Ecology

1.
Bateman, A. J. Intra-sexual selection in Drosophila. Heredity (Edinb). 2, 349–368 (1948).
CAS  Article  Google Scholar 
2.
Trivers, R. L. Parental investment and sexual selection. In Sexual Selection and the Descent of Man (Ed. B. Campbell.) 136–179 (Aldinc, Chicago, 1972).

3.
Parker, G. A. & Pizzari, T. Sexual selection: the logical imperative. In Current Perspectives on Sexual Selection: What’s Left After Darwin? (Ed. T. Horquet.) 119–163 (Springer, Dordrecht, 2015).

4.
Clutton-Brock, T. Reproductive competition and sexual selection. Philos. Trans. R. Soc. Biol. B Sci. 372, 20160310 (2017).
Article  Google Scholar 

5.
Kokko, H., Brooks, R., Jennions, M. D. & Morley, J. The evolution of mate choice and mating biases. Proc. R. Soc. London. Ser. B Biol. Sci. 270, 653–664 (2003).
Article  Google Scholar 

6.
Ihle, M., Kempenaers, B. & Forstmeier, W. Fitness benefits of mate choice for compatibility in a socially monogamous species. PLoS Biol. 13, e1002248 (2015).
Article  CAS  PubMed  PubMed Central  Google Scholar 

7.
Fromhage, L. & Jennions, M. D. Coevolution of parental investment and sexually selected traits drives sex-role divergence. Nat. Commun. 7, 12517 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

8.
Courtiol, A., Etienne, L., Feron, R., Godelle, B. & Rousset, F. The evolution of mutual mate choice under direct benefits. Am. Nat. 188, 521–538 (2016).
Article  Google Scholar 

9.
Byrne, P. G. & Rice, W. R. Evidence for adaptive male mate choice in the fruit fly Drosophila melanogaster. Proc. R. Soc. B Biol. Sci. 273, 917–922 (2006).
Article  Google Scholar 

10.
Simmons, L. W., Lüpold, S. & Fitzpatrick, J. L. Evolutionary trade-off between secondary sexual traits and ejaculates. Trends Ecol. Evol. 32, 964–976 (2017).
Article  PubMed  Google Scholar 

11.
Gwynne, D. T. Sexual competition among females: What causes courtship-role reversal?. Trends Ecol. Evol. 6, 118–121 (1991).
CAS  Article  PubMed  Google Scholar 

12.
Edward, D. A. & Chapman, T. The evolution and significance of male mate choice. Trends Ecol. Evol. 26, 647–654 (2011).
Article  PubMed  Google Scholar 

13.
Vallejos, J. G., Grafe, T. U., Sah, H. H. A. & Wells, K. D. Calling behavior of males and females of a Bornean frog with male parental care and possible sex-role reversal. Behav. Ecol. Sociobiol. 71, 95 (2017).
Article  Google Scholar 

14.
Amundsen, T. & Forsgren, E. Male mate choice selects for female coloration in a fish. Proc. Natl. Acad. Sci. 98, 13155–13160 (2001).
ADS  CAS  Article  PubMed  Google Scholar 

15.
Bonduriansky, R. The evolution of male mate choice in insects: A synthesis of ideas and evidence. Biol. Rev. 76, 305–339 (2001).
CAS  Article  PubMed  Google Scholar 

16.
Servedio, M. R. & Lande, R. Population genetic models of male and mutual mate choice. Evolution (N. Y.). 60, 674–685 (2006).
Google Scholar 

17.
Lailvaux, S. P. & Irschick, D. J. A functional perspective on sexual selection: Insights and future prospects. Anim. Behav. 72, 263–273 (2006).
Article  Google Scholar 

18.
Kirkpatrick, M., Rand, A. S. & Ryan, M. J. Mate choice rules in animals. Anim. Behav. 71, 1215–1225 (2006).
Article  Google Scholar 

19.
Holveck, M.-J. & Riebel, K. Low-quality females prefer low-quality males when choosing a mate. Proc. R. Soc. B Biol. Sci. 277, 153–160 (2009).
Article  Google Scholar 

20.
Aquiloni, L. & Gherardi, F. Mutual mate choice in crayfish: Large body size is selected by both sexes, virginity by males only. J. Zool. 274, 171–179 (2008).
Article  Google Scholar 

21.
Honěk, A. Intraspecific variation in body size and fecundity in insects: a general relationship. Oikos 66, 483–492 (1993).
Article  Google Scholar 

22.
Monroe, M. J., South, S. H. & Alonzo, S. H. The evolution of fecundity is associated with female body size but not female-biased sexual size dimorphism among frogs. J. Evol. Biol. 28, 1793–1803 (2015).
CAS  Article  PubMed  Google Scholar 

23.
Pincheira-Donoso, D. & Hunt, J. Fecundity selection theory: Concepts and evidence. Biol. Rev. 92, 341–356 (2017).
Article  PubMed  Google Scholar 

24.
Dosen, L. D. & Montgomerie, R. Female size influences mate preferences of male guppies. Ethology 110, 245–255 (2004).
Article  Google Scholar 

25.
Kokko, H., Jennions, M. D. & Brooks, R. Unifying and testing models of sexual selection. Annu. Rev. Ecol. Evol. Syst. 37, 43–66 (2006).
Article  Google Scholar 

26.
Booksmythe, I., Mautz, B., Davis, J., Nakagawa, S. & Jennions, M. D. Facultative adjustment of the offspring sex ratio and male attractiveness: A systematic review and meta-analysis. Biol. Rev. 92, 108–134 (2017).
Article  PubMed  Google Scholar 

27.
Hamilton, W. D. & Zuk, M. Heritable true fitness and bright birds: A role for parasites?. Science 218, 384–387 (1982).
ADS  CAS  Article  PubMed  Google Scholar 

28.
Dunn, P. O., Garvin, J. C., Whittingham, L. A., Freeman-Gallant, C. R. & Hasselquist, D. Carotenoid and melanin-based ornaments signal similar aspects of male quality in two populations of the common yellowthroat. Funct. Ecol. 24, 149–158 (2010).
Article  Google Scholar 

29.
Emlen, D. J., Warren, I. A., Johns, A., Dworkin, I. & Lavine, L. C. A mechanism of extreme growth and reliable signaling in sexually selected ornaments and weapons. Science (80-). 337, 860–864 (2012).
ADS  CAS  Article  Google Scholar 

30.
Dhole, S., Stern, C. A. & Servedio, M. R. Direct detection of male quality can facilitate the evolution of female choosiness and indicators of good genes: Evolution across a continuum of indicator mechanisms. Evolution (N.Y.). 72, 770–784 (2018).
Google Scholar 

31.
Roberts, M. L., Buchanan, K. L. & Evans, M. R. Testing the immunocompetence handicap hypothesis: A review of the evidence. Anim. Behav. 68, 227–239 (2004).
Article  Google Scholar 

32.
Joye, P. & Kawecki, T. J. Sexual selection favours good or bad genes for pathogen resistance depending on males’ pathogen exposure. Proc. R. Soc. B 286, 20190226 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

33.
Able, D. J. The contagion indicator hypothesis for parasite-mediated sexual selection. Proc. Natl. Acad. Sci. 93, 2229–2233 (1996).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

34.
Penn, D. & Potts, W. K. Chemical signals and parasite-mediated sexual selection. Trends Ecol. Evol. 13, 391–396 (1998).
CAS  Article  PubMed  PubMed Central  Google Scholar 

35.
Arakawa, H., Cruz, S. & Deak, T. From models to mechanisms: Odorant communication as a key determinant of social behavior in rodents during illness-associated states. Neurosci. Biobehav. Rev. 35, 1916–1928 (2011).
Article  PubMed  PubMed Central  Google Scholar 

36.
Beltran-Bech, S. & Richard, F.-J. Impact of infection on mate choice. Anim. Behav. 90, 159–170 (2014).
Article  Google Scholar 

37.
Rantala, M. J., Kortet, R., Kotiaho, J. S., Vainikka, A. & Suhonen, J. Condition dependence of pheromones and immune function in the grain beetle, Tenebrio molitor. Funct. Ecol. 17, 534–540 (2003).
Article  Google Scholar 

38.
Wyatt, T. D. Pheromones. Curr. Biol. 27, R739–R743 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

39.
Johansson, B. G. & Jones, T. M. The role of chemical communication in mate choice. Biol. Rev. 82, 265–289 (2007).
Article  PubMed  PubMed Central  Google Scholar 

40.
Koh, T. H., Seah, W. K., Yap, L.-M.Y.L. & Li, D. Pheromone-based female mate choice and its effect on reproductive investment in a spitting spider. Behav. Ecol. Sociobiol. 63, 923–930 (2009).
Article  Google Scholar 

41.
Peso, M., Elgar, M. A. & Barron, A. B. Pheromonal control: Reconciling physiological mechanism with signalling theory. Biol. Rev. 90, 542–559 (2015).
Article  PubMed  Google Scholar 

42.
Roberts, S. C., Gosling, L. M., Thornton, E. A. & McClung, J. Scent-marking by male mice under the risk of predation. Behav. Ecol. 12, 698–705 (2001).
Article  Google Scholar 

43.
Foster, S. P. & Anderson, K. G. Sex pheromones in mate assessment: Analysis of nutrient cost of sex pheromone production by females of the moth, Heliothis virescens. J. Exp. Biol. 218, 1252–1258 (2015).
Article  PubMed  Google Scholar 

44.
Happ, G. M. Multiple sex pheromones of the mealworm beetle, Tenebrio molitor L.. Nature 222, 180 (1969).
ADS  CAS  Article  PubMed  Google Scholar 

45.
Stökl, J. & Steiger, S. Evolutionary origin of insect pheromones. Curr. Opin. Insect Sci. 24, 36–42 (2017).
Article  PubMed  Google Scholar 

46.
Roitberg, B. D. Chemical communication. in Insect Behavior: From Mechanisms to Ecological and Evolutionary Consequences (eds. Córdoba-Aguilar et al.) vol. I 416 (Oxford University Press, 2018).

