More stories

  • in

    Cooperative partner choice in multi-level male dolphin alliances

    1.West, S. A. & Ghoul, M. Conflict within cooperation. Curr. Biol. 29, R425–R426. https://doi.org/10.1016/j.cub.2019.04.028 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    2.Darwin, C. The Origin of Species. (John Murray, 1859).3.Pennisi, E. How did cooperative behavior evolve?. Science 309, 93–93. https://doi.org/10.1126/science.309.5731.93 (2005).CAS 
    Article 
    PubMed 

    Google Scholar 
    4.Ghoul, M., Andersen, S. B. & West, S. A. Sociomics: Using omic approaches to understand social evolution. Trends Genet. 33, 408–419. https://doi.org/10.1016/j.tig.2017.03.009 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    5.Kay, T., Lehmann, L. & Keller, L. Kin selection and altruism. Curr. Biol. 29, R438–R442. https://doi.org/10.1016/j.cub.2019.01.067 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    6.Rodrigues, A. M. & Kokko, H. Models of social evolution: Can we do better to predict ‘who helps whom to achieve what’?. Philos. Trans. R. Soc. Lond. B Biol. Sci. 371, 20150088 (2016).Article 

    Google Scholar 
    7.Strassmann, J. E., Page, R. E. Jr., Robinson, G. E. & Seeley, T. D. Kin selection and eusociality. Nature 471, E5. https://doi.org/10.1038/nature09833 (2011).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    8.Silk, J. B. Nepotistic cooperation in non-human primate groups. Philos. Trans. R. Soc. Lond. B Biol. Sci. 364, 3243–3254. https://doi.org/10.1098/rstb.2009.0118 (2009).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    9.Foerster, S. et al. Social bonds in the dispersing sex: Partner preferences among adult female chimpanzees. Anim. Behav. 105, 139–152. https://doi.org/10.1016/j.anbehav.2015.04.012 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    10.Bourke, A. F. G. Hamilton’s rule and the causes of social evolution. Philos. Trans. R. Soc. Lond. B Biol. Sci. https://doi.org/10.1098/rstb.2013.0362 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    11.Hamilton, W. D. The genetical evolution of social behaviour. I. II. J. Theor. Biol. 7, 1–52 (1964).CAS 
    Article 

    Google Scholar 
    12.Chapais, B. In Cooperation in Primates and Humans: Mechanisms and Evolution (eds Kappeler, P. M. & van Schaik, C. P.) 47–64 (Springer, 2006).13.Borgeaud, C. & Bshary, R. Wild vervet monkeys trade tolerance and specific coalitionary support for grooming in experimentally induced conflicts. Curr. Biol. 25, 3011–3016. https://doi.org/10.1016/j.cub.2015.10.016 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    14.Massen, J. J. M. In Encyclopedia of Animal Cognition and Behavior (eds. Vonk, J. & Shackelford, T.) 1–6 (Springer International Publishing, 2017).15.Cords, M. & Thompson, N. A. In APA Handbook of Comparative Psychology: Basic Concepts, Methods, Neural Substrate, and Behavior, Vol. 1 APA Handbooks in Psychology®. 899–913 (American Psychological Association, 2017).16.Barclay, P. Biological markets and the effects of partner choice on cooperation and friendship. Curr. Opin. Psychol. 7, 33–38. https://doi.org/10.1016/j.copsyc.2015.07.012 (2016).Article 

    Google Scholar 
    17.Samuni, L. et al. Social bonds facilitate cooperative resource sharing in wild chimpanzees. Proc. R. Soc. B Biol. Sci. 285, 20181643 (2018).Article 

    Google Scholar 
    18.St-Pierre, A., Larose, K. & Dubois, F. Long-term social bonds promote cooperation in the iterated Prisoner’s Dilemma. Proc. R. Soc. B Biol. Sci. 276, 4223–4228. https://doi.org/10.1098/rspb.2009.1156 (2009).Article 

    Google Scholar 
    19.Berghänel, A., Ostner, J., Schröder, U. & Schülke, O. Social bonds predict future cooperation in male Barbary macaques, Macaca sylvanus. Anim. Behav. 81, 1109–1116. https://doi.org/10.1016/j.anbehav.2011.02.009 (2011).Article 

    Google Scholar 
    20.Thompson, N. A. Understanding the links between social ties and fitness over the life cycle in primates. Behaviour 156, 859. https://doi.org/10.1163/1568539X-00003552 (2019).Article 

    Google Scholar 
    21.Caro, T. M. Cheetah mothers bias parental investment in favour of cooperating sons. Ethol. Ecol. Evol. 2, 381–395. https://doi.org/10.1080/08927014.1990.9525399 (1990).Article 

    Google Scholar 
    22.Lukas, D. & Clutton-Brock, T. Social complexity and kinship in animal societies. Ecol. Lett. 21, 1129–1134. https://doi.org/10.1111/ele.13079 (2018).Article 
    PubMed 

    Google Scholar 
    23.Clutton-Brock, T. Cooperation between non-kin in animal societies. Nature 462, 51–57 (2009).ADS 
    CAS 
    Article 

    Google Scholar 
    24.Riehl, C. Living with strangers: Direct benefits favour non-kin cooperation in a communally nesting bird. Proc. R. Soc. B Biol. Sci. 278, 1728–1735 (2011).Article 

