Ecology

1.Luyssaert, S. et al. Old-growth forests as global carbon sinks. Nature 455, 213–215 (2008).ADS 
CAS 
Article 

Google Scholar 
2.Odum, E. P. The strategy of ecosystem development. Science 164, 262–270 (1969).ADS 
CAS 
Article 

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

Google Scholar 
4.Ciais, P. et al. Carbon and other biogeochemical cycles. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).5.Baccini, A. et al. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 358, 230–234 (2017).ADS 
MathSciNet 
CAS 
Article 

Google Scholar 
6.Global Soil Organic Carbon Map (GSOCmap) Technical Report http://www.fao.org/3/I8891EN/i8891en.pdf (FAO/ITPS, 2018).7.Belyea, L. R. & Malmer, N. Carbon sequestration in peatland: patterns and mechanisms of response to climate change. Glob. Change Biol. 10, 1043–1052 (2004).ADS 
Article 

Google Scholar 
8.Zhang, J. et al. C:N:P stoichiometry in China’s forests: from organs to ecosystems. Funct. Ecol. 32, 50–60 (2018).Article 

Google Scholar 
9.Fang, Y. et al. Atmospheric deposition and leaching of nitrogen in Chinese forest ecosystems. J. For. Res. 16, 341–350 (2011).CAS 
Article 

Google Scholar 
10.Fenn, M. E. et al. Nitrogen excess in North American ecosystems: predisposing factors, ecosystem responses, and management strategies. Ecol. Appl. 8, 706–733 (1998).Article 

Google Scholar 
11.MacDonald, J. A. et al. Nitrogen input together with ecosystem nitrogen enrichment predict nitrate leaching from European forests. Glob. Change Biol. 8, 1028–1033 (2002).ADS 
Article 

Google Scholar 
12.Dentener, F. et al. Nitrogen and sulfur deposition on regional and global scales: a multimodel evaluation. Glob. Biogeochem. Cycles 20, GB4003 (2006).ADS 
Article 

Google Scholar 
13.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 
14.Moffat, A. M. et al. Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes. Agric. For. Meteorol. 147, 209–232 (2007).ADS 
Article 

Google Scholar 
15.Wu, J. et al. Synthesis on the carbon budget and cycling in a Danish, temperate deciduous forest. Agric. For. Meteorol. 181, 94–107 (2013).ADS 
Article 

Google Scholar 
16.Soloway, A. D., Amiro, B. D., Dunn, A. L. & Wofsy, S. C. Carbon neutral or a sink? Uncertainty caused by gap-filling long-term flux measurements for an old-growth boreal black spruce forest. Agric. For. Meteorol. 233, 110–121 (2017).ADS 
Article 

Google Scholar 
17.McHugh, I. D. et al. Interactions between nocturnal turbulent flux, storage and advection at an “ideal” eucalypt woodland site. Biogeosciences 14, 3027–3050 (2017).ADS 
CAS 
Article 

Google Scholar 
18.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 
19.Kang, M. et al. New gap-filling strategies for long-period flux data gaps using a data-driven approach. Atmosphere 10, 568 (2019).ADS 
Article 

Google Scholar 
20.Hayek, M. N. et al. A novel correction for biases in forest eddy covariance carbon balance. Agric. For. Meteorol. 250–251, 90–101 (2018).ADS 
Article 

Google Scholar 
21.Wirth, C., Messier, C., Bergeron, Y., Frank, D. & Fankhänel, A. Old-growth forest definitions: a pragmatic view. In Old‐Growth Forests (eds Wirth, C. et al.) Ecological Studies Vol. 207, 1–33 (Springer, 2009).22.Luyssaert, S., Inglima, I. & Jung, M. Global Forest Ecosystem Structure and Function Data for Carbon Balance Research https://doi.org/10.3334/ORNLDAAC/949 (Oak Ridge National Laboratory Distributed Active Archive Center, 2009). More

1.Proctor, L. M. & Fuhrman, J. A. Viral mortality of marine bacteria and cyanobacteria. Nature 343, 60–62 (1990).Article 

Google Scholar 
2.Bergh, O., Børsheim, K. Y., Bratbak, G. & Heldal, M. High abundance of viruses found in aquatic environments. Nature 340, 467–468 (1989).CAS 
PubMed 
Article 

Google Scholar 
3.Cai, L. et al. Active and diverse viruses persist in the deep sub-seafloor sediments over thousands of years. ISME J. 13, 1857–1864 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Reyes, A., Semenkovich, N. P., Whiteson, K., Rohwer, F. & Gordon, J. I. Going viral: next-generation sequencing applied to phage populations in the human gut. Nat. Rev. Microbiol. 10, 607–617 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Fuhrman, J. A. Marine viruses and their biogeochemical and ecological effects. Nature 399, 541–548 (1999).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Weinbauer, M. G. Ecology of prokaryotic viruses. FEMS Microbiol. Rev. 28, 127–181 (2004).CAS 
PubMed 
Article 