47.
Hurd, H. & Parry, G. Metacestode-induced depression of the production of, and response to, sex pheromone in the intermediate host, Tenebrio molitor. J. Invertebr. Pathol. 58, 82–87 (1991).
CAS  Article  PubMed  Google Scholar 

48.
McConnell, M. W. & Judge, K. A. Body size and lifespan are condition dependent in the mealworm beetle, Tenebrio molitor, but not sexually selected traits. Behav. Ecol. Sociobiol. 72, 32 (2018).
Article  Google Scholar 

49.
Bryning, G. P., Chambers, J. & Wakefield, M. E. Identification of a sex pheromone from male yellow mealworm beetles, Tenebrio molitor. J. Chem. Ecol. 31, 2721–2730 (2005).
CAS  Article  PubMed  Google Scholar 

50.
Nielsen, M. L. & Holman, L. Terminal investment in multiple sexual signals: Immune-challenged males produce more attractive pheromones. Funct. Ecol. 26, 20–28 (2012).
Article  Google Scholar 

51.
Worden, B. D., Parker, P. G. & Pappas, P. W. Parasites reduce attractiveness and reproductive success in male grain beetles. Anim. Behav. 59, 543–550 (2000).
CAS  Article  PubMed  Google Scholar 

52.
Worden, B. D. & Parker, P. G. Females prefer noninfected males as mates in the grain beetle Tenebrio molitor: Evidence in pre-and postcopulatory behaviours. Anim. Behav. 70, 1047–1053 (2005).
Article  Google Scholar 

53.
Sadd, B. et al. Modulation of sexual signalling by immune challenged male mealworm beetles (Tenebrio molitor, L.): Evidence for terminal investment and dishonesty. J. Evol. Biol. 19, 321–325 (2006).
CAS  Article  PubMed  Google Scholar 

54.
Krams, I. A. et al. Male mealworm beetles increase resting metabolic rate under terminal investment. J. Evol. Biol. 27, 541–550 (2014).
CAS  Article  PubMed  Google Scholar 

55.
Kivleniece, I., Krams, I., Daukšte, J., Krama, T. & Rantala, M. J. Sexual attractiveness of immune-challenged male mealworm beetles suggests terminal investment in reproduction. Anim. Behav. 80, 1015–1021 (2010).
Article  Google Scholar 

56.
Reyes-Ramírez, A., Enríquez-Vara, J. N., Rocha-Ortega, M., Téllez-García, A. & Córdoba-Aguilar, A. Female choice for sick males over healthy males: Consequences for offspring. Ethology 125, 241–249 (2019).
Article  Google Scholar 

57.
Oliveira, A. S., Braga, G. U. L. & Rangel, D. E. N. Metarhizium robertsii illuminated during mycelial growth produces conidia with increased germination speed and virulence. Fungal Biol. 122, 555–562 (2018).
Article  PubMed  Google Scholar 

58.
Sasan, R. K. & Bidochka, M. J. The insect-pathogenic fungus Metarhizium robertsii (Clavicipitaceae) is also an endophyte that stimulates plant root development. Am. J. Bot. 99, 101–107 (2012).
Article  PubMed  Google Scholar 

59.
Barelli, L., Moonjely, S., Behie, S. W. & Bidochka, M. J. Fungi with multifunctional lifestyles: Endophytic insect pathogenic fungi. Plant Mol. Biol. 90, 657–664 (2016).
CAS  Article  PubMed  Google Scholar 

60.
Branine, M., Bazzicalupo, A. & Branco, S. Biology and applications of endophytic insect-pathogenic fungi. PLoS Pathog. 15, e1007831 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

61.
Keyser, C. A., Thorup-Kristensen, K. & Meyling, N. V. Metarhizium seed treatment mediates fungal dispersal via roots and induces infections in insects. Fungal. Ecol. 11, 122–131 (2014).
Article  Google Scholar 

62.
Castro, T. et al. Persistence of Brazilian isolates of the entomopathogenic fungi Metarhizium anisopliae and M. robertsii in strawberry crop soil after soil drench application. Agric. Ecosyst. Environ. 233, 361–369 (2016).
Article  Google Scholar 

63.
Härdling, R. & Kokko, H. The evolution of prudent choice. Evol. Ecol. Res. 7, 697–715 (2005).
Google Scholar 

64.
Venner, S., Bernstein, C., Dray, S. & Bel-Venner, M.-C. Make love not war: When should less competitive males choose low-quality but defendable females?. Am. Nat. 175, 650–661 (2010).
Article  PubMed  Google Scholar 

65.
Bhattacharya, A. K., Ameel, J. J. & Waldbauer, G. P. A method for sexing living pupal and adult yellow mealworms. Ann. Entomol. Soc. Am. 63, 1783 (1970).
Article  Google Scholar 

66.
Silva, W. O. B., Mitidieri, S., Schrank, A. & Vainstein, M. H. Production and extraction of an extracellular lipase from the entomopathogenic fungus, Metarhizium anisopliae. Process Biochem. 40, 321–326 (2005).
Article  CAS  Google Scholar 

67.
Zhou, J., Jiang, W., Ding, J., Zhang, X. & Gao, S. Effect of Tween 80 and β-cyclodextrin on degradation of decabromodiphenyl ether (BDE-209) by white rot fungi. Chemosphere 70, 172–177 (2007).
ADS  CAS  Article  PubMed  Google Scholar 

68.
Liu, Y.-S. & Wu, J.-Y. Effects of Tween 80 and pH on mycelial pellets and exopolysaccharide production in liquid culture of a medicinal fungus. J. Ind. Microbiol. Biotechnol. 39, 623–628 (2012).
CAS  Article  PubMed  Google Scholar 

69.
Gerber, G. H. Reproductive behaviour and physiology of Tenebrio molitor (Coleoptera: Tenebrionidae). III. Histogenetic changes in the internal genitalia, mesenteron, and cuticle during sexual maturation. Can. J. Zool. 54, 990–1002 (1976).
Article  Google Scholar 

70.
Briscoe, A. D. & Chittka, L. The evolution of color vision in insects. Annu. Rev. Entomol. 46, 471–510 (2001).
CAS  Article  PubMed  PubMed Central  Google Scholar 

71.
Team, R. C. R: A language and environment for statistical computing. R Found. Stat. Comput. Vienna, Austria. https://www.R-project.org (2017).

72.
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. arXiv Prepr. arXiv1406.5823 (2014).

73.
Jaeger, B. Package ‘r2glmm’. R Found. Stat. Comput. Vienna Avail. CRAN R-Project org/package=R2glmm Stat https://doi.org/10.1002/sim.3429 (2017).
Article  Google Scholar 

74.
Williams, G. C. Natural selection, the costs of reproduction, and a refinement of Lack’s principle. Am. Nat. 100, 687–690 (1966).
Article  Google Scholar 

75.
Clutton-Brock, T. H. Reproductive effort and terminal investment in iteroparous animals. Am. Nat. 123, 212–229 (1984).
Article  Google Scholar 

76.
Duffield, K. R., Bowers, E. K., Sakaluk, S. K. & Sadd, B. M. A dynamic threshold model for terminal investment. Behav. Ecol. Sociobiol. 71, 185 (2017).
Article  PubMed  PubMed Central  Google Scholar 

77.
Jones, K. M., Monaghan, P. & Nager, R. G. Male mate choice and female fecundity in zebra finches. Anim. Behav. 62, 1021–1026 (2001).
Article  Google Scholar 

78.
Griggio, M., Valera, F., Casas, A. & Pilastro, A. Males prefer ornamented females: A field experiment of male choice in the rock sparrow. Anim. Behav. 69, 1243–1250 (2005).
Article  Google Scholar 

79.
Naud, M.-J., Curtis, J. M. R., Woodall, L. C. & Gaspar, M. B. Mate choice, operational sex ratio, and social promiscuity in a wild population of the long-snouted seahorse Hippocampus guttulatus. Behav. Ecol. 20, 160–164 (2008).
Article  Google Scholar 

80.
Cutrera, A. P., Fanjul, M. S. & Zenuto, R. R. Females prefer good genes: MHC-associated mate choice in wild and captive tuco-tucos. Anim. Behav. 83, 847–856 (2012).
Article  Google Scholar 

81.
Mobley, K. B., Chakra, M. A. & Jones, A. G. No evidence for size-assortative mating in the wild despite mutual mate choice in sex-role-reversed pipefishes. Ecol. Evol. 4, 67–78 (2014).
Article  PubMed  Google Scholar 

82.
Tschinkel, W. R. & Willson, C. D. Inhibition of pupation due to crowding in some tenebrionid beetles. J. Exp. Zool. 176, 137–145 (1971).
CAS  Article  PubMed  Google Scholar 

83.
Morales-Ramos, J. A. & Rojas, M. G. Effect of larval density on food utilization efficiency of Tenebrio molitor (Coleoptera: Tenebrionidae). J. Econ. Entomol. 108, 2259–2267 (2015).
CAS  Article  PubMed  Google Scholar 

84.
Morales-Ramos, J. A., Rojas, M. G., Kay, S., Shapiro-Ilan, D. I. & Tedders, W. L. Impact of adult weight, density, and age on reproduction of Tenebrio molitor (Coleoptera: Tenebrionidae). J. Entomol. Sci. 47, 208–220 (2012).
Article  Google Scholar 

85.
Kraak, S. B. M. & Bakker, T. C. M. Mutual mate choice in sticklebacks: Attractive males choose big females, which lay big eggs. Anim. Behav. 56, 859–866 (1998).
CAS  Article  PubMed  Google Scholar 

