-carbon stocks have great potential to accumulate more soil carbon as CO2 levels increase. These results thus add weight to previous calls to protect existing soil-carbon stocks to mitigate the effects of climate change8.
Carbon dioxide loss from tropical soils increases on warming
There are some limitations to the set of CO2-enrichment experiments included in Terrer and colleagues’ meta-analysis. The experiments are biased towards temperate systems, and most of the forests studied are associated with ECM, whereas the grasslands are all AM-associated. The authors did not find that the type of ecosystem had a substantial effect on the observed responses to CO2, but it remains to be seen whether the reported trade-off between above- and below-ground carbon sequestration for AM- compared with ECM-associated plants applies to forests alone9. Further experiments, especially in tropical ecosystems, are now needed to address these issues.Tropical ecosystems are large contributors to the global terrestrial carbon sink10, but they are notoriously under-studied. Field observations are scarce and few manipulation experiments — such as CO2 enrichment or nutrient additions — have been carried out in these ecosystems11,12. Below-ground processes are particularly challenging to assess in the tropics, where the effects of multiple nutrient scarcities often come into play12. Terrer and colleagues’ study provides a promising framework that can be elaborated to describe diverse plant–soil interactions in various terrestrial ecosystems in the future.CO2-enrichment experiments generally last for just a few years, or just over a decade at most13. Such timescales are unlikely to capture the effects of elevated CO2 levels on plant mortality, plant-species composition and soil-carbon turnover time, all of which can affect the sequestration of carbon by ecosystems in different ways in the longer term. Mechanistic understanding gained from experiments about the coupling between carbon and nutrient cycling can, however, be integrated into computational models. And this will allow us to constrain estimates of the size of the terrestrial carbon sink in the coming decades. The interactions between plants and their associated soil fungi, as well as other crucial below-ground agents and processes such as microbial communities, are already stirring up modelling efforts14,15. Terrer and colleagues’ study now invites researchers to test hypotheses about the processes that drive coordinated above- and below-ground responses to rising CO2 levels. Such studies could be a real step forwards in our understanding of the fate of the terrestrial carbon sink. More