Google Scholar 
7.Vega Thurber, R. L., Payet, J. P., Thurber, A. R. & Correa, A. M. S. Virus-host interactions and their roles in coral reef health and disease. Nat. Rev. Microbiol. 15, 205–216 (2017).Article 
CAS 

Google Scholar 
8.Zimmerman, A. E. et al. Metabolic and biogeochemical consequences of viral infection in aquatic ecosystems. Nat. Rev. Microbiol. 18, 21–34 (2020).CAS 
PubMed 
Article 

Google Scholar 
9.Wilhelm, S. W. & Suttle, C. A. Viruses and nutrient cycles in the sea. Bioscience 49, 781–788 (1999). Seminal work modelling how viral activity in the oceans prevents up to a quarter of organic matter from being exported to higher trophic levels; instead, this matter is recycled (by viral lysis) into a form that can be assimilated by microorganisms.Article 

Google Scholar 
10.Calendar, R. L. The Bacteriophages 2nd edn (Oxford University Press, 2005).11.Sullivan, M. B., Weitz, J. S. & Wilhelm, S. W. Viral ecology comes of age. Environ. Microbiol. Rep. 9, 33–35 (2017).PubMed 
Article 

Google Scholar 
12.Roux, S., Hallam, S. J., Woyke, T. & Sullivan, M. B. Viral dark matter and virus–host interactions resolved from publicly available microbial genomes. eLife 4, e08490 (2015).PubMed Central 
Article 
PubMed 

Google Scholar 
13.Paez-Espino, D. et al. Uncovering Earth’s virome. Nature 536, 425–430 (2016). An in silico catalogue of the diversity of viruses on Earth that serves as the foundation for the Joint Genome Institute’s growing IMG/VR database.CAS 
PubMed 
Article 

Google Scholar 
14.Emerson, J. B. et al. Host-linked soil viral ecology along a permafrost thaw gradient. Nat. Microbiol. 3, 870–880 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Stough, J. M. A. et al. Diversity of active viral infections within the Sphagnum microbiome. Applied Environ. Microbiol. https://doi.org/10.1128/AEM.01124-18 (2018).Article 

Google Scholar 
16.Gregory, A. C. et al. Marine DNA viral macro- and microdiversity from pole to pole. Cell 177, 1109–1123.e1114 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.De Corte, D. et al. Viral communities in the global deep ocean conveyor belt assessed by targeted viromics. Front. Microbiol. https://doi.org/10.3389/fmicb.2019.01801 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
18.Jang, H. B. et al. Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks. Nat. Biotechnol. 37, 632–639 (2019).Article 
CAS 

Google Scholar 
19.Roux, S. A viral ecogenomics framework to uncover the secrets of nature’s “microbe whisperers”. mSystems 4, e00111–e00119 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Roux, S. et al. Minimum information about an uncultivated virus genome (MIUViG). Nat. Biotechnol. 37, 29 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
21.Hobbs, Z. & Abedon, S. T. Diversity of phage infection types and associated terminology: the problem with ‘lytic or lysogenic’. FEMS Microbiol. Lett. https://doi.org/10.1093/femsle/fnw047 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
22.Hay, I. D. & Lithgow, T. Filamentous phages: masters of a microbial sharing economy. EMBO Reports 20, e47427 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
23.McLeod, S. M., Kimsey, H. H., Davis, B. M. & Waldor, M. K. CTXphi and Vibrio cholerae: exploring a newly recognized type of phage-host cell relationship. Mol. Microbiol. 57, 347–356 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Howard-Varona, C. et al. Regulation of infection efficiency in a globally abundant marine Bacteriodetes virus. ISME J. 11, 284–295 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Howard-Varona, C. et al. Multiple mechanisms drive phage infection efficiency in nearly identical hosts. ISME J. 12, 1605–1618 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
26.Kirzner, S., Barak, E. & Lindell, D. Variability in progeny production and virulence of cyanophages determined at the single-cell level. Environ. Microbiol. Rep. 8, 605–613 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
27.Gregory, A. C. et al. Genomic differentiation among wild cyanophages despite widespread horizontal gene transfer. BMC Genomics 17, 930 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
28.Holmfeldt, K. et al. Large‐scale maps of variable infection efficiencies in aquatic Bacteroidetes phage‐host model systems. Environ. Microbiol. 18, 3949–3961 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Zborowsky, S. & Lindell, D. Resistance in marine cyanobacteria differs against specialist and generalist cyanophages. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1906897116 (2019). A meticulous investigation revealing that cyanobacteria defend against specialist phages by blocking their entry, whereas generalist phage infections are arrested intracellularly; thus generalist phages may be more common agents of horizontal gene transfer and co-infection.Article 
PubMed 
PubMed Central 

Google Scholar 
30.Lwoff, A. Lysogeny. Bacteriol. Rev. 17, 269–337 (1953).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Howard-Varona, C., Hargreaves, K. R., Abedon, S. T. & Sullivan, M. B. Lysogeny in nature: mechanisms, impact and ecology of temperate phages. ISME J. 11, 1511 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
32.Abedon, S. T. The murky origin of Snow White and her T-even dwarfs. Genetics 155, 481–486 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
33.Demerec, M. & Fano, U. Bacteriophage-resistant mutants in Escherichia coli. Genetics 30, 119–136 (1945).CAS 
PubMed 
PubMed Central 