86.
Sandvik, M., Rosenqvist, G. & Berglund, A. Male and female mate choice affects offspring quality in a sex–role–reversed pipefish. Proc. R. Soc. Lond. Ser. B Biol. Sci. 267, 2151–2155 (2000).
CAS  Article  Google Scholar 

87.
Drickamer, L. C., Gowaty, P. A. & Wagner, D. M. Free mutual mate preferences in house mice affect reproductive success and offspring performance. Anim. Behav. 65, 105–114 (2003).
Article  Google Scholar 

88.
Bertram, S. M. et al. Linking mating preferences to sexually selected traits and offspring viability: Good versus complementary genes hypotheses. Anim. Behav. 119, 75–86 (2016).
Article  Google Scholar 

89.
Bowers, E. K. et al. Sex-biased terminal investment in offspring induced by maternal immune challenge in the house wren (Troglodytes aedon). Proc. R. Soc. B Biol. Sci. 279, 2891–2898 (2012).
Article  Google Scholar 

90.
Poulin, R. & Maure, F. Host manipulation by parasites: A look back before moving forward. Trends Parasitol. 31, 563–570 (2015).
Article  PubMed  Google Scholar 

91.
August, C. J. The role of male and female pheromones in the mating behaviour of Tenebrio molitor. J. Insect Physiol. 17, 739–751 (1971).
Article  Google Scholar 

92.
Font, E. & Desfilis, E. Courtship, mating, and sex pheromones in the mealworm beetle (Tenebrio molitor). In Exploring Animal Behavior in Laboratory and Field (eds. Ploger, B. J. & Yasukawa, K.) 43–58 (Elsevier, New York, 2003).

93.
Obata, S. & Hidaka, T. Experimental analysis of mating behavior in Tenebrio molitor L. (Coleoptera: Tenebrionidae). Appl. Entomol. Zool. 17, 60–66 (1982).
Article  Google Scholar  More

1.
Otles, S., Despoudi, S., Bucatariu, C. & Kartal, C. Food waste management, valorization, and sustainability in the food industry. In Food Waste Recovery (ed. Galanakis, C. M.) 3–23 (Academic Press, London, 2015).
Google Scholar 
2.
Schieber, A., Stintzing, F. C. & Carle, R. By-products of plant food processing as a source of functional compounds—Recent developments. Trends Food Sci. Technol. 12, 401–413 (2001).
CAS  Article  Google Scholar 

3.
Gowe, C. Review on potential use of fruit and vegetables by-products as a valuable source of natural food additives. Food Sci. Qual. Manag. 45, 47–61 (2015).
Google Scholar 

4.
Chia, S. Y. et al. Effects of waste stream combinations from brewing industry on performance of Black soldier fly, Hermetia illucens (Diptera: Stratiomyidae). PeerJ. 6, e5885 (2018).
Article  CAS  Google Scholar 

5.
Newman, P. & Jennings, I. Cities as Sustainable Ecosystems: Principles and Practices (Island Press, Washington, D.C., 2008).
Google Scholar 

6.
Lynch, K. M., Steffen, E. J. & Arendt, E. K. Brewers’ spent grain: A review with an emphasis on food and health. J. Inst. Brew. 122, 553–568 (2016).
CAS  Article  Google Scholar 

7.
Bolwig, S., Mark, M. S., Happel, M. K. & Brekke, A. Beyond animal feed?: the valorisation of brewers’ spent grain. In From Waste to Value: Valorisation Pathways for Organic Waste Streams in Circular Bioeconomies (ed. Taylor & Francis) 107–126 (2019).

8.
Malakhova, D. V., Egorova, M. A., Prokudina, L. I., Netrusov, A. I. & Tsavkelova, E. A. The biotransformation of brewer’s spent grain into biogas by anaerobic microbial communities. World J. Microbiol. Biotechnol. 31, 2015–2023 (2015).
CAS  Article  Google Scholar 

9.
Čičková, H., Newton, G. L., Lacy, R. C. & Kozánek, M. The use of fly larvae for organic waste treatment. Waste Manag. 35, 68–80 (2015).
Article  CAS  Google Scholar 

10.
Van Huis, A. Potential of insects as food and feed in assuring food security. Ann. Rev. Entomol. 58, 563–583 (2013).
Article  CAS  Google Scholar 

11.
Oonincx, D. G. A. B., Van Broekhoven, S., Van Huis, A. & van Loon, J. J. A. Feed conversion, survival and development, and composition of four insect species on diets composed of food by-products. PLoS ONE 10, e0144601 (2015).
Article  Google Scholar 

12.
Wang, Y. S. & Shelomi, M. Review of black soldier fly (Hermetia illucens) as animal feed and human food. Foods. 6, 91 (2017).
Article  CAS  Google Scholar 

13.
Costa-Neto, E. M. Insects as human food: An overview. Amazon. Rev. Antropol. 5, 562–582 (2013).
Google Scholar 

14.
Diener, S. et al. Black soldier fly larvae for organic waste treatment–prospects and constraints. In Proceedings, WasteSafe 2011—2nd Int. Conf. on Solid Waste Management in the Developing Countries (eds. Alamgir, M. et al.) 52–59 (2011).

15.
Zhou, F., Tomberlin, J. K., Zheng, L., Yu, Z. & Zhang, J. Developmental and waste reduction plasticity of three black soldier fly strains (Diptera: Stratiomyidae) raised on different livestock manures. J. Med. Entomol. 50, 1224–1230 (2013).
Article  Google Scholar 

16.
Nguyen, T., Tomberlin, J. K. & Vanlaerhoven, S. Ability of black soldier fly (Diptera: Stratiomyidae) larvae to recycle food waste. Environ. Entomol. 44, 406–410 (2015).
CAS  Article  Google Scholar 

17.
Zheng, L., Li, Q., Zhang, J. & Yu, Z. Double the biodiesel yield: Rearing black soldier fly larvae, Hermetia illucens, on solid residual fraction of restaurant waste after grease extraction for biodiesel production. Renew. Energy. 41, 75–79 (2012).
CAS  Article  Google Scholar 

18.
Webster, C. D. et al. Bio-ag reutilization of distiller’s dried grains with solubles (DDGS) as a substrate for black soldier fly larvae, Hermetia illucens, along with poultry by-product meal and soybean meal, as total replacement of fish meal in diets for Nile tilapia, Oreochromis niloticus.. Aquacult. Nutr. 22, 976–988 (2016).
CAS  Article  Google Scholar 

19.
Lalander, C. et al. Faecal sludge management with the larvae of the black soldier fly (Hermetia illucens)—From a hygiene aspect. Sci. Total Environ. 458, 312–318 (2013).
ADS  Article  CAS  Google Scholar 

20.
Banks, I. J., Gibson, W. T. & Cameron, M. M. Growth rates of black soldier fly larvae fed on fresh human faeces and their implication for improving sanitation. Trop. Med. Int. Health. 19, 14–22 (2014).
Article  Google Scholar 

21.
Barroso, F. G. et al. The potential of various insect species for use as food for fish. Aquaculture 422, 193–201 (2014).
Article  Google Scholar 

22.
Henry, M., Gasco, L., Piccolo, G. & Fountoulaki, E. Review on the use of insects in the diet of farmed fish: Past and future. Anim. Feed Sci. Technol. 203, 1–22 (2015).
CAS  Article  Google Scholar 

23.
Surendra, K. C., Olivier, R., Tomberlin, J. K., Rajesh Jha, R. & Khanalet, S. K. Bioconversion of organic wastes into biodiesel and animal feed via insect farming. Renew. Energy. 98, 197–202 (2016).
CAS  Article  Google Scholar 

24.
Leong, S. Y., Kutty, S. R. M., Malakahmad, A. & Tan, C. K. Feasibility study of biodiesel production using lipids of Hermetia illucens larva fed with organic waste. Waste Manag. 47, 84–90 (2016).
CAS  Article  Google Scholar 

25.
Li, W. et al. Potential biodiesel and biogas production from corncob by anaerobic fermentation and black soldier fly. Bioresour Technol. 194, 276–282 (2015).
CAS  Article  Google Scholar 

26.
Cammack, J. A. & Tomberlin, J. K. The impact of diet protein and carbohydrate on select life-history traits of the black soldier fly Hermetia illucens (L.) (Diptera: Stratiomyidae). Insects. 8, 56 (2017).
Article  Google Scholar 

27.
Li, W. et al. Simultaneous utilization of glucose and xylose for lipid accumulation in black soldier fly. Biotechnol. Biofuels. 8, 1–6 (2015).
Article  CAS  Google Scholar 

28.
Soma, D. D. et al. Does mosquito mass-rearing produce an inferior mosquito?. Malar. J. 16, 357 (2017).
Article  CAS  Google Scholar 

29.
Sørensen, J., Addison, M. & Terblanche, J. Mass-rearing of insects for pest management: Challenges, synergies and advances from evolutionary physiology. Crop Prot. 38, 87–94 (2012).
Article  Google Scholar 

30.
Kuriwada, T., Kumano, N., Shiromoto, K. & Haraguchi, D. Effect of mass rearing on life history traits and inbreeding depression in the sweetpotato weevil (Coleoptera: Brentidae). J. econ. entomol. 103, 1144–1148 (2010).
CAS  Article  Google Scholar 

31.
Zheng, M. L., Zhang, D. J., Damiens, D. D., Yamada, H. & Gilles, J. R. Standard operating procedures for standardized mass rearing of the dengue and chikungunya vectors Aedes aegypti and Aedes albopictus (Diptera: Culicidae)—I—egg quantification. Parasit. Vectors. 8, 42 (2015).
Article  Google Scholar 

32.
Ghimire, M. N. & Phillips, T. W. Mass rearing of Habrobracon hebetor Say (Hymenoptera: Braconidae) on larvae of the Indian meal moth, Plodia interpunctella (Lepidoptera: Pyralidae): Effects of host density, parasitoid density, and rearing containers. J. Stored Prod. Res. 46, 214–220 (2010).
Article  Google Scholar 