    Google Scholar 
    25.Carter, G. G. & Wilkinson, G. S. Social benefits of non-kin food sharing by female vampire bats. Proc. R. Soc. B Biol. Sci. 282, 20152524. https://doi.org/10.1098/rspb.2015.2524 (2015).CAS 
    Article 

    Google Scholar 
    26.Boesch, C., Kohou, G., Néné, H. & Vigilant, L. Male competition and paternity in wild chimpanzees of the Taï forest. Am. J. Phys. Anthropol. 130, 103–115. https://doi.org/10.1002/ajpa.20341 (2006).Article 
    PubMed 

    Google Scholar 
    27.Mitani, J. C., Merriwether, D. A. & Zhang, C. Male affiliation, cooperation and kinship in wild chimpanzees. Anim. Behav. 59, 885–893 (2000).CAS 
    Article 

    Google Scholar 
    28.Wroblewski, E. E. et al. Male dominance rank and reproductive success in chimpanzees, Pan troglodytes schweinfurthii. Anim. Behav. 77, 873–885. https://doi.org/10.1016/j.anbehav.2008.12.014 (2009).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    29.Díaz-Muñoz, S. L., Du Val, E. H., Krakauer, A. H. & Lacey, E. A. Cooperating to compete: Altruism, sexual selection and causes of male reproductive cooperation. Anim. Behav. 88, 67–78. https://doi.org/10.1016/j.anbehav.2013.11.008 (2014).Article 

    Google Scholar 
    30.Diaz-Aguirre, F., Parra, G. J., Passadore, C. & Möller, L. Kinship influences social bonds among male southern Australian bottlenose dolphins (Tursiops cf. australis). Behav. Ecol. Sociobiol. 72, 190. https://doi.org/10.1007/s00265-018-2621-4 (2018).Article 

    Google Scholar 
    31.Parsons, K. M. et al. Kinship as a basis for alliance formation between male bottlenose dolphins, Tursiops truncatus, in the Bahamas. Anim. Behav. 66, 185–194. https://doi.org/10.1006/anbe.2003.2186 (2003).Article 

    Google Scholar 
    32.Möller, L. M., Beheregaray, L. B., Harcourt, R. G. & Krützen, M. Alliance membership and kinship in wild male bottlenose dolphins (Tursiops aduncus) of southeastern Australia. Proc. R. Soc. B Biol. Sci. 268, 1941–1947. https://doi.org/10.1098/rspb.2001.1756 (2001).Article 

    Google Scholar 
    33.Wells, R. S. In Primates and Cetaceans: Field Research and Conservation of Complex Mammalian Societies (eds Yamagiwa, J. & Karczmarski, L.) 149–172 (Springer Japan, 2014).34.Connor, R. C., Wells, R. S., Mann, J. & Read, A. J. In Cetacean Societies: Field Studies of Dolphins and Whales (eds Mann, J, Connor, R.C., Tyack, P., & Whitehead, H.) 91–126 (University of Chicago Press, 2000).35.Trivers, R. The evolution of reciprocal altruism. Q. Rev. Biol. 46, 35–57 (1971).Article 

    Google Scholar 
    36.Connor, R. C. Pseudo-reciprocity: Investing in mutualism. Anim. Behav. 34, 1562–1566. https://doi.org/10.1016/S0003-3472(86)80225-1 (1986).Article 

    Google Scholar 
    37.Connor, R. C. The benefits of mutualism: A conceptual framework. Biol. Rev. 70, 427–457. https://doi.org/10.1111/j.1469-185X.1995.tb01196.x (1995).Article 

    Google Scholar 
    38.West-Eberhard, M. J. The evolution of social behavior by kin selection. Q. Rev. Biol. 50, 1–33. https://doi.org/10.1086/408298 (1975).Article 

    Google Scholar 
    39.Randić, S., Connor, R. C., Sherwin, W. B. & Krützen, M. A novel mammalian social structure in Indo-Pacific bottlenose dolphins (Tursiops sp.): Complex male alliances in an open social network. Proc. R. Soc. B Biol. Sci. 279, 3083–3090. https://doi.org/10.1098/rspb.2012.0264 (2012).Article 

    Google Scholar 
    40.Krützen, M., Barré, L. M., Connor, R. C., Mann, J. & Sherwin, W. B. ‘O father: where art thou?’—Paternity assessment in an open fission–fusion society of wild bottlenose dolphins (Tursiops sp.) in Shark Bay, Western Australia. Mol. Ecol. 13, 1975–1990. https://doi.org/10.1111/j.1365-294X.2004.02192.x (2004).CAS 
    Article 
    PubMed 

    Google Scholar 
    41.Connor, R. C. & Krützen, M. Male dolphin alliances in Shark Bay: Changing perspectives in a 30-year study. Anim. Behav. 103, 223–235. https://doi.org/10.1016/j.anbehav.2015.02.019 (2015).Article 

    Google Scholar 
    42.Connor, R. C., Heithaus, M. R. & Barre, L. M. Complex social structure, alliance stability and mating access in a bottlenose dolphin ‘super-alliance’. Proc. R. Soc. B Biol. Sci. 268, 263–267 (2001).CAS 
    Article 