Google Scholar 
34.Bronfenbrenner, J. J. & Korb, C. Studies on the bacteriophage of d’Herelle: I. Is the lytic principle volatile? J. Exp. Med. 41, 73–79 (1925).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Kourilsky, P. & Knapp, A. Lysogenization by bacteriophage lambda: III. – Multiplicity dependent phenomena occuring upon infection by lambda. Biochimie 56, 1517–1523 (1975).Article 

Google Scholar 
36.St-Pierre, F. & Endy, D. Determination of cell fate selection during phage lambda infection. Proc. Natl Acad. Sci. USA 105, 20705 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Zeng, L. et al. Decision making at a subcellular level determines the outcome of bacteriophage infection. Cell 141, 682–691 (2010). Re-examination of the phage λ decision switch via single-cell tracking of infection fates, revealing how increasing cellular multiplicity of infection increases the stochastic tendency towards lysogeny after infection.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Trinh, J. T., Székely, T., Shao, Q., Balázsi, G. & Zeng, L. Cell fate decisions emerge as phages cooperate or compete inside their host. Nat. Commun. 8, 14341 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Joh, R. I. & Weitz, J. S. To lyse or not to lyse: Transient-mediated stochastic fate determination in cells infected by bacteriophages. PLOS Comput. Biol. 7, e1002006 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
40.Fillol-Salom, A. et al. Bacteriophages benefit from generalized transduction. PLOS Pathog. 15, e1007888 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Howard-Varona, C. et al. Fighting fire with fire: phage potential for the treatment of E. coli O157 infection. Antibiotics 7, 101 (2018).CAS 
PubMed Central 
Article 
PubMed 

Google Scholar 
42.Pratama, A. A. & van Elsas, J. D. A novel inducible prophage from the mycosphere inhabitant Paraburkholderia terrae BS437. Sci. Rep. 7, 9156 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
43.Jiang, S. C. & Paul, J. H. Seasonal and diel abundance of viruses and occurrence of lysogeny/bacteriocinogeny in the marine environment. Mar. Ecol. Prog. Ser. 104, 163–172 (1994).Article 

Google Scholar 
44.Brum, J. R., Hurwitz, B. L., Schofield, O., Ducklow, H. W. & Sullivan, M. B. Seasonal time bombs: dominant temperate viruses affect Southern Ocean microbial dynamics. ISME J. 10, 437–449 (2016). Demonstration that lysogenic activity is favoured in low-productivity polar months (and lytic activity is favoured in high-productivity months), providing support for decades-old ecological hypotheses on the link between abiotic factors and viral strategies.CAS 
PubMed 
Article 

Google Scholar 
45.Levin, R. A., Voolstra, C. R., Weynberg, K. D. & van Oppen, M. J. H. Evidence for a role of viruses in the thermal sensitivity of coral photosymbionts. ISME J. 11, 808–812 (2017).CAS 
PubMed 
Article 

Google Scholar 
46.Vega Thurber, R. L. et al. Metagenomic analysis indicates that stressors induce production of herpes-like viruses in the coral Porites compressa. Proc. Natl Acad. Sci. USA 105, 18413–18418 (2008).CAS 
PubMed 
Article 

Google Scholar 
47.Correa, A. M. S. et al. Viral outbreak in corals associated with an in situ bleaching event: atypical herpes-like viruses and a new megavirus infecting Symbiodinium. Front. Microbiol. 7, 127 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
48.Lawrence, S. A., Davy, J. E., Aeby, G. S., Wilson, W. H. & Davy, S. K. Quantification of virus-like particles suggests viral infection in corals affected by Porites tissue loss. Coral Reefs 33, 687–691 (2014).Article 

Google Scholar 
49.Lawrence, S. A., Floge, S. A., Davy, J. E., Davy, S. K. & Wilson, W. H. Exploratory analysis of Symbiodinium transcriptomes reveals potential latent infection by large dsDNA viruses. Environ. Microbiol. 19, 3909–3919 (2017).CAS 
PubMed 
Article 

Google Scholar 
50.Weynberg, K. D. et al. Prevalent and persistent viral infection in cultures of the coral algal endosymbiont Symbiodinium. Coral Reefs 36, 773–784 (2017).Article 

Google Scholar 
51.Ptashne, M. et al. How the λ repressor and cro work. Cell 19, 1–11 (1980).CAS 
PubMed 
Article 

Google Scholar 
52.Warwick-Dugdale, J., Buchholz, H. H., Allen, M. J. & Temperton, B. Host-hijacking and planktonic piracy: how phages command the microbial high seas. Virol. 16, 15 (2019).Article 

Google Scholar 
53.Silpe, J. E. & Bassler, B. L. A host-produced quorum-sensing autoinducer controls a phage lysis-lysogeny decision. Cell 176, 268–280.e213 (2019).CAS 
PubMed 
Article 