33.
Chia, S. Y. et al. Threshold temperatures and thermal requirements of black soldier fly Hermetia illucens: Implications for mass production. PLoS ONE 13, 1–26 (2018).
Article  CAS  Google Scholar 

34.
McGill, B. J. Matters of scale. Science 328, 575–576 (2010).
ADS  CAS  Article  Google Scholar 

35
Jucker, C., Erba, D., Leonardi, M. G., Lupi, D. & Savoldelli, S. Assessment of vegetable and fruit substrates as potential rearing media for Hermetia illucens (Diptera: Stratiomyidae) larvae. Environ. Entomol. 46, 1415–1423 (2017).
CAS  Article  Google Scholar 

36.
Jucker, C., Leonardi, M. G., Rigamonti, I., Lupi, D. & Savoldelli, S. Brewery’s waste streams as a valuable substrate for Black Soldier Fly Hermetia illucens (Diptera: Stratiomyidae). J. Entomol. Acarol. Res. 51, 8876 (2020).
Article  Google Scholar 

37.
Nguyen, T. T. X., Tomberlin, J. K. & Vanlaerhoven, S. Influence of resources on Hermetia illucens (Diptera: Stratiomyidae) larval development. J. Med. Entomol. 50, 898–906 (2013).
Article  Google Scholar 

38.
Barbi, S. et al. Valorization of seasonal agri-food leftovers through insects. Sci. Total Environ. 709, 136209 (2020).
ADS  CAS  Article  Google Scholar 

39.
Bava, L. et al. Rearing of Hermetia illucens on different organic by-products: Influence on growth, waste reduction, and environmental impact. Animals 29, 289 (2019).
Article  Google Scholar 

40.
Meneguz, M. et al. Effect of rearing substrate on growth performance, waste reduction efficiency and chemical composition of black soldier fly (Hermetia illucens) larvae. J. Sci. Food Agric. 98, 5776–5784 (2018).
CAS  Article  Google Scholar 

41.
Slone, D. & Gruner, S. V. Thermoregulation in larval aggregations of carrion-feeding blow flies (Diptera: Calliphoridae). J. Med. Entomol. 44, 516–523 (2007).
CAS  Article  Google Scholar 

42.
Gere, G. Investigations into the laws governing the growth of Hyphantria cunea drury caterpillars. Acta Biol. Hung. 7, 43–72 (1956).
Google Scholar 

43.
Long, D. B. Effects of population density on larvae of Lepidoptera. Trans. R. Entomol. Soc. Lond. 104, 543–585 (1953).
Article  Google Scholar 

44.
Parra Paz, A. S., Carrejo, N. S. & Gómez Rodríguez, C. H. Effects of larval density and feeding rates on the bioconversion of vegetable waste using black soldier fly larvae Hermetia illucens (L.), (Diptera: Stratiomyidae). Waste Biomass Valor. 6, 1059–1065 (2015).
Article  Google Scholar 

45.
Bonelli, M. et al. Structural and functional characterization of Hermetia illucens larval midgut. Front. Physiol. 10, 204 (2019).
Article  Google Scholar 

46.
Tschirner, M. & Simon, A. Influence of different growing substrates and processing on the nutrient composition of black soldier fly larvae destined for animal feed. J. Insect Food Feed. 1, 249–259 (2015).
Article  Google Scholar 

47.
Gobbi, P., Martinez Sanchez, A. & Rojo, S. The effects of larval diet on adult life history traits of the black soldier fly, Hermetia illucens [Diptera: Stratiomyidae]. Eur. J. Entomol. 110, 461–468. https://doi.org/10.14411/eje.2013.061 (2013).
Article  Google Scholar 

48.
Kim, E., Park, J., Lee, S. & Kim, Y. Identification and physiological characters of intestinal bacteria of the black soldier fly, Hermetia illucens. Korean J. Appl. Entomol. 53, 15–26 (2014).
Article  Google Scholar 

49.
Spranghers, T. et al. Nutritional composition of black soldier fly (Hermetia illucens) prepupae reared on different organic waste substrates. J. Sci. Food Agric. 97, 2594–2600 (2017).
CAS  Article  Google Scholar 

50.
Inagaki, S. & Yamashita, O. Metabolic shift from lipogenesis to glycogenesis in the last instar larval fat body of the silkworm, Bombyx mori. Insect Biochem. 16, 327–331 (1986).
CAS  Article  Google Scholar 

51.
Tomberlin, J. K., Sheppard, D. C. & Joyce, J. A. Selected life-history traits of black soldier flies (Diptera: Stratiomyidae) reared on three artificial diets. Ann. Entomol. Soc. Am. 95, 379–386 (2002).
Article  Google Scholar 

52.
Bosch, G. et al. Standardisation of quantitative resource conversion studies with black soldier fly larvae. J. Insects Food Feed. 6, 95–109 (2020).
Article  Google Scholar 

53.
Ståhls, G. et al. The puzzling mitochondrial phylogeography of the black soldier fly (Hermetia illucens), the commercially most important insect protein species. BMC Evol. Biol. 20, 60 (2020).
Article  Google Scholar 

54.
Zhan, S. et al. Genomic landscape and genetic manipulation of the black soldier fly Hermetia illucens, a natural waste recycler. Cell. Res. 30, 50–60 (2019).
Article  Google Scholar 

55.
Barragan-Fonseca, K. B., Dicke, M. & van Loon, J. J. A. Nutritional value of the black soldier fly (Hermetia illucens L.) and its suitability as animal feed—A review. J. Insects Food Feed 1, 1–16 (2017).
Google Scholar 

56.
Booth, D. C. & Sheppard, C. Oviposition of the black soldier fly, Hermetia Illucens (Diptera: Stratiomyidae): Eggs, masses, timing, and site characteristics. Environ. Entomol. 13, 421–423 (1984).
Article  Google Scholar 

57.
Hogsette, J. A. New diets for production of house flies and stable flies (Diptera: Muscidae) in the laboratory. J. Econ. Entomol. 85, 2291–2294 (1992).
CAS  Article  Google Scholar 

58.
Loveridge, J. P. Age and the changes in water and fat content of adult laboratory- reared Locusta migratoria migratorioides. Rhod. J. Agric. Res. 11, 131–143 (1973).
Google Scholar 

59.
Sokal, R. R. & Rohlf, F. J. Biometry: The Principles and Practice of Statistics in Biological Research 887 (W.H. Freeman & Company, New York, 1995).
Google Scholar  More

Species distribution modelling
A total of 1548 occurrence locations were obtained for B. fagacearum from the United States Forest Service (provided to us by Erin Bullas-Appleton of the Canadian Food Inspection Agency in July 2018) and the Global Biodiversity Information Facility17. These records were filtered at a 300 arcsecond (approximately 10-km) resolution to remove duplicates and reduce spatial clustering, leaving 1401 unique location records (Fig. 1a). Occurrence locations for two key insect vectors of oak wilt, C. truncatus (Fig. 2a) and C. sayi (Fig. 3a), were obtained from GBIF, publications10,18, and from specimens in the following collections: Atlantic Forestry Centre, Fredericton, NB, Canada; Canadian National Collection of Insects, Arachnids, and Nematodes, Agriculture and Agri-Food Canada Research Centre, Ottawa, ON, Canada; Ontario Forest Research Institute, Sault Ste. Marie, ON, Canada; Gareth S. Powell Collection, Nephi, UT, USA; Reginald Webster Collection, Charters Settlement, New Brunswick, Canada; and the Florida State Collection of Arthropods, Gainesville, FL, USA . After filtering at a 10-km resolution, there were 82 and 58 unique occurrence records for C. truncatus and C. sayi respectively.
Figure 1

Occurrence data (a) used for generating climate suitability models for Bretziella fagacearum. Maps with colour gradients indicate Maxent-derived climate suitability for B. fagacearum for the: 1981–2010 period (b); 2011–2040 period (c); and 2041–2070 period (d). Stippling delineates the ANUCLIM-derived climate envelope for B. fagacearum in each time period. Hatching delineates the current distribution of Quercus in Canada. Climate projections are based on a composite of four climate models and the RCP 4.5 emissions scenario (see text for further details). Maps were generated using ARCGIS v.9.3 (ESRI, Redlands, CA, USA; https://www.esri.com/arcgis/about-arcgis).

Full size image

Figure 2

Occurrence data (a) used for generating climate suitability models for Colopterus truncatus. Maps with colour gradients indicate Maxent-derived climate suitability for C. truncatus for the: 1981–2010 period (b); 2011–2040 period (c); and 2041–2070 period (d). Stippling delineates the ANUCLIM-derived climate envelope for C. truncatus in each time period. Hatching delineates the current distribution of Quercus in Canada. Climate projections are based on a composite of four climate models and the RCP 4.5 emissions scenario (see text for further details). Maps were generated using ARCGIS v.9.3 (ESRI, Redlands, CA, USA; https://www.esri.com/arcgis/about-arcgis).

Full size image

Figure 3

Occurrence data (a) used for generating climate suitability models for Carpophilus sayi. Maps with colour gradients indicate Maxent-derived climate suitability for C. sayi for the: 1981–2010 period (b); 2011–2040 period (c); and 2041–2070 period (d). Stippling delineates the ANUCLIM-derived climate envelope for C. sayi in each time period. Hatching delineates the current distribution of Quercus in Canada. Climate projections are based on a composite of four climate models and the RCP 4.5 emissions scenario (see text for further details). Maps were generated using ARCGIS v.9.3 (ESRI, Redlands, CA, USA; https://www.esri.com/arcgis/about-arcgis).