    Google Scholar 
    43.Mann, J., Connor, R. C., Barre, L. M. & Heithaus, M. R. Female reproductive success in bottlenose dolphins (Tursiops sp.): Life history, habitat, provisioning, and group-size effects. Behav. Ecol. 11, 210–219. https://doi.org/10.1093/beheco/11.2.210 (2000).Article 

    Google Scholar 
    44.Smolker, R. A., Richards, A. F., Connor, R. C. & Pepper, J. W. Sex differences in patterns of association among Indian Ocean Bottlenose Dolphins. Behaviour 123, 38–69. https://doi.org/10.1163/156853992X00101 (1992).Article 

    Google Scholar 
    45.Krützen, M. et al. Contrasting relatedness patterns in bottlenose dolphins (Tursiops sp.) with different alliance strategies. Proc. R. Soc. B Biol. Sci. 270, 497–502 (2003).Article 

    Google Scholar 
    46.Gerber, L. et al. Affiliation history and age similarity predict alliance formation in adult male bottlenose dolphins. Behav. Ecol. https://doi.org/10.1093/beheco/arz195 (2020).Article 
    PubMed 

    Google Scholar 
    47.Smith, J. E. Hamilton’s legacy: Kinship, cooperation and social tolerance in mammalian groups. Anim. Behav. 92, 291–304. https://doi.org/10.1016/j.anbehav.2014.02.029 (2014).Article 

    Google Scholar 
    48.Connor, R. C. Cooperation beyond the dyad: on simple models and a complex society. Philos. Trans. R. Soc. Lond. B Biol. Sci. 365, 2687–2697. https://doi.org/10.1098/rstb.2010.0150 (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    49.Krzyszczyk, E., Patterson, E. M., Stanton, M. A. & Mann, J. The transition to independence: Sex differences in social and behavioural development of wild bottlenose dolphins. Anim. Behav. 129, 43–59. https://doi.org/10.1016/j.anbehav.2017.04.011 (2017).Article 

    Google Scholar 
    50.Molesti, S. & Majolo, B. Cooperation in wild Barbary macaques: Factors affecting free partner choice. Anim. Cogn. 19, 133–146. https://doi.org/10.1007/s10071-015-0919-4 (2016).Article 
    PubMed 

    Google Scholar 
    51.Carter, G. G. & Wilkinson, G. S. Food sharing in vampire bats: Reciprocal help predicts donations more than relatedness or harassment. Proc. R. Soc. B Biol. Sci. 280, 20122573–20122573. https://doi.org/10.1098/rspb.2012.2573 (2013).Article 

    Google Scholar 
    52.Young, C., Majolo, B., Schülke, O. & Ostner, J. Male social bonds and rank predict supporter selection in cooperative aggression in wild Barbary macaques. Anim. Behav. 95, 23–32. https://doi.org/10.1016/j.anbehav.2014.06.007 (2014).Article 

    Google Scholar 
    53.Gilby, I. C. et al. Fitness benefits of coalitionary aggression in male chimpanzees. Behav. Ecol. Sociobiol. 67, 373–381. https://doi.org/10.1007/s00265-012-1457-6 (2013).Article 
    PubMed 

    Google Scholar 
    54.Cronin, K. A. Prosocial behaviour in animals: The influence of social relationships, communication and rewards. Anim. Behav. 84, 1085–1093. https://doi.org/10.1016/j.anbehav.2012.08.009 (2012).Article 

    Google Scholar 
    55.Schino, G. & Aureli, F. In Advances in the Study of Behavior, Vol. 39, 45–69 (Academic Press, 2009).56.Watts, D. P. & Mitani, J. C. Boundary patrols and intergroup encounters in wild chimpanzees. Behaviour 138, 299–327 (2001).Article 

    Google Scholar 
    57.Connor, R. C., Watson-Capps, J. J., Sherwin, W. B. & Krützen, M. A new level of complexity in the male alliance networks of Indian Ocean bottlenose dolphins (Tursiops sp.). Biol. Lett. 7, 623–626. https://doi.org/10.1098/rsbl.2010.0852 (2011).Article 
    PubMed 

    Google Scholar 
    58.Silk, J. B., Alberts, S. C. & Altmann, J. Social relationships among adult female baboons (Papio cynocephalus) II. Variation in the quality and stability of social bonds. Behav. Ecol. Sociobiol. 61, 197–204. https://doi.org/10.1007/s00265-006-0250-9 (2006).Article 

    Google Scholar 
    59.Silk, J. B. et al. Female chacma baboons form strong, equitable, and enduring social bonds. Behav. Ecol. Sociobiol. 64, 1733–1747. https://doi.org/10.1007/s00265-010-0986-0 (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    60.Silk, J. B., Alberts, S. C., Altmann, J., Cheney, D. L. & Seyfarth, R. M. Stability of partner choice among female baboons. Anim. Behav. 83, 1511–1518. https://doi.org/10.1016/j.anbehav.2012.03.028 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    61.Mitani, J. C. Male chimpanzees form enduring and equitable social bonds. Anim. Behav. 77, 633–640. https://doi.org/10.1016/j.anbehav.2008.11.021 (2009).Article 

    Google Scholar 
    62.Bizzozzero, M. R. et al. Tool use and social homophily among male bottlenose dolphins. Proc. R. Soc. B Biol. Sci. 286, 20190898 (2019).CAS 
    Article 