Google Scholar 
54.Erez, Z. et al. Communication between viruses guides lysis–lysogeny decisions. Nature 541, 488 (2017). Demonstration that viruses can ‘communicate’ to decide between lysis and lysogeny by co-opting a host system: extracellular release of small peptides.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Stokar-Avihail, A., Tal, N., Erez, Z., Lopatina, A. & Sorek, R. Widespread utilization of peptide communication in phages infecting soil and pathogenic bacteria. Cell Host Microbe 25, 746–755 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
56.Ofir, G. & Sorek, R. Contemporary phage biology: from classic models to new insights. Cell 172, 1260–1270 (2018).CAS 
PubMed 
Article 

Google Scholar 
57.McNamara, J. M. & Houston, A. I. State-dependent life histories. Nature 380, 215–221 (1996).CAS 
PubMed 
Article 

Google Scholar 
58.Tan, D. et al. High cell densities favor lysogeny: induction of an H20 prophage is repressed by quorum sensing and enhances biofilm formation in Vibrio anguillarum. ISME J. 14, 1731–1742 (2020).CAS 
PubMed 
Article 

Google Scholar 
59.Pleška, M., Lang, M., Refardt, D., Levin, B. R. & Guet, C. C. Phage–host population dynamics promotes prophage acquisition in bacteria with innate immunity. Nat. Ecol. Evol 2, 359–366 (2018).PubMed 
Article 

Google Scholar 
60.Güemes, A. G. C. et al. Viruses as winners in the game of life. Annu. Rev. Virol. 3, 197–214 (2016).Article 
CAS 

Google Scholar 
61.Stewart, F. M. & Levin, B. R. The population biology of bacterial viruses: why be temperate. Theor. Popul. Biol. 26, 93–117 (1984). A seminal article that lays out key pressure points that should dictate temperate phage biology.CAS 
PubMed 
Article 

Google Scholar 
62.Lipsitch, M., Siller, S. & Nowak, M. A. The evolution of virulence in pathogens with vertical and horizontal transmission. Evolution 50, 1729–1741 (1996).PubMed 
Article 

Google Scholar 
63.Frank, S. A. Models of parasite virulence. Q. Rev. Biol. 71, 37–78 (1996).CAS 
PubMed 
Article 

Google Scholar 
64.Weitz, J. S., Li, G., Gulbudak, H., Cortez, M. H. & Whitaker, R. J. Viral invasion fitness across a continuum from lysis to latency. Virus Evol. https://doi.org/10.1093/ve/vez006 (2019). Theoretical study that examines the impact of ecological factors on the proliferation of viruses, enabled by a cell-centric (rather than a particle-centric) view of viral invasion fitness.Article 
PubMed 
PubMed Central 

Google Scholar 
65.Li, G., Cortez, M. H., Dushoff, J. & Weitz, J. S. When to be temperate: on the fitness benefits of lysis vs. lysogeny. Virus Evol. https://doi.org/10.1093/ve/veaa042 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
66.Berngruber, T. W., Froissart, R., Choisy, M. & Gandon, S. Evolution of virulence in emerging epidemics. PLOS Pathog. 9, e1003209 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Wahl, L. M., Betti, M. I., Dick, D. W., Pattenden, T. & Puccini, A. J. Evolutionary stability of the lysis-lysogeny decision: Why be virulent? Evolution 73, 92–98 (2019).CAS 
PubMed 

Google Scholar 
68.Coy, S. R., Alsante, A. N., Van Etten, J. L. & Wilhelm, S. W. Cryopreservation of Paramecium bursaria Chlorella virus-1 during an active infection cycle of its host. PLoS ONE 14, e0211755 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
69.Godfrey-Smith, P. in Individuals Across the Sciences (eds Guay, A. & T. Pradeu, T.) (Oxford University Press, 2015).70.Forterre, P. The virocell concept and environmental microbiology. ISME J. 7, 233 (2013). Proposes the virocell concept, which argues that a given cell represents distinct entities when infected versus uninfected by a virus, providing a non-lytic mechanism by which viruses can significantly alter biogeochemical cycles.CAS 
PubMed 
Article 

Google Scholar 
71.Rosenwasser, S., Ziv, C., van Creveld, S. G. & Vardi, A. Virocell metabolism: metabolic innovations during host-virus interactions in the ocean. Trends Microbiol. 24, 821–832 (2016).CAS 
PubMed 
Article 

Google Scholar 
72.Howard-Varona, C. et al. Phage-specific metabolic reprogramming of virocells. ISME J. 14, 881–895 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
73.Forterre, P. (ed.) Virocell Concept, The. In eLS https://doi.org/10.1002/9780470015902.a0023264 (2012).74.Diekmann, O., Heesterbeek, H. & Britton, T. Mathematical Tools for Understanding Infectious Disease Dynamics. 1st edn, 517 (Princeton University Press, 2012).75.Diekmann, O., Heesterbeek, J. A. P. & Metz, J. A. J. On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations. J. Math. Biol. 28, 365–382 (1990).CAS 
PubMed 
Article 