Full size image

Climate estimates were obtained at each occurrence location by interrogating North American climate models (described in McKenney et al.19) of the 1981–2010 normal period for the following four variables: (1) fall (i.e., September–December) precipitation (FALLPCP); (2) average spring (i.e., March–June) temperature (SPRINGTMP); (3) annual climate moisture index (CMI; a climate-based moisture balance variable; see Hogg20 for details); and (4) Average Minimum Temperature of the Coldest Month (MINTCM). These climate variables were selected based on their reported influences on B. fagacearum (FALLPCP and CMI21,22) C. sayi and C. truncatus (SPRINGTMP10), and insect distributions in general (MINTCM23). Further, none of the selected variables were highly correlated (i.e., r  60 years) for each province in our study area. Focusing on current/near-term oak timber stocks (since we are not estimating B. fagacearum spread), we multiplied merchantable volume over 40 years old—roughly the age at which oak becomes harvestable in Ontario42—by average provincial stumpage values.
Estimating stumpage values was somewhat challenging due to inter-provincial variation in stumpage systems and reporting of stumpage fees. For the province of Québec, we obtained oak-specific stumpage fees for 191 harvest zones for the period April 1, 2019 to March 31, 2020 (Bureau de mise en marché43). Since stumpage fees in Québec vary by wood quality class (i.e., A, B, and C), we further obtained information on the proportion of wood harvested in each class over the same period (unpublished dataset, Bureau de mise en marché des Bois). We then calculated the average stumpage fee for the province by averaging across harvest zones and quality classes, while weighting by the proportion of wood in each quality class. For Ontario, we obtained stumpage fees for two quality classes of hardwoods (i.e., Class 1 and Class 2) and four oak-related product types (Veneer, Sawlogs, Composite, and Firewood) for January 1 to December 31, 2019 (Ontario Ministry of Natural Resources and Forestry44). Given that oak is typically considered a higher value hardwood, and in lieu of information on how oak is partitioned across product types in Ontario, we calculated oak stumpage fees as an average across product types for the Class 1 hardwood category. Note that the stumpage rates employed here include the Renewal and Forest Futures fees that are part of the Ontario stumpage system. Finally, stumpage values for the province of Nova Scotia were obtained for a single hardwood quality class for the period April 1, 2017 to March 31, 2018 (Province of Nova Scotia45). As in Ontario, stumpage fees were averaged across product types. The stumpage values for Nova Scotia were applied to the relatively small amount of oak in the neighbouring Maritime Provinces of New Brunswick and Prince Edward Island (PEI).
Alternatively, gross domestic product (GDP) can provide an estimate of the total economic activity associated with a given industry. Annual GDP estimates for broad categories (e.g., forestry and logging industry, and wood product manufacturing industry) are available for each province at Natural Resources Canada’s forestry statistics website (https://cfs.nrcan.gc.ca/statsprofile/overview/ca). In order to estimate GDP specifically for oak-related timber products, we first multiplied these provincial broad category GDP values by the proportion of the total provincial harvest that was composed of hardwoods (multipliers obtained from published provincial data sources as detailed in the Results section below). This value was then further refined by multiplying by the proportion of hardwoods in the province that was composed of oak species. These estimates were obtained from forest attribute grids13, by summing merchantable volume of (1) oak and (2) all broadleaf species within the industrial forestry limits of each province. Spatial summaries were carried out using the raster and rgdal packages in r. Though admittedly coarse, we felt this approach was the best available given the dearth of readily available economic data for individual tree species/genera; similar approaches have been used previously to estimate economic impacts of invasive species46,47.
These two approaches (i.e., stumpage-based and GDP-based) provide different perspectives on oak-related timber values at risk. The stumpage approach attaches a basic price to standing timber resources, but does not consider downstream economic activities associated with harvest, such as wages, equipment purchases, and capital expenditures. This approach implicitly assumes that substitution possibilities (e.g., other tree species) can fully replace oak-related contributions to the economy with minimal adjustment costs and, as such, is a conservative estimate of potential timber value losses. Alternatively, the GDP approach attempts to include all downstream economic contributions and assumes little or no opportunity for substitution, such that oak timber losses would be accompanied by a proportional reduction in economic activity. We present both estimates here to provide policy-makers with a range of possible impacts. The value of costs through time is generally arrived at using economic discounting; however, here we have no estimates of the timeline associated with oak wilt spread and hence have chosen to report gross, undiscounted values. See Aukema et al.48 for further discussion. More

1.
BirdLife International. Pygoscelis antarcticus. The IUCN Red List of Threatened Species 2018:e.T22697761A132601557. https://doi.org/10.2305/IUCN.UK.2018-2.RLTS.T22697761A132601557.en (2018).
2.
Sander, M., Balbao, T. C., Polito, M. J., Costa, E. S. & Carneiro, A. P. B. Recent decrease in Chinstrap penguin (Pygoscelis antarctica) populations at two of Admiralty Bay’s islets on King George Island, South Shetland Islands, Antarctica. Polar Biol. 30, 659–661 (2007).
Article  Google Scholar 

3.
Naveen, R., Lynch, H. J., Forrest, S., Mueller, T. & Polito, M. First direct, site-wide penguin survey at Deception Island, Antarctica, suggests significant declines in breeding Chinstrap penguins. Polar Biol. 35(12), 1879–1888 (2012).
Google Scholar 

4.
Lynch, H. J. et al. In stark contrast to widespread declines along the Scotia Arc, a survey of the South Sandwich Islands finds a robust seabird community. Polar Biol. 39, 1–11 (2016).
ADS  Article  Google Scholar 

5.
Dunn, M. et al. Population size and decadal trends of three penguin species nesting at Signy Island South Orkney Islands. PLoS ONE 11, e0164025 (2016).
Article  Google Scholar 

6.
Strycker, N. et al. Fifty-year change in penguin abundance on Elephant Island, South Shetland Islands, Antarctica: Results of the 2019–20 census (2020) (in review).

7.
Croxall, J. P. & Furse, J. R. Food of Chinstrap penguins (Pygoscelis antarctica) and Macaroni penguins (Eudyptes chrysolophus) at Elephant Island Group, South Shetland Islands. Ibis 122, 237–245 (1980).
Article  Google Scholar 

8.
Trivelpiece, W. Z. et al. Variability in krill biomass links harvesting and climate warming to penguin population changes in Antarctica. PNAS 108(18), 7625–7628 (2011).
ADS  CAS  Article  Google Scholar 

9.
Lynch, H. J., Naveen, R., Trathan, P. N. & Fagan, W. F. Spatially integrated assessment reveals widespread changes in penguin populations on the Antarctic Peninsula. Ecology 93(6), 1367–1377 (2012).
Article  Google Scholar 

10.
Casanovas, P., Naveen, R., Forrest, S., Poncet, J. & Lynch, H. J. A comprehensive coastal seabird survey maps out the front lines of ecological change on the Western Antarctic Peninsula. Polar Biol. 38, 927–940 (2015).
Article  Google Scholar 

11.
Croxall, J. P., Trathan, P. & Murphy, E. Environmental change and Antarctic seabird populations. Science 297, 1510–1514 (2002).
ADS  CAS  Article  Google Scholar 

12.
CCAMLR. Standard methods for monitoring parameters of predator species. CCAMLR Ecosystem Monitoring Programme, Hobart (2004).

13.
Hinke, J., Salwicka, K., Trivelpiece, S., Watters, G. & Trivelpiece, W. Divergent responses of Pygoscelis penguins reveal common environmental driver. Oecologia 153, 845–855 (2007).
ADS  Article  Google Scholar 

14.
Lynch, H. J., Fagan, W. F. & Naveen, R. Population trends and reproductive success at a frequently visited penguin colony on the western Antarctic Peninsula. Polar Biol. 33, 493–503 (2010).
Article  Google Scholar 

15.
Fraser, W. R., Trivelpiece, W. Z., Ainley, D. G. & Trivelpiece, S. G. Increases in Antarctic penguin populations: Reduced competition with whales or a loss of sea ice due to environmental warming?. Polar Biol. 11(8), 525–531 (1992).
Article  Google Scholar 

16.
Hinke, J. et al. Identifying risk: concurrent overlap of the Antarctic krill fishery with krill-dependent predators in the Scotia Sea. PLoS ONE 12, e0170132 (2017).
Article  Google Scholar 

17.
Hill, S. L. et al. Reference points for predators will progress ecosystem-based management of fisheries. Fish Fish. 21(2), 368–378 (2020).
Article  Google Scholar 

18.
Juáres, M. A. et al. Adélie penguin population changes at Stranger Point: 19 years of monitoring. Antarct. Sci. 27(5), 455–461 (2015).
ADS  Article  Google Scholar 

19.
Lynch, M., Youngflesh, C., Agha, N. M., Ottinger, M. A. & Lynch, H. Tourism and stress hormone measures in Gentoo Penguins on the Antarctic Peninsula. Polar Biol. 42, 1299–1306 (2019).
Article  Google Scholar 

20.
Croxall, J. P. & Kirkwood, E. D. The Distribution of Penguins on the Antarctic Peninsula and Islands of the Scotia Sea (British Antarctic Survey, Cambridge, 1979).
Google Scholar 

21.
Woehler, E. J. The Distribution and Abundance of Antarctic and Subantarctic Penguins (Scientific Committee on Antarctic Research, Cambridge, 1993).
Google Scholar 

22.
Conroy, J. W. H., White, M. G., Furse, J. R. & Bruce, G. Observations on the breeding biology of the Chinstrap penguin, Pygoscelis Antarctica, at Elephant Island, South Shetland Islands. Br. Antarct. Surv. Bull. 40, 23–32 (1975).
Google Scholar 

23.
Lowther, A. D. Antarctic marine mammals. Encyclopedia of Marine Mammals 3rd edn, 27–32 (Academic Press, London, 2018).
Google Scholar 