    Google Scholar 
    63.Massen, J. J. M. & Koski, S. E. Chimps of a feather sit together: Chimpanzee friendships are based on homophily in personality. Evol. Hum. Behav. 35, 1–8. https://doi.org/10.1016/j.evolhumbehav.2013.08.008 (2014).Article 

    Google Scholar 
    64.Mourier, J., Vercelloni, J. & Planes, S. Evidence of social communities in a spatially structured network of a free-ranging shark species. Anim. Behav. 83, 389–401. https://doi.org/10.1016/j.anbehav.2011.11.008 (2012).Article 

    Google Scholar 
    65.Mitani, J. C., Watts, D. P., Pepper, J. W. & Merriwether, D. A. Demographic and social constraints on male chimpanzee behaviour. Anim. Behav. 64, 727–737. https://doi.org/10.1006/anbe.2002.4014 (2002).Article 

    Google Scholar 
    66.Ruckstuhl, K. E. & Neuhaus, P. Behavioral synchrony in ibex groups: Effects of age, sex and habitat. Behaviour 138, 1033. https://doi.org/10.1163/156853901753286551 (2001).Article 

    Google Scholar 
    67.Hammerstein, P. & Noë, R. Biological trade and markets. Philos. Trans. R. Soc. Lond. B Biol. Sci. https://doi.org/10.1098/rstb.2015.0101 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    68.Sandel, A. A., Langergraber, K. E. & Mitani, J. C. Adolescent male chimpanzees (Pan troglodytes) form social bonds with their brothers and others during the transition to adulthood. Am. J. Primatol. 82, e23091. https://doi.org/10.1002/ajp.23091 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    69.Sherman, P. W. Kinship, demography, and belding’s ground squirrel nepotism. Behav. Ecol. Sociobiol. 8, 251–259 (1981).Article 

    Google Scholar 
    70.Faaborg, J. et al. Confirmation of cooperative polyandry in the Galapagos hawk (Buteo galapagoensis). Behav. Ecol. Sociobiol. 36, 83–90 (1995).Article 

    Google Scholar 
    71.Heinsohn, R. G. Kidnapping and reciprocity in cooperatively breeding white-winged choughs. Anim. Behav. 41, 1097–1100. https://doi.org/10.1016/S0003-3472(05)80652-9 (1991).Article 

    Google Scholar 
    72.Tang-Martinez, Z. The mechanisms of kin discrimination and the evolution of kin recognition in vertebrates: A critical re-evaluation. Behav. Proc. 53, 21–40. https://doi.org/10.1016/S0376-6357(00)00148-0 (2001).CAS 
    Article 

    Google Scholar 
    73.Nolin, D. A. Kin preference and partner choice. Hum. Nat. 22, 156–176. https://doi.org/10.1007/s12110-011-9113-9 (2011).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    74.Suchak, M., Eppley, T. M., Campbell, M. W. & de Waal, F. B. M. Ape duos and trios: spontaneous cooperation with free partner choice in chimpanzees. PeerJ 2, e417. https://doi.org/10.7717/peerj.417 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    75.Gale, D. & Shapley, L. S. College admissions and the stability of marriage. Am. Math. Mon. 69, 9–15 (1962).MathSciNet 
    Article 

    Google Scholar 
    76.Krützen, M. et al. A biopsy system for small cetaceans: darting success and wound healing in Tursiops spp.. Mar. Mamm. Sci. 18, 863–878. https://doi.org/10.1111/j.1748-7692.2002.tb01078.x (2002).Article 

    Google Scholar 
    77.King, S. L. et al. Bottlenose dolphins retain individual vocal labels in multi-level alliances. Curr. Biol. 28, 1993-1999.e1993. https://doi.org/10.1016/j.cub.2018.05.013 (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    78.R: A Language and Environment for Statistical Computing v. 3.4.0. (R Foundation for Statistical Computing, Vienna, Austria, 2017).79.Farine, D. R. Animal social network inference and permutations for ecologists in R using asnipe. Methods Ecol. Evol. 4, 1187–1194 (2013).Article 

    Google Scholar 
    80.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

    Google Scholar 
    81.Wang, J. Triadic IBD coefficients and applications to estimating pairwise relatedness. Genet. Res. 89, 135–153. https://doi.org/10.1017/S0016672307008798 (2007).CAS 
    Article 
    PubMed 

    Google Scholar 
    82.Wang, J. Coancestry: A program for simulating, estimating and analysing relatedness and inbreeding coefficients. Mol. Ecol. Resourc. 11, 141–145. https://doi.org/10.1111/j.1755-0998.2010.02885.x (2011).Article 

    Google Scholar 
    83.Fox, J. & Weisberg, S. An R Companion to Applied Regression. 3rd edn (Sage, 2019).84.Connor, R. C., Richards, A. F., Smolker, R. A. & Mann, J. Patterns of female attractiveness in Indian Ocean Bottlenose Dolphins. Behaviour 133, 37–69 (1996).Article 