Google Scholar 
76.Diekmann, O., Heesterbeek, J. A. P. & Roberts, M. G. The construction of next-generation matrices for compartmental epidemic models. J. R. Soc. Interface 7, 873–885 (2010).CAS 
PubMed 
Article 

Google Scholar 
77.van den Driessche, P. & Watmough, J. in Mathematical Epidemiology. Lecture Notes in Mathematics Vol. 1945 (eds Brauer, F., van den Driessche, P. & Wu, J.) 159–178 (Springer, 2008).78.Gandon, S., Day, T., Metcalf, C. J. E. & Grenfell, B. T. Forecasting epidemiological and evolutionary dynamics of infectious diseases. Trends Ecol. Evol. 31, 776–788 (2016).PubMed 
Article 

Google Scholar 
79.Roossinck, M. J. The good viruses: viral mutualistic symbioses. Nat. Rev. Microbiol. 9, 99–108 (2011).CAS 
PubMed 
Article 

Google Scholar 
80.Bondy-Denomy, J. & Davidson, A. R. When a virus is not a parasite: the beneficial effects of prophages on bacterial fitness. J. Microbiol. 52, 235–242 (2014).CAS 
PubMed 
Article 

Google Scholar 
81.Nanda, A. M., Thormann, K. & Frunzke, J. Impact of spontaneous prophage induction on the fitness of bacterial populations and host-microbe interactions. J. Bacteriol. 197, 410 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
82.Obeng, N., Pratama, A. A. & Elsas, J. D. V. The significance of mutualistic phages for bacterial ecology and evolution. Trends Microbiol. 24, 440–449 (2016).CAS 
PubMed 
Article 

Google Scholar 
83.Hutchinson, G. E. Concluding remarks. Cold Spring Harb. Symposia Quant. Biol. 22, 415–427 (1957).Article 

Google Scholar 
84.Taylor, V. L., Fitzpatrick, A. D., Islam, Z. & Maxwell, K. L. The diverse impacts of phage morons on bacterial fitness and virulence. Adv. Virus Res. 103, 1–31 (2019).CAS 
PubMed 
Article 

Google Scholar 
85.Hendrix, R. W., Lawrence, J. G., Hatfull, G. F. & Casjens, S. The origins and ongoing evolution of viruses. Trends Microbiol. 8, 504–508 (2000).CAS 
PubMed 
Article 

Google Scholar 
86.Casjens, S. R. & Hendrix, R. W. Bacteriophage lambda: early pioneer and still relevant. Virology 479-480, 310–330 (2015).CAS 
PubMed 
Article 

Google Scholar 
87.Fortier, L. C. & Sekulovic, O. Importance of prophages to evolution and virulence of bacterial pathogens. Virulence 4, 354–365 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
88.Harrison, E. & Brockhurst, M. A. Ecological and evolutionary benefits of temperate phage: what does or doesn’t kill you makes you stronger. BioEssays 39, 1700112 (2017).Article 

Google Scholar 
89.Berngruber, T. W., Weissing, F. J. & Gandon, S. Inhibition of superinfection and the evolution of viral latency. J. Virol. 4, 10200–10208 (2010).Article 
CAS 

Google Scholar 
90.Susskind, M. M., Botstein, D. & Wright, A. Superinfection exclusion by P22 prophage in lysogens of Salmonella typhimurium: III. Failure of superinfecting phage DNA to enter sieA+ lysogens. Virology 62, 350–366 (1974).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
91.van Houte, S., Buckling, A. & Westra, E. R. Evolutionary ecology of prokaryotic immune mechanisms. Microbiol. Mol. Biol. Rev. 80, 745 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
92.Dodd, I. B., Shearwin, K. E. & Egan, J. B. Revisited gene regulation in bacteriophage lambda. Curr. Opin. Genet. Dev. 15, 145–152 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
93.Díaz-Muñoz, S. L. Viral coinfection is shaped by host ecology and virus-virus interactions across diverse microbial taxa and environments. Virus Evol. 3, vex011 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
94.Breitbart, M., Bonnain, C., Malki, K. & Sawaya, N. A. Phage puppet masters of the marine microbial realm. Nat. Microbiol. 3, 754–766 (2018).CAS 
PubMed 
Article 

Google Scholar 
95.Knowles, B. et al. Lytic to temperate switching of viral communities. Nature 531, 466–470 (2016).CAS 
PubMed 
Article 

Google Scholar 
96.Weitz, J. S., Beckett, S. J., Brum, J. R., Cael, B. B. & Dushoff, J. Lysis, lysogeny and virus-microbe ratios. Nature 549, E1–E3 (2017).CAS 
PubMed 
Article 

Google Scholar 
97.Knowles, B. & Rohwer, F. Knowles & Rohwer reply. Nature 549, E3–E4 (2017).CAS 
PubMed 
Article 