24.
Hinke, J. et al. Spatial and isotopic niche partitioning during winter in Chinstrap and Adélie penguins from the South Shetland Islands. Ecosphere 6, 125 (2015).
Article  Google Scholar 

25.
Dimitrijević, D. et al. Isotopic niches of sympatric Gentoo and Chinstrap penguins: evidence of competition for Antarctic krill?. Polar Biol. 41, 1655–1669 (2018).
Article  Google Scholar 

26.
Trathan, P. N., Croxall, J. P. & Murphy, E. J. Dynamics of Antarctic penguin populations in relation to inter-annual variability in sea ice distribution. Polar Biol. 16, 321–330 (1996).
Article  Google Scholar 

27.
He, H. et al. Aerial photography based census of Adélie penguin and its application in CH4 and N20 budget estimation in Victoria Land, Antarctic. Sci. Rep. 7, 12942 (2017).
ADS  Article  Google Scholar 

28.
Bromwich, D. H. et al. Central West Antarctica among the most rapidly warming regions on Earth. Nat. Geosci. 6, 139–145 (2013).
ADS  CAS  Article  Google Scholar 

29.
World Meteorological Organization. 2020.

30.
Flores, H. Impact of climate change on Antarctic krill. Mar. Ecol. Prog. Ser. 458, 1–19 (2012).
ADS  Article  Google Scholar 

31.
Reid, K., Sims, M., White, R. W. & Gillon, K. W. Spatial distribution of predator/prey interactions in the Scotia Sea: Implications for measuring predator/fisheries overlap. Deep Sea Res. Part II 51, 1383–1396 (2004).
ADS  Article  Google Scholar 

32.
Watters, G. M., Hinke, J. & Reiss, C. S. Long-term observations from Antarctica demonstrate that mismatched scales of fisheries management and predator–prey interaction lead to erroneous conclusions about precaution. Sci. Rep. 10, 2314 (2020).
ADS  CAS  Article  Google Scholar 

33.
Polito, M. J. et al. Stable isotope analyses of feather amino acids identify penguin migration strategies at ocean basin scales. Biol. Lett. 13, 20170241 (2017).
Article  Google Scholar 

34.
Hinke, J., Santos, M., Malgorzata Korczak-Abshire, G. M. & Watters, G. Individual variation in migratory movements of Chinstrap penguins leads to widespread occupancy of ice-free winter habitats over the continental shelf and deep ocean basins of the Southern Ocean. PLoS ONE 14(12), e0226207 (2019).
CAS  Article  Google Scholar 

35.
Hurlbert, A. H. & Liang, Z. Spatiotemporal variation in avian migration phenology: citizen science reveals effects of climate change. PLoS ONE 7, e31662 (2012).
ADS  CAS  Article  Google Scholar 

36.
Cohen, J. M., Lajeunesse, J. M. & Rohr, J. R. A global synthesis of animal phenological responses to climate change. Nat. Clim. Change 8, 224–228 (2018).
ADS  Article  Google Scholar 

37.
Samplonius, J. M. et al. Strengthening the evidence base for temperature-mediated phenological asynchrony and its impacts. EcoEvoRxiv Preprints. https://doi.org/10.32942/osf.io/jmy67. Published online ahead of print May 5, 2020.

38.
Robinson, R. A. et al. Travelling through a warming world: climate change and migratory species. Endanger. Species Res. 7, 87–99 (2009).
ADS  Article  Google Scholar 

39.
Atkinson, A. et al. Krill (Euphausia superba) distribution contracts southward during rapid regional warming. Nat. Clim. Change 9, 142–147 (2019).
ADS  Article  Google Scholar 

40.
Freer, J. et al. Limited genetic differentiation among Chinstrap penguin (Pygoscelis antarctica) colonies in the Scotia Arc and Western Antarctic Peninsula. Polar Biol. 38(9), 1493–1502 (2015).
Article  Google Scholar 

41.
Clucas, G. V. et al. Comparative population genomics reveals key barriers to dispersal in Southern Ocean penguins. Mol. Ecol. 27, 4680–4697 (2018).
CAS  Article  Google Scholar 

42.
Humphries, G. et al. Mapping Application for Penguin Populations and Projected Dynamics (MAPPPD): data and tools for dynamic management and decision support. Polar Rec. 53, 1–7 (2017).
Article  Google Scholar 

43.
Lynch, H. J. & LaRue, M. A. First global census of the Adélie penguin. The Auk Ornithol. Adv. 131, 457–466 (2014).
Google Scholar 

44.
Che-Castaldo, C. et al. Pan-Antarctic analysis aggregating spatial estimates of Adélie penguin abundance reveals robust dynamics despite stochastic noise. Nat. Commun. 8, 832 (2017).
ADS  Article  Google Scholar  More

Microorganisms drive biogeochemical cycling in the earth system1 (Fig. 1). Photoautotrophic microorganisms are responsible for about half of CO2 fixation and O2 production on earth, and heterotrophic microorganisms are responsible for much of the return reaction: the oxidation of organic matter back into CO2. The temporal and spatial separation of photoautotrophy and heterotrophy in the global environment drives the biological sequestration of carbon, the reduction of atmospheric CO2, and the maintenance of elevated atmospheric and oceanic O22,3,4,5. Chemoautotrophic microorganisms also fix CO2 and, together with anaerobic heterotrophic metabolisms, carry out diverse chemical transformations including the fluxes of nitrogen to and from biologically available states and the formation of the potent greenhouse gas nitrous oxide (N2O)6,7. Since these transformations respond to, and feedback on, changes in climate (Fig. 1), estimating microbial activity accurately at global scales is important for climate science.
Fig. 1: Key microbially driven redox transformations that mediate the atmospheric fluxes of climatically relevant gases.

Radiatively active gases are notated with red type. The processes in black type are represented in some way (though not necessarily with electron balancing) in both the marine and terrestrial biospheres in earth system models within the Coupled Model Intercomparison Project (land: NCAR Community Earth System Model103; ocean: GFDL COBALTv2104), which are used for projections of climate change in reports by the Intergovernmental Panel on Climate Change. Processes in green type are represented in only the terrestrial model. Current models do not yet include other relevant reactions, some of which are represented in gray type, such as anaerobic ammonia oxidation (anammox), the marine production and consumption of methane, the redox cycling of iron, manganese, and other metals, and the methane-relevant redox chemistry of phosphorus105. COBALTv2 does account for sulfate reduction in marine sediments, but sulfate is not represented. Image courtesy of NASA.