    Google Scholar  More

  • in

    Injury alters motivational trade-offs in calves during the healing period

    This work was undertaken at the University of California Davis Dairy Teaching and Research Facility from June to September 2018. All experimental protocols were approved by and carried out in accordance with the University of California Davis Institutional Animal Care and Use Committee (protocol # 20505).TreatmentsWe enrolled all female calves born between June 19 and September 1 2018, for a total of 28 Holsteins and 8 Jerseys. Our sample size was determined by the availability of calves being born in our herd of approximately 105 lactating cows during this period. Calves were blocked by birth order and randomly allocated to 1 of 3 treatments balanced for breed: disbudded the morning of (Day 0) or 21 days before (Day 21) the startle test, or sham-disbudded (Sham, n = 12/treatment). Among the control calves, half were sham-disbudded the morning of the test, whereas the other half underwent the procedure 21 days earlier. Birth weights were similar across treatments (mean ± SD; Day 0: 35 ± 5 kg; Day 21: 35 ± 6 kg; Sham: 36 ± 9 kg). The startle test occurred between 25 and 32 days of age for all calves. Thus, all Day 0 calves and half of the Sham calves were disbudded between 25 and 32 days of age, and all Day 21 calves and half of the Sham calves were disbudded between 4 and 11 days of age. This design meant all animals were at the same stage of cognitive and motor development during data collection. This was a priority for us because we expected age to strongly influence behavioural responses during the startle test. While it is also possible that disbudding at different ages may affect responses, previous research suggests disbudding has similar outcomes across this range13,15,20.Animal husbandry and housingImmediately after birth, calves were housed individually in outdoor enclosures consisting of a plastic hutch (2.0 m long × 1.5 m wide) and a wire-fenced pen (2.0 m long × 1.5 wide × 0.9 m high). The enclosures were spaced 0.5 m apart and bedded with sand approximately 15 to 20 cm deep.Calves were bottle-fed colostrum twice a day for 5 days. From 5 days of age, calves received milk replacer (26% CP and 16% fat, 15% total solids; Calva Products Inc., Acampo, CA) in bottles at 0645, 1245, and 1845 h. At each meal, Holsteins were fed 1.9 L from 1 to 13 days, 2.4 L from 14 to 23 days, and 2.8 L from 24 days. Jerseys received 1.4 L from 1 to 13 days, 1.9 L from 14 to 23 days, and 2.4 L from 24 days. Water and starter (18.3% CP, 2.8% fat, 4% crude fat; Associated Feed & Supply Co., Turlock, CA) were provided ad libitum in buckets. As part of a separate concurrent study, 11 calves (3 Sham, 3 Day 21, 5 Day 0) received chopped mountain grass hay (34% CP) ad libitum.DisbuddingDisbudding occurred between 730 and 1000 h. For the procedure, the calf was restrained in a head device in her home enclosure21. A 5 × 5 cm patch of hair was clipped with a size 40 electric razor blade on each side of the head to locate the horn bud. We used a 20 gauge × 25 mm needle to administer a cornual nerve block consisting of 5.5 mL buffered lidocaine (2% lidocaine hydrochloride diluted with 8.4% sodium bicarbonate in a 10:1 ratio). If the horn bud was not numb after 10 min, as assessed by pinprick, we gave an additional 2 mL of buffered lidocaine (13% of horn buds). An electric cautery iron (X50, Rhinehart Development Corp., Spencerville, IN) was fitted with a 1.3 cm tip and heated to 439 ± 15 °C (mean ± SD). It was applied to the horn bud for 17 ± 5 s (mean ± SD). Immediately before disbudding, the calf received approximately 1 mg/kg of meloxicam tablets in a gelatin capsule (3.5 g; Torpac Inc., Fairfield, NJ). For Day 0 calves, meloxicam was given after the startle test had occurred later that same day (maximum 12 h later) to ensure the calf was in a drug-free state during the test. Sham-disbudded calves received the same treatment, with the exception that the iron was ambient temperature and the gelatin capsule was empty. Sham calves did not receive meloxicam because the Animal Medicinal Drug Use Clarification Act limits nontherapeutic off-label use of this drug22. SJJA performed all disbudding procedures.ArenaWe tested calves individually in a single 10-min period in a shaded outdoor arena bedded with 10 to 15 cm of sand. The arena was divided into a waiting pen (2.0 × 1.5 m) and a test pen (3.0 × 5.5 m) constructed of 0.9 m high wire panels (MidWest Homes for Pets Foldable Metal Exercise pen, Muncie, IN). A rolling gate provided access between the pens (Fig. 1).Figure 1Aerial view of the arena used for startle tests, including the position of the milk bottle and speaker used to broadcast the startle noise. Figure is drawn to scale.Full size imageA bottle containing 500 mL of the calves’ regular milk replacer was secured to the panel opposite the entrance to the test pen. The bottle was fitted with a rubber teat positioned 80 cm above the ground. Between calves, a fresh bottle was placed in the arena and urine and feces were removed with a shovel.Testing procedureCalves were habituated to the arena for 15 min daily between 700 and 1100 h for 3 consecutive days before the startle test. Calves were brought to the arena in the same order each day, with order balanced across treatments. During habituation, no startle stimulus was delivered, but otherwise the same procedure followed on test days was applied.The startle test occurred between 1530 and 1800 h (Supplementary Video S1). The calf was transported from her home pen to the waiting pen in a cart (Caf-Cart, Raytec, Ephrata, PA). The test began when the gate providing access to the test pen was opened and ended after 10 min. The gate was closed behind the calf after she had entered so that the waiting pen was inaccessible during the test. Three observers were seated quietly 3.5 m away from the pen during the test, and were partially concealed behind a tree branch. One observer remotely controlled the speaker broadcasting the startle noise, while the other two observers were