Google Scholar 
98.Wagner, P. L. & Waldor, M. K. Bacteriophage control of bacterial virulence. Infect. Immun. 70, 3985–3993 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
99.Erickson, A. K. et al. Bacteria facilitate enteric virus co-infection of mammalian cells and promote genetic recombination. Cell Host Microbe 23, 77–88.e75 (2018).CAS 
PubMed 
Article 

Google Scholar 
100.Davies, E. V., Winstanley, C., Fothergill, J. L. & James, C. E. The role of temperate bacteriophages in bacterial infection. FEMS Microbiol. Lett. https://doi.org/10.1093/femsle/fnw015 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
101.Schroven, K., Aertsen, A. & Lavigne, R. Bacteriophages as drivers of bacterial virulence and their potential for biotechnological exploitation. FEMS Microbiol. Rev. https://doi.org/10.1093/femsre/fuaa041 (2020).Article 

Google Scholar 
102.Waldor, M. K. & Mekalanos, J. J. Lysogenic conversion by a filamentous phage encoding cholera toxin. Science 272, 1910–1914 (1996).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
103.Matsuda, M. & Barksdale, L. Phage-directed synthesis of diphtherial toxin in non-toxinogenic Corynebacterium diphtheriae. Nature 210, 911–913 (1966).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
104.O’Brien, A. D. et al. Shiga-like toxin-converting phages from Escherichia coli strains that cause hemorrhagic colitis or infantile diarrhea. Science 226, 694 (1984).PubMed 
Article 
PubMed Central 

Google Scholar 
105.Gerlach, D. et al. Methicillin-resistant Staphylococcus aureus alters cell wall glycosylation to evade immunity. Nature 563, 705–709 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
106.Jahn, M. T. et al. A phage protein aids bacterial symbionts in eukaryote immune evasion. Cell Host Microbe 26, 542–550.e545 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
107.Weynberg, K. D., Voolstra, C. R., Neave, M. J., Buerger, P. & Van Oppen, M. J. H. From cholera to corals: Viruses as drivers of virulence in a major coral bacterial pathogen. Sci. Rep. 5, 17889 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
108.Menouni, R., Hutinet, G., Petit, M. A. & Ansaldi, M. Bacterial genome remodeling through bacteriophage recombination. FEMS Microbiol. Lett. 362, 1–10 (2015).CAS 
PubMed 
Article 

Google Scholar 
109.Feiner, R. et al. A new perspective on lysogeny: prophages as active regulatory switches of bacteria. Nat. Rev. Microbiol. 13, 641–650 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
110.Duerkop, B. A., Clements, C. V., Rollins, D., Rodrigues, J. L. M. & Hooper, L. V. A composite bacteriophage alters colonization by an intestinal commensal bacterium. Proc. Natl Acad. Sci. USA 109, 17621–17626 (2012). Demonstrates that temperate virus infections (including those derived from distinct, spatially separated prophage elements) can ‘make winners’ out of their hosts by providing the hosts with competitive advantages.CAS 
PubMed 
Article 

Google Scholar 
111.Gama, J. A. et al. Temperate bacterial viruses as double-edged swords in bacterial warfare. PLoS ONE https://doi.org/10.1371/journal.pone.0059043 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
112.Davies, E. V. et al. Temperate phages enhance pathogen fitness in chronic lung infection. ISME J. 10, 2553–2555 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
113.Bossi, L., Fuentes, J. A., Mora, G. & Figueroa-Bossi, N. Prophage contribution to bacterial population dynamics. J. Bacteriol. 185, 6467–6471 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
114.Basso, J. T. R. et al. Genetically similar temperate phages form coalitions with their shared host that lead to niche-specific fitness effects. ISME J. 14, 1688–1700 (2020). Demonstrates that two genetically similar, but incompatible, temperate phages that lysogenize the same Roseobacter host can impart distinct physiological traits on that host; thus, each makes its host ‘the winner’ under different environmental conditions.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
115.Li, X. Y. et al. Temperate phages as self-replicating weapons in bacterial competition. J. R. Soc. Interface https://doi.org/10.1098/rsif.2017.0563 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
116.Weitz, J. S. et al. Phage-bacteria infection networks. Trends Microbiol. 21, 82–91 (2013).CAS 
PubMed 
Article 

Google Scholar 
117.Dang, V., Howard-Varona, C., Schwenck, S. & Sullivan, M. B. Variably lytic infection dynamics of large Bacteroidetes podovirus phi38:1 against two Cellulophaga baltica host strains. Environ. Microbiol. 17, 4659–4671 (2015).CAS 
PubMed 
Article 

Google Scholar 
118.Holmfeldt, K., Howard-Varona, C., Solonenko, N. & Sullivan, M. B. Contrasting genomic patterns and infection strategies of two co-existing Bacteroidetes podovirus genera. Environ. Microbiol. 16, 2501–2513 (2014).CAS 
PubMed 
Article 