Full size image

However, understanding and projecting the impacts of microbial processes are limited in part due to oversimplified representation in earth system models. For example, in marine biogeochemical models, much attention is given to the complex impacts of phytoplankton—the photoautotrophic microorganisms responsible for primary production—and their small zooplankton predators8,9,10. The bacterial and archaeal activities responsible for other critical aspects of biogeochemical cycling in the land and ocean—remineralization, denitrification, nitrogen fixation, methanogenesis, etc.—are often crudely parameterized10. Such models have limited prognostic capability. For example, models typically prescribe the ecological niche of a given metabolism with imposed, empirically determined parameters that are site- or organism-specific. These parameterizations may or may not apply to other environments, including past and future ecosystems.
These simplistic approaches have been largely necessary due to the difficulties of characterizing the taxonomy and metabolic capabilities of natural microbial communities. However, the rapid expansion of genetic sequencing capabilities has enabled a clearer view of microbial biogeography and activity in the environment. In consequence, computational biogeochemistry is opening up the black box of remineralization and other microbially mediated processes in marine and terrestrial environments11,12,13,14,15,16,17,18.
As we expand models to include the full metabolic potential of microorganisms, how can we organize and reduce the complexity of the descriptions of metabolic diversity? Non-photosynthetic organisms oxidize chemical species for energy, and thus their respiration is biogeochemically significant19. Here, we explain how the key reduction-oxidation (redox) reactions that supply energy for metabolisms can provide an additional organizing principle for explicit descriptions of microbial populations in ecosystem models. This redox basis can be exploited to quantitatively resolve chemical transformations in terms of assimilatory and respiratory fluxes. While not yet incorporated into earth system models, this view has been advocated for such applications20, and has been embraced and employed in the field of environmental biotechnology, such as in the interpretation and modeling of wastewater bioreactors21. Just as models of ocean and atmospheric circulation are constrained by conservation of energy and potential vorticity, complementing mass balance with powerful redox and energetic constraints enables self-consistent descriptions of diverse microbial metabolisms.
This approach aims to advance ecological modeling beyond species-specific descriptions to those that matter for biogeochemical function, in line with trait-based modeling approaches9. In analogy to the use of redox chemistry, trait-based functional type models of phytoplankton have used cell size as an organizing principle for understanding phytoplankton biogeography, biodiversity, and impact on biogeochemistry9,22,23. These types of theoretical constraints allow for the inclusion of more functional types without introducing as many degrees of freedom as would be necessary if each were empirically described. The guiding perspective is that organizing complex biological behavior by its underlying chemical and physical constraints gives more universally applicable descriptions of large-scale biogeochemistry.
When incorporating a redox-balanced approach into ecosystem models, microbial function emerges from underlying chemistry as a consequence of interactions between populations modeled as metabolic functional types and their environment. Resulting theoretically grounded ecosystem models independently simulate microbial growth, respiration, and abundances in ways that we can compare with observations such as sequencing datasets. Thus, sequencing datasets are used as critical tests for the models, as external constraints rather than as input to the models, allowing for an iterative relationship between theory, observations, and models.
In contrast with empirically informed models, this approach involves constructing a model of microbial activity theoretically, and then comparing the results with the observations in order to gain an understanding of the system. The goal is to understand why biology functions as it does, in addition to anticipating global impacts. From a first-principles biogeochemical perspective with respect to physical and chemical forcing, genes are an intermediate step between forcing and function, with the detailed complexity of biological reality following the underlying chemical and physical constraints (analogous to the form follows function principle of architect Louis Sullivan). This does not equate to thinking that biology (or genetic information) does not matter or can be replaced entirely in models by physics and chemistry. Rather, we want to fundamentally understand biological activity as an integrated part of an ecosystem, and physics and chemistry become tools for doing so.
Here, we outline the basis for using redox chemistry as an organizing principle and its translation into quantitative descriptions of microbial activity that are simple enough for global earth system models. We then discuss the benefits of this approach in the context of their implications for improved understanding and projections of global change impacts. Finally, we discuss limitations and possible future developments.
Predicting microbial activity
From one perspective, microbial communities are characterized by interactions at the micro-scale: gene expression, enzymatic capabilities, metabolites, species-specific interdependencies, etc., as well as the physical and chemical environment surrounding small cells24,25,26,27,28. The information from sequencing in particular has allowed for a huge expansion of insight into the detailed in situ activity of uncultivated species. When investigating global-scale impacts, how do we decide which of these details may be bypassed for simplicity? Or, if this simplification is impossible, must we incrementally construct a microbial ecosystem model that incorporates all known micro-scale detail?
Another way forward arises from a macro-scale perspective, which examines how ecosystem function relates to the chemical potential utilized by organisms for energy20,29,30. For example, it is well known that microbial communities in sediments and anoxic zones organize according to the redox tower – the ranking of half-reactions by electrochemical potential14,31,32. Furthermore, respiration by living organisms increases the entropy of the environment by dissipating concentrated sources of chemical energy in accordance with the Second Law of Thermodynamics30,33,34.
This perspective suggests that chemical potential can be used to predict the activity of microbial communities and their biogeochemical impact. However, given the notorious complexity of microbial cells and systems, which is many steps away from governing chemical or physical equations, how can we be sure that this activity is indeed predictable? Frentz et al.35 demonstrated that external conditions cause the seemingly random fluctuations observed in microbial growth, rather than stochastic variation in gene expression. This provides direct evidence of deterministic behavior, and so the authors conclude that microbial systems can in principle be determined by macroscopic laws.
How is this determinism manifested? If microbial communities can respond relatively quickly to changes to their local environment, they may predictably optimize the exploitation of locally available resources. In the ocean, dispersal in microbes is thought to be a highly efficient process such that microbial communities can in effect draw from an extensive seed bank36,37, as captured in the phrase “everything is everywhere, the environment selects”38. Furthermore, recent evidence also shows that gene acquisitions and deletions happen quickly enough to allow for horizontal gene transfer to dominate bacterial adaptation39,40,41,42,43, implying that evolution can occur within few generations and thus on timescales similar to ecological interactions. Perhaps consequentially, similar geochemical environments have been demonstrated to have high-microbial functional redundancy despite different taxonomic compositions17,44. This may be interpreted with the hypothesis that physics and chemistry selects for metabolic traits, and that these traits can be housed in different organisms with taxonomic composition shaped by micro-scale or biotic interactions17,44,45.
The prediction of microbial activity from environmental chemical potential has a long history in microbiology20,21,46,47,48,49,50,51,52, and is conceptually similar to other redox-balanced approaches to understanding microbial activity in sediments, soils, subsurfaces, and aquatic systems13,31,53,54,55,56,57,58. Illustrating the power of these approaches, anticipating metabolism from chemical potential resulted in a prediction that anaerobic ammonia oxidation (anammox) should exist decades before it was observed59,60. Quantitatively understanding microbially mediated rates of conversion of substrates has practical implications for wastewater treatment, and thus the field of biotechnology has established methodologies for an approach in textbook form21. Flux balance analysis (FBA) models can be considered as much more highly detailed analogs of this approach that resolve the mass and electron balances among a multitude of chemical reactions within a cell61,62.
Redox-balanced metabolic functional types
We can resolve microbial activity in global ecosystem models using the underlying redox chemistry of diverse metabolisms as a constraint. One specific way forward is to model distinct metabolisms as populations of metabolic functional types. This systematically quantifies relative rates of substrate consumption, biomass synthesis, and excretions of transformed products associated with each metabolism. Coupled with estimates of substrate uptake, this replaces implicit parameterizations of processes such as organic matter consumption, oxygen depletion, and denitrification with electron-balanced respiratory fluxes of dynamic microbial populations. Box 1 provides a detailed description of this methodology for multi-dimensional models.
A particular set of redox reactions may distinguish a functional type, such as the oxidation of organic matter using oxygen (aerobic heterotrophy), or the oxidation of ammonia or nitrite using oxygen (chemoautotrophic nitrification) as exemplified in Table 1. For each metabolism, an electron-balanced description consists of multiple half-reactions: biomass synthesis, oxidation of an electron donor, and reduction of an electron acceptor21,48. The ratio of anabolism and catabolism can then be represented by the fraction f of electrons fueling cell synthesis vs. respiration for energy, following ref. 21. This provides a yield y (moles biomass synthesized per mole substrate utilized) of each required substrate that reflects two inputs: electron fraction f and the coefficients of the half-reactions (Fig. 2). The interlinked yields reflect the energy supplied by the redox reaction, the energy required for synthesis and other cellular demands, and the inefficiencies of energy conversion. Either f or y for any one of the substrates may be estimated theoretically with Gibbs free energies of reaction21 or with a combination of theoretical and empirical strategies63.
Table 1 Simplified equations describing two exemplary metabolic functional types.
Full size table

Fig. 2: Schematic of a single cell represented as a metabolic functional type carrying out the aerobic oxidation of ammonia.

The redox balance informs the elemental ratios of substrates utilized, biomass synthesized, and waste products excreted (Table 1).

Full size image

The result is a stoichiometric budget of the metabolism of the whole organism (Table 1). These descriptions quantify the elemental ratios of utilized substrates, biomass, and the excretion of waste products. For example, the descriptions account for the CO2 produced by heterotrophic metabolisms as well as the CO2 fixed by chemoautotrophic metabolisms (Table 1, Fig. 2, and Supplementary Fig. 2), linking microbial activity directly to global carbon cycling.
To estimate the growth rate of each functional type, the yields from the metabolic budgets are combined with the uptake rates of the required substrates (Box 1). Limiting uptake rates may rely on empirically derived uptake kinetic parameters, or they can be estimated theoretically from diffusive supply, cell size, membrane physiology, and other physical constraints64,65,66. If theoretical models of uptake are used, the physical constraints on substrate acquisition and the redox chemical constraints on energy acquisition can provide an entirely theoretical estimate of the growth of each metabolic functional type.
One strategy is to represent the populations carrying out each of these discrete metabolisms as one functional type population, which aggregates the diverse community of many species that are fueled by the same (or a similar) redox reaction (Fig. 3). Such aggregation has been deemed a useful strategy for representing the biogeochemical impacts of microbial communities for certain research questions67,68. However, for other questions this wipes out critical diversity among the aggregated populations. For example, diverse aerobic heterotrophic populations consume organic matter over a wide range of rates, and these rates dictate the amount of biologically sequestered carbon in the ocean. Redox chemistry and physical limitations alone may not inform the heterogeneity among similar metabolisms. One additional constraint is the limited capacity of the cell and thus its allocation of proteome towards different functions69. While the electrons supplied to the cell must be conserved following the redox balance, the electrons may be partitioned differently into machinery for substrate uptake vs. biomass synthesis, for instance, for different phenotypes. This partitioning can be quantitatively related to ecological fitness and biogeochemical impact via uptake kinetics, effective yields, and other traits9 (Supplementary Note 1 and Supplementary Fig. 1).
Fig. 3: Solutions from a global simulation resolving multiple metabolic functional types.

Net primary productivity (NPP), the biomasses of the metabolic functional types, and the sinking particulate organic carbon (POC) flux are resolved along a transect of a global microbial ecosystem model coupled with an estimate of the ocean circulation (Darwin-MITgcm18).

Full size image

Box 1 Incorporating metabolic functional types into ecosystem models

A metabolic functional type can be represented as a population with a growth rate that is limited or co-limited by multiple required substrates. If Liebig’s Law of the Minimum is employed, the limiting growth rate μ is described as

$$mu = min (V_{mathrm{i}}y_{mathrm{i}})$$
(1)

where Vi is the specific uptake rate of substrate i, and yield yi is the biomass yield with respect to that substrate. Yields for the different substrates and elements are interlinked in the metabolic budget derived from the underlying redox chemistry. Yields reflect Gibbs free energies of reaction among other factors. In the simplest model, non-limiting substrates are consumed in proportion to the limiting resource according to the metabolic budget, although in reality they may accumulate in the form of storage molecules.
Each metabolic functional type population can be incorporated into a multi-dimensional environmental model (e.g., an ocean simulation) with physical transport as

$$frac{{dB}}{{dt}} = mu B – Lleft( B right)B – underbrace {nabla cdot left( {bf{u}}{B} right)}_{{mathrm{advection}}} + underbrace {nabla cdot left( {{mathbf{kappa }}nabla B} right)}_{{mathrm{diffusion}}}$$
(2)

for biomass concentration B, loss rate L, velocity u, and diffusion coefficient κ. The loss rate function varies with biomass and represents a combination of processes, including predation, viral lysis, maintenance, and senescence. These processes remain largely unconstrained, although efforts have been made to relate losses to ecological dynamics107,108.
The yield partitions the amount of substrate taken up by the population into that used for growth, Viyi, versus that exiting the cell in modified form as a waste product, Vi(1 − yi) (Fig. 2 and Table 1). Equation 1 suggests a correlation between μ and y, but yields may be further modified by other factors. For example, accounting for maintenance energy decreases the ratio of growth to respiration, contributing to a decoupling between growth rate and yield particularly at low growth rates109. Furthermore, a trade-off between uptake rate and yield at the cellular level reflects the allocation of enzyme towards machinery for substrate uptake vs. biomass synthesis, among other factors. Considering a proteome constraint can incorporate this trade-off (Supplementary Note 1).