present to respond if a calf escaped from the arena (only one calf jumped out, on the first day of habituation, and was promptly escorted back into the pen). Calves showed no apparent responses to the observers and had no visual contact with other animals.As soon as the calf’s mouth was within a tongue’s reach of the teat, a 0.4 s, 105 ± 2 dB burst of white-noise was emitted through a wireless speaker (OT4200 Big Turtle Shell, Outdoor Tech, Laguna Hills, CA) mounted directly behind the bottle. The noise was created using an online signal generator23. We measured the sound level using a decibel meter (BAFX Products, Milwaukee, WI) held 30 cm in front of the bottle, approximating the distance of the calf’s ears to the source.Behavioural data collectionTests were recorded with a camcorder (HC-V180, Panasonic, Kadoma, Japan) positioned on a tripod approximately 3 m away from of the pen. One trained observer, blinded to the treatments, scored behaviours in all videos taken of the startle test and the third day of habituation (Table 1). Videos were analysed using BORIS (Behavioural Observation Research Interactive Software24). Intra-observer reliability was calculated using 12 randomly selected videos of the startle test (Intraclass correlation coeffcient ≥ 0.95).Table 1 Behavioural definitions used to evaluate calves’ responses in an arena test.Full size tableAccelerometers (Hobo Pendant G Acceleration Data Logger, Onset Computer Corporation, Bourne, MA) were used to assess the magnitude of the startle response. On habituation and test days, we fitted calves with a triaxial accelerometer set to record acceleration in the x-, y-, and z-axis every 0.05 s. The accelerometer was placed in a pouch, strapped around the right hind leg, and secured with Vet Wrap (Co-Flex, Andover Coated Products Inc., Salisbury, MA) while the calf was in the waiting pen of the arena, immediately before the gate to the test pen was opened. Data were downloaded using HOBOware Pro Software (Onset Computer Corporation, Bourne, MA). To calculate the magnitude of the startle response, we summed total acceleration in all 3 axes over the startle duration for that calf. Total acceleration was calculated as the square root of the sum of squared acceleration in each axis25. No startle response was recorded for one calf who did not approach the bottle on the test day.All calves were weighed the morning of the startle test (mean ± SD; Day 0: 56 ± 10 kg; Day 21: 55 ± 9 kg; Sham: 55 ± 11 kg).Wound healing and sensitivityWe measured sensitivity via mechanical nociceptive thresholds around the horn bud area 1 to 2 h after the startle test using a digital algometer fitted with a 4-mm-diameter round rubber tip (ProdPlus; TopCat Metrology Ltd., Little Downham, UK). The calf was restrained in the head device in her home pen and blindfolded to reduce responses to visual cues. We then applied an increasing amount of force to the edge of the disbudding wound, or intact horn bud for sham calves, as described previously13. The test ended when the calf moved her head or a maximum cut-off point of 10 N was reached. We repeated the test if a fly landed on the head, a loud noise occurred, or the calf urinated or defecated. If a test was interrupted 3 times, it was abandoned (0% of tests).Wound sensitivity was tested at the lateral and caudal edges of each wound or the equivalent location on sham calves. The order of test sites was: left lateral, left caudal, right caudal, and right lateral. To ensure force was applied at a consistent rate, personnel operating the algometer were trained and met a set of rigorous criteria before performing the tests13. We calculated the rate that force was applied in each test from video recordings (0.29 ± 0.10 N/s; 2% of videos missing). If force was increased at a rate  0.6 N/s or video was missing, the data were excluded (3% of tests). Due to the nature of the tests, the operator of the algometer was not blind to treatment.We took digital photographs of the wound with a DSLR camera (D5300; Nikon Corp., Tokyo, Japan) after sensitivity testing was completed. Photos were taken 15 cm from the wound. One person scored the photos for tissues present in the wound bed using a 0/1 scoring system13. Due to the clear differences in Day 0 and Day 21 wounds, the scorer was not blind to treatment.Statistical analysisDue to the presence of zeros in the data, we used zero-inflated beta regressions to assess the effect of treatment (Sham, Day 0, Day 21) on the proportion of time suckling on the third day of habituation and during the startle test. A zero-inflated beta regression is a mixture of two models: a beta model for estimating non-zero proportions and a logistic model for estimating the probability of zeroes26. This approach allowed us to infer treatment effects on both the occurrence and duration of suckling. General linear models were used to test the effect of treatment on the duration of the startle response and its magnitude as measured from the accelerometer data.We analyzed the effects of treatment on latency to approach the bottle and latency to return after startling using parametric survival regression models with a log-logistic distribution. Days on which the calf did not perform the behaviour within the allotted time (15 min for habituation, 10 min for startle test) were handled as right-censored data.We ran a general linear model to test the effect of treatment on wound sensitivity. A preliminary analysis indicated that there was no effect of side (left vs right) or location (caudal vs lateral) on wound sensitivity, so we averaged data for each calf into one score.Data were analysed in R, version 3.5.227. General linear models were fitted using the “lm” function in base R. We confirmed homogeneity of variance and normality using residuals vs fits plots and Q-Q plots, respectively. Beta and survival regressions were performed with the “glmmTMB” function in the glmmTMB package version 1.0.028, and the “survreg” function in the survival package version 2.3829, respectively. If treatment effects were identified in any of the models (P  More