Google Scholar 
119.Flores, C. O., Meyer, J. R., Valverde, S., Farr, L. & Weitz, J. S. Statistical structure of host–phage interactions. Proc. Natl Acad. Sci. USA 108, E288–E297 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
120.Parmar, K. M., Gaikwad, S. L., Dhakephalkar, P. K., Kothari, R. & Singh, R. P. Intriguing interaction of bacteriophage-host association: an understanding in the era of omics. Front. Microbiol. 8, 559 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
121.Flores, C. O., Valverde, S. & Weitz, J. S. Multi-scale structure and geographic drivers of cross-infection within marine bacteria and phages. ISME J. 7, 520 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
122.Koskella, B. & Meaden, S. Understanding bacteriophage specificity in natural microbial communities. Viruses 5, 806–823 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
123.Roux, S. et al. Ecology and evolution of viruses infecting uncultivated SUP05 bacteria as revealed by single-cell- and meta- genomics. eLife https://doi.org/10.7554/eLife.03125 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
124.Labonte, J. M. et al. Single-cell genomics-based analysis of virus-host interactions in marine surface bacterioplankton. ISME J. 9, 2386–2399 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
125.Munson-McGee, J. H. et al. A virus or more in (nearly) every cell: ubiquitous networks of virus-host interactions in extreme environments. ISME J. 12, 1706–1714 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
126.Díaz-Muñoz, S. L., Sanjuán, R. & West, S. Sociovirology: conflict, cooperation, and communication among viruses. Cell Host Microbe 22, 437–441 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
127.Landsberger, M. et al. Anti-CRISPR phages cooperate to overcome CRISPR-Cas immunity. Cell 174, 908 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
128.Kieft, K., Zhou, Z. & Anantharaman, K. VIBRANT: automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences. Microbiome 8, 90 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
129.Coutinho, F. H. et al. Marine viruses discovered via metagenomics shed light on viral strategies throughout the oceans. Nat. Commun. https://doi.org/10.1038/ncomms15955 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
130.Alrasheed, H., Jin, R. & Weitz, J. S. Caution in inferring viral strategies from abundance correlations in marine metagenomes. Nat. Commun. 10, 501 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
131.Roossinck, M. J. Metagenomics of plant and fungal viruses reveals an abundance of persistent lifestyles. Front.Microbiol. https://doi.org/10.3389/fmicb.2014.00767 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
132.Bordenstein, S. R. & Bordenstein, S. R. Eukaryotic association module in phage WO genomes from Wolbachia. Nat. Commun. 7, 13155 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
133.Gilmore, M. S. & Miller, O. K. A bacterium’s enemy isn’t your friend. Nature 563, 637–638 (2018).CAS 
PubMed 
Article 

Google Scholar 
134.Callanan, J. et al. RNA phage biology in a metagenomic era. Viruses 10, 386 (2018).PubMed Central 
Article 
CAS 
PubMed 

Google Scholar 
135.Dion, M. B., Oechslin, F. & Moineau, S. Phage diversity, genomics and phylogeny. Nat. Rev. Microbiol. 18, 125–138 (2020).CAS 
PubMed 
Article 

Google Scholar 
136.Ross, A., Ward, S. & Hyman, P. More is better: Selecting for broad host range bacteriophages. Front. Microbiol. https://doi.org/10.3389/fmicb.2016.01352 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
137.de Jonge, P. A. et al. Adsorption sequencing as a rapid method to link environmental bacteriophages to hosts. iScience https://doi.org/10.1016/j.isci.2020.101439 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
138.Deng, L. et al. Viral tagging reveals discrete populations in Synechococcus viral genome sequence space. Nature 513, 242–245 (2014).CAS 
PubMed 
Article 

Google Scholar 
139.Džunková, M. et al. Defining the human gut host–phage network through single-cell viral tagging. Nat. Microbiol. https://doi.org/10.1038/s41564-019-0526-2 (2019).Article 
PubMed 

Google Scholar 
140.Labonte, J. M. et al. Single cell genomics-based analysis of gene content and expression of prophages in a diffuse-flow deep-sea hydrothermal system. Front.Microbiol. https://doi.org/10.3389/fmicb.2019.01262 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
141.Edwards, R. A., McNair, K., Faust, K., Raes, J. & Dutilh, B. E. Computational approaches to predict bacteriophage-host relationships. FEMS Microbiol. Rev. 40, 258–272 (2016).CAS 
PubMed 
Article 

Google Scholar 
142.Jover, L. F., Romberg, J. & Weitz, J. S. Inferring phage–bacteria infection networks from time-series data. R. Soc. Open Sci. 3, 160654 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
143.Woodcroft, B. J. et al. Genome-centric view of carbon processing in thawing permafrost. Nature 560, 49–54 (2018).CAS 
PubMed 
Article 