  • in

    Mercury content in the Siberian tiger (Panthera tigris altaica Temminck, 1844) from the coastal and inland areas of the Russia

    This is the first study to evaluate the mercury content in the fur of Siberian tigers in the Far East of Russia. It is a commonly recognized fact that fish are the main source of mercury entering the organism of predators and the trophic network of the ecosystem. In some areas, the seasonal abundance of salmonids can provide tigers with protein: masu (Oncorhynchus masou) from April to early July, chum (O. keta) in late autumn and up to December and pink salmon (O. gorbuscha) in July–early October. However, our observations during 1976–2018 (15 (Poddubnaya, unpublished data)) and data on mercury content show that tigers do not eat salmon often. Tigers do not hunt the redfin dace (Tribolodon hakonensis) a cyprinid fish, moving up in huge swarms from April to June.Although tigers were never observed to consume fish in mass quantities, as is the case with bears, one would expect that the total concentration of mercury (THg) in the body of tigers from two sections of the Sikhote-Alin (basin drainage of the Sea of Japan and the catchment of the Amur River) would vary depending on the availability of anadromous fish. The rivers of the Sea of Japan basin are shorter and shallower than Amur’s tributaries. Therefore, the likelihood of a tiger catching fish here seems to be higher. However, Amur’s tributaries are richer in fish and the probability of catching fish has to be no less than that observed in the coast. Thus, it can be assumed that the proportion of fish in tiger’s diet on the coast and in the inland region should be similar. Apparently, the consumption of salmon by tigers can be neglected in this analysis.The minor role of fish in the Siberian tiger diet is further evidenced by comparing its average mercury content with other large felids known to consume fish and other aquatic animals. Thus, individuals of Florida panther (Puma concolor coryi), consuming aquatic and fish-eating animals have elevated levels of MMHg—1.62 ± 1.87 mg/kg or 1.84 mg/kg THg 20. The average mercury concentration in the jaguar (Panthera onca), mainly preying on fish and alligators, reaches even higher values of up to 4.27 mg/kg (from 2.13 to 7.26 mg/kg) 21. The average THg content in the Siberian tiger is 0.383 ± 0.062 mg/kg indicating that it eats little fish, if any.In the south of the Russian Far East, some ungulates caught by the tiger on the eastern macro slope of Sikhote-Alin periodically go to the sea to lick salt and eat algae. This can lead to some increase in the level of mercury in their tissues and in the tiger along the trophic chain. In addition, ungulates, especially deer, can eat various lichens, including Usnea in the temperate forests.As we found out, THg in lichens from the coast, where sea fog is observed, was 0.170 ± 0.017 mg kg−1 (n = 30), which is 2.6 times higher than the average value for inland areas (0.065 ± 0.004 mg kg−1 (n = 24) (Fig. 1A). The absolute values of THg in Usnea lichens from the coast in the south of the Russian Far East turned out to be higher than in the Ramalina menziesii lichens (from the same order Lecanorales and the same ecological form as Usnea) from the coast in California 3 (0.138 ± 0.012 mg kg−1). It is possible that such differences are related to species-specific features of their thalli. Different species of lichens from the same locality can accumulate different amounts of toxic substances in their thallus. Thus, Usnea contained 0.170 ± 0.017 mg kg−1, and the mesomorphic evernia (Evernia mesomorpha) collected on the same site—0.292 mg kg−1(n = 2).Figure 1Map of sampling sites and mean values of (A) THg concentrations in lichen (Usnea sp.) (site names correspond to data in Table 1), (B) THg in tiger fur. The blue triangles and circles represent the samples from coastal sub-region and the black—from inland sub-region. Lichen sampling was done in 2019 and tiger fur sampling was done in 2004–2014. The map was generated in Adobe Photoshop CS6, based on a map from the public domain on the site https://yandex.ru/legal/maps_termsofuse/?lang=en.Full size imageWeiss-Penzias et al. 3 do not give the average THg for inland lichens, but they show that the high MMHg content in lichens on the coast is obtained through coastal marine atmospheric fog. We compared our data with those for THg on Bathurst Island 22, where the spatial pattern in THg enrichment was very similar to that of MMHg, with enrichment highest at coastal sites and decreasing within 10 km, suggesting similar origins of atmospheric THg and MMHg to lichens. Potential sources of inorganic Hg and MMHg to lichens are diverse (e.g. 3,22). MMHg from THg can range from 4.4 to 23% 3, therefore special future studies are needed to understand the dynamics of Hg species in lichen.The average individual THg concentrations in tiger fur samples from the coast ranged from 0.115 to 0.918 mg kg−1 (n = 12), on average 0.434 ± 0.067 (Fig. 1B, Table 1), while tiger fur samples from the inland regions (n = 12) had lower concentrations of THg (range from 0.057 to 0.950 mg kg−1, average 0.239 ± 0.075); the differences between the means of the two sites were statistically significant (p = 0.01) (Fig. 1B, Table 1).Table 1 Statistics on the subsets of THg concentration data used in this paper.Full size tableThe average concentrations of THg in the tiger fur of two subregions were lower (0.434 ± 0.067 and 0.239 ± 0.075 mg kg−1) than similar values for pumas from California 3. In contrast to California, where the average THg content in pumas from the coastal area was three times higher than in animals from the inland areas, in the Russian Far East the average THg content in tiger fur sampled in the area influenced by the sea fog was two times higher than that in comparable samples from inland areas. Although, it is the inland area (the Amur River basin) that is subject to high levels of human activity, including the mining of coal and gold in the past and present, and we could expect higher levels of