Google Scholar 
144.Nayfach, S., Shi, Z. J., Seshadri, R., Pollard, K. S. & Kyrpides, N. C. New insights from uncultivated genomes of the global human gut microbiome. Nature 568, 505–510 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
145.Almeida, A. et al. A new genomic blueprint of the human gut microbiota. Nature 568, 499–504 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
146.Pasolli, E. et al. Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography, and lifestyle. Cell 176, 649–662.e620 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
147.Tully, B. J., Graham, E. D. & Heidelberg, J. F. The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. Sci. Data 5, 170203 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
148.Mihara, T. et al. Linking virus genomes with host taxonomy. Viruses 8, 66 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
149.Laffy, P. W. et al. HoloVir: a workflow for investigating the diversity and function of viruses in invertebrate holobionts. Front. Microbiol. https://doi.org/10.3389/fmicb.2016.00822 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
150.Bolduc, B., Youens-Clark, K., Roux, S., Hurwitz, B. L. & Sullivan, M. B. iVirus: facilitating new insights into viral ecology with software and community datasets imbedded in a cyberinfrastructure. ISME J. 11, 7–14 (2017).PubMed 
Article 

Google Scholar 
151.Baran, N., Goldin, S., Maidanik, I. & Lindell, D. Quantification of diverse virus populations in the environment using the polony method. Nat. Microbiol. 3, 62–72 (2018).CAS 
PubMed 
Article 

Google Scholar 
152.Mruwat, N. et al. A single-cell polony method reveals low levels of infected Prochlorococcus in oligotrophic waters despite high cyanophage abundances. ISME J. (2020).153.Martínez-García, M., Santos, F., Moreno-Paz, M., Parro, V. & Antón, J. Unveiling viral–host interactions within the ‘microbial dark matter’. Nat. Commun. 5, 4542 (2014).PubMed 
Article 
CAS 

Google Scholar 
154.Spencer, S. J. et al. Massively parallel sequencing of single cells by epicPCR links functional genes with phylogenetic markers. ISME J. 10, 427–436 (2016).CAS 
PubMed 
Article 

Google Scholar 
155.Bickhart, D. M. et al. Assignment of virus and antimicrobial resistance genes to microbial hosts in a complex microbial community by combined long-read assembly and proximity ligation. Genome Biol. 20, 153 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
156.Marbouty, M., Baudry, L., Cournac, A. & Koszul, R. Scaffolding bacterial genomes and probing host-virus interactions in gut microbiome by proximity ligation (chromosome capture) assay. Sci. Adv. 3, e1602105 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
157.Lopez-Madrigal, S., Latorre, A., Porcar, M., Moya, A. & Gil, R. Mealybugs nested endosymbiosis: going into the ‘matryoshka’ system in Planococcus citri in depth. BMC Microbiol. https://doi.org/10.1186/1471-2180-13-74 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
158.Noda, S. et al. Cospeciation in the triplex symbiosis of termite gut protists (Pseudotrichonympha spp.), their hosts, and their bacterial endosymbionts. Mol. Ecol. 16, 1257–1266 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
159.Woyke, T. & Schulz, F. Entities inside one another – a matryoshka doll in biology? Environ. Microbiol. Rep. 11, 26–28 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
160.Chatterjee, A. & Duerkop, B. A. Beyond bacteria: Bacteriophage-eukaryotic host interactions reveal emerging paradigms of health and disease. Front. Microbiol. https://doi.org/10.3389/fmicb.2018.01394 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
161.Bordenstein, S. R., Marshall, M. L., Fry, A. J., Kim, U. & Wernegreen, J. J. The tripartite associations between bacteriophage, Wolbachia, and arthropods. PLOS Pathog. 2, e43 (2006).PubMed 
PubMed Central 
Article 

Google Scholar 
162.Shropshire, J. D., On, J., Layton, E. M., Zhou, H. & Bordenstein, S. R. One prophage WO gene rescues cytoplasmic incompatibility in Drosophila melanogaster. Proc. Natl Acad. Sci. USA 115, 4987 (2018). One of the genes in Wolbachia-infecting prophage WO that was previously shown to induce cytoplasmic incompatibility (in combination with a second gene) in insect gametes is demonstrated to also independently rescue cytoplasmic incompatibility and nullify associated embryonic defects.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
163.Beckmann, J. F. et al. The toxin–antidote model of cytoplasmic incompatibility: Genetics and evolutionary implications. Trends Genet. 35, 175–185 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
164.Sweere, J. M. et al. Bacteriophage trigger antiviral immunity and prevent clearance of bacterial infection. Science 363, eaat9691 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
165.Marquez, L. M., Redman, R. S., Rodriguez, R. J. & Roossinck, M. J. A virus in a fungus in a plant: Three-way symbiosis required for thermal tolerance. Science 315, 513–515 (2007). An early example of a mutualistic ‘nested’ symbiosis involving viruses; in this case, the direct fungal host of a virus as well as the plant host of the fungus benefitted from viral infection.CAS 
PubMed 
Article 

Google Scholar 
166.van Oppen, M. J. H., Leong, J.-A. & Gates, R. D. Coral-virus interactions: a double-edged sword? Symbiosis 47, 1–8 (2009).Article 

Google Scholar 
167.Tikhe, C. V. & Husseneder, C. Metavirome sequencing of the termite gut reveals the presence of an unexplored bacteriophage community. Front. Microbiol. https://doi.org/10.3389/fmicb.2017.02548 (2018).Article 
PubMed 
PubMed Central 

Google Scholar  More

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