mercury in living components of ecosystems. The average concentrations of THg in the tiger fur were only from 0.056 to 0.232 mg kg−1 (n = 4) near coal and gold mining sites.Different age classes were sampled in both the coastal and inland areas, and THg concentrations increased with age (adult  > young) in both areas (Table 1). This pattern is typical for predatory animals in general and for pumas, in particular 3, which is natural due to the cumulative effect and increasing mercury content with age 22,23,24,25. Differences in the average individual mercury content in individual fur samples of young and adult tigers were significant (p = 0.04) (Table 1). Moreover, the differences between the average mercury content in young and adult tigers were insignificant in the inland site (p = 0.32), while being significant on the coast (p = 0.04) (Table 1).We did not observe any significant differences in THg concentrations between the sexes (p = 0.86) (Table 1) and between males from the coast and the inland (p = 0.25) (Table 1) as was noted earlier for puma 3,21. On the contrary, the average values of mercury in the fur of individuals from the coast were 3.1 times higher than from the inland sites within the group of females, this difference was statistically significant (p = 0.03) (Table 1). These data contradict to what was observed in puma by Weiss-Penzias et al. 3. Such feature of female tiger is apparently associated with their shorter migration routes and smaller individual territories compared to males 26,27. Unlike males, which can cross the main Sikhote-Alin ridge, females are usually located either on the territory in the zone of sea fog influence, or in the inland areas.In addition, young females often remain within the territory of their mothers during dispersal 15. Rather similar information was obtained for the wild European cat 28, where THg in females was about 1.4 times higher than in males, although the differences were not statistically significant. There were no significant differences in THg content between young tigers in coastal and inland areas, as well as in the samples of animals, which died in autumn–winter and spring-early summer (Table 1).Apparently, preying on land animals does not lead to the accumulation of high Hg levels in felids. The average Hg levels in the fur of a near-water species such as the ocelot (Felis pardalis) mainly preying on terrestrial animals, varied in the same range as the tiger: 0.5–1.25 mg kg−1 29. Tigers are mainly consumers of the second level and therefore the average content of mercury in their body (0.383 ± 0.062) (Table 1) is lower than in the fur of consumers of the third level such as the pine marten 1.80 ± 1.34 mg kg−1 30 or Daubenton’s bat 1.15 ± 0.27 mg kg−1 31.The only sample with a maximum mercury content of 1.402 mg kg−1 (age and gender unknown) was from the southwestern Primorye, which is located on the coast and where there are cinnabar deposits nearby. This sample was not used in the total analysis. Interestingly, the available sample of a young female Far Eastern leopard fur (Panthera pardus orientalis) from the same site had practically the same mercury content (1.456 mg kg−1). Local increased mercury content in the body of tigers can be associated with deposits of mercury-containing minerals. These data do not confuse our understanding of the sources of mercury in the ecosystem; they serve as a signal for a more profound study of natural processes.Our data on a higher mercury level (THg) in lichens and tigers of the sea coast compared to inland areas may be related to the effects of coastal sea atmospheric fog, a potential source of monomethylmercury (MMHg) produced in the ocean 3.The levels of mercury we found in Siberian tigers from the Russian Far East are about four times lower than the mercury content in the fur and vibrissa of puma from California 3. It seems that such differences are related to the position of these regions relative to the zones of deep faults of the mantle formation of the East Pacific platform 32. The maximum concentrations of Hg in the near-surface atmosphere are confined to such zones, and the concentrations decrease at a distance from them. California is located closer to such zones, while the south of the Russian Far East is further away.And the fact that different levels of mercury in ecosystems depend on the distance relative to the deep faults of the East Pacific platform is confirmed, for example, by pink salmon: fish from the Sea of Japan contain much less mercury than fish from the Kuril region closer to the fault zone (from 0.045 to 0.087 mg kg−1 wet weight 31. At the same time, we must not forget that California is the most populated and one of the most industrially developed states in the USA. However, it seems that natural processes currently play the main role in formation of heavy metal content in the discussed populations. Thus, lead concentrations in organs and tissues (liver, gonads, and muscle) of fish from Kuril oceanic waters was one and a half order of magnitude higher than that of pink salmon from the Sea of Japan 33.If the global anthropogenic mercury pollution of terrestrial and aquatic ecosystems continues, coastal food webs in the zone of influence of the East Pacific platform will be at most risk of toxicological effects. More

  • in

    Revisiting the rules of life for viruses of microorganisms

    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

  • in

    Distributions of Arctic and Northwest Atlantic killer whales inferred from oxygen isotopes

    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

  • in

    Increasing availability of palatable prey induces predator-dependence and increases predation on unpalatable prey

    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

  • in

    Total body irradiation causes a chronic decrease in antioxidant levels

    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

  • in

    Finding Nemo’s clock reveals switch from nocturnal to diurnal activity

    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