Benefits and implications for anticipating global change
Redox chemistry aids in reducing the number of degrees of freedom in descriptions of diverse microbial metabolisms. We next discuss the benefits of this electron-balanced approach, each contextualized by specific projected impacts of global change due to microbial activity and broad challenges in the fields of microbial ecology and biogeochemistry.
Flexible and broadly applicable metabolic thresholds: A key question for microbial biogeochemical studies, for which biogeochemical models are primed to answer, is how the biogeographies of diverse, active metabolisms vary with changes in the physical and chemical environment. What threshold determines the viability of a given metabolism?
Redox-balanced metabolic budgets obviate the need to impose critical concentrations or other thresholds that determine the presence of any given metabolism. Rather than being imposed following empirical relationships, metabolic biogeography emerges dynamically from ecological interactions and reflects environmental chemical potential. This flexibility aids in understanding metabolic thresholds more fundamentally, and it expands model applicability to diverse and unobserved environments. This is of particular importance for understanding global change, since past and future worlds may include very different ecosystems that do not reflect current empirical trends.
For example, the oceans are currently losing oxygen due to global warming5,70. If anoxic zones continue to expand, this will increase the habitat of anaerobic microorganisms, whose respiration results in emissions of N2 and N2O to the atmosphere7. Many biogeochemical models prescribe O2 concentrations that inhibit anaerobic activity in accordance with observations of specific organisms or communities in experimental conditions. This assumes that the same O2 concentrations limit metabolism similarly in all environments, and often trades mechanistic understanding of oxygen limitation for empirical correlations that may reflect a variety of natural and introduced biases, such as micro-scale heterogeneity, physical mixing in the ocean, and experimental bottle effects.
In contrast, a metabolic functional type model does not require imposed oxygen threshold concentrations (Supplementary Fig. 3). When oxygen supply is abundant, anaerobic types are competitively excluded because growth using alternative electron acceptors is lower than with oxygen. When oxygen supply is low, aerobic populations may persist and continue to deplete any available oxygen even as their growth is limited by oxygen, allowing for a steady state stable coexistence of aerobic and anaerobic metabolisms, which is consistent with a variety of observations71.
Descriptions of microbial growth that reflect underlying chemical potential can enable predictions of many other metabolic transitions, such as nitrogen fixation, nitrification, and the transition to sulfur oxidation and reduction13,18,72,73,74. As another example, this approach predicts the restriction of nitrification from the sunlit surface ocean as a consequence of competitive exclusion by phytoplankton in many environments (Fig. 4 and Supplementary Fig. 2), as well as active nitrification in some surface locations where phytoplankton are limited by another factor not affecting the chemoautotrophs, such as at high latitudes where phytoplankton are limited by light18. The emergent exclusion from most of the surface ocean anticipates that many clades of nitrifying microorganisms have adapted to long-term exclusion from the surface and consequentially lost (or did not develop) photoprotective cellular machinery.
Fig. 4: Model simulation and observations of the marine nitrification system.

Biogeochemistry is driven by microbial metabolic functional types in a vertical water column model18. Lines are model solutions, and marked points are observations from two stations in the Pacific Ocean75,106 (see Supplementary Fig. 2 for more detail) (a). Chlorophyll a concentrations and abundances of ammonia-oxidizing organisms (AOO) and nitrite-oxidizing organisms (NOO). Observed abundances are of the 16S rRNA abundances of archaeal Marine Group I and Nitrospina-like bacteria75,106. Model abundances are converted from biomass with 0.1 fmol N cell-1 for AOO, 0.2 fmol N cell-1 for NOO76, and one gene copy per cell. (b). Light (solar irradiance) and bulk nitrification rates.

Full size image

Replacing implicit descriptions of organic matter remineralization: The fate of organic matter dictates the amount of carbon sequestered in the marine and terrestrial biospheres. Microbial consumption mediates the carbon stored in soils, the carbon stored in the ocean as dissolved organic matter (DOM), and the sinking flux of organic carbon that constitutes the marine biological carbon pump4, without which atmospheric CO2 would be 100–200 ppm higher than current levels. We want to understand how these carbon reservoirs respond to changes in climate, such as increased temperatures and changes in precipitation patterns. However, in biogeochemical models, simple rate constants often dictate the remineralization of elements from organic back into inorganic constituents.
Replacing simplistic parameterizations with dynamic metabolic functional types means that electron-balanced descriptions of growth and respiration instead drive the fate of organic matter in earth system models (Fig. 3). In addition to a more sophisticated and responsive description of carbon sequestration, non-living organic matter is fully integrated into ecosystem frameworks, enabling theoretical studies of phytoplankton-bacteria interactions to complement observational and experimental approaches.
Much work remains in the development of these descriptions. As we discuss below, accurate estimates of organic matter turnover rates require more accurate descriptions of the complex processes governing microbial uptake rates of organic matter. However, the redox-informed yields are still useful for quantifying the relative amount of CO2 excreted and the absolute amount of biomass sustained on a given substrate, independent of uptake kinetics (Supplementary Note 2).
Relationships between abundances, rates, nutrient concentrations, and elemental ratios: An overarching puzzle challenging microbial ecology is to understand how chemical transformations in the environment are set by the ecological interactions at the organism level, among individual microscopic cells. It is clear that abundances of populations are not simply and directly correlated with biogeochemical impact (i.e., higher abundance does not necessarily imply an associated higher rate of chemical transformation). Untangling the relationship between abundances and biogeochemical function is also necessary for interpretation of genetic evidence that provide insight into this complex ecosystem structure.
Redox-balanced metabolic functional type modeling links rates of biomass synthesis associated with a particular metabolism to its rate of respiration as well as the standing stock of limiting nutrients. As functional type modeling is coupled with estimates of population loss rates due to grazing, viral lysis, or other mortality, simulations also resolve the standing stocks of functional biomass. This quantifies the relationship between biomass concentrations and volumetric rates of chemical transformations, emphasizing how relatively low biomass may be associated with relatively high bulk rates71.
For example, the approach has revealed a clear example of the signature of chemical potential in the ecology of marine nitrification18 (Fig. 4). In this model, the two steps of nitrification are represented by two functional type populations. This predicts about a three-fold difference in the abundances of the organisms responsible for each of the two steps of nitrification, despite the fact that the two populations carry out the same rate of subsurface N-cycling at steady state6,18. A three-fold or greater difference in abundance and associated ammonium (NH4+) and nitrite (NO2−) concentrations is consistent with observed differences18,75, and it reflects that the oxidation of one mole of NH4+ generates three times more electrons than the oxidation of one mole of NO2−, with differences in cell size further contributing to differences in abundances (Fig. 4 and Supplementary Fig. 2). Recent observations confirm the redox-based difference in NH4+ and NO2− biomass yield76,77, although measured rates from a nonsteady environment suggest that NO2−-oxidizing bacteria can partition electrons more efficiently than NH4+-oxidizing archaea76 (i.e., higher fraction f despite lower yield y; see Supplementary Note 3).
As redox-based descriptions resolve the stoichiometry of whole organism metabolism, they also link together elemental cycles. Explicit description of relative elemental flow through the ecosystem, and specifically their variation from average values, is critical for understanding climate-biogeochemical feedbacks78,79,80. For example, the nitrification model also estimates the CO2 fixation rates associated with nitrification rates (Supplementary Fig. 2), enabling global-scale, electron-balanced projections of the amount of carbon converted to organic form by chemoautotrophic nitrifying microorganisms.
Connections with sequencing datasets: How do we relate metabolic functional type models to sequencing datasets measuring genetic, transcriptomic, and proteomic diversity? Connecting biogeochemical models with sequencing data is critical because this data provides an enormous amount of information about ecosystem structure and function. Genes (or transcripts) themselves are not necessarily the most concise or useful currency given functional redundancies, unattributed function, and variation in gene dosage from horizontal gene transfer as well as growth rate40. Recent gene-centric models aim to resolve the abundances of key genes as proxies for a predetermined set of metabolic pathways13,14,17. However, the parameters used to describe metabolic pathways in these models are estimated similarly to the redox-balanced yields and efficiencies described here.
The innovation of gene-centric models is the sophisticated conversion of estimates of biogeochemical activity and biomass to genes. For example, the model of Coles et al.17 resolves biomass and nutrient concentrations prognostically, and then uses a three-part formula—representing constitutive, regulated, and steady state transcription—to diagnostically calculate transcription rates from modeled biomass and growth rates17. Thus, the two types of modeling are complimentary, with redox chemistry providing estimates of metabolic activity from fundamental principles, and the careful calibrations between activity and sequencing providing a comparative metric.
The examples here externalize the conversion between modeled activity and sequencing information as a transparent process. In Fig. 4, the predicted functional biomass of ammonia-oxidizing population is related to archaeal Marine Group I (MGI) and Nitrospina-like 16S rRNA genes with two conversion factors: the cell elemental quota (fmol N cell−1) and the number of cellular gene copies. Conversion error arises since cell mass and size vary with growth rate81,82. Maintaining transparency of the conversion from predicted microbial activity to genes and transcripts allows interdisciplinary audiences to understand and critique the models.
Limitations and possible extensions
Using chemical potential as a theoretically grounding organizing principle for the resolution of diverse metabolisms can greatly improve microbial descriptions in global biogeochemical models. However, the approach does have its limitations, which generally increase in significance with increased temporal or spatial resolution.
Modeling metabolic diversity with functional type populations requires choosing how metabolisms are distributed among the populations. This has consequences when interpreting time-varying states: model solutions become dependent on the partitioning of metabolism among the functional types as the timescales of physical change approach the timescales of microbial growth (see Supplementary Note 4, Supplementary Fig. 3, and Supplementary Fig. 4 for a detailed example). Other species-specific time-varying phenomena such as the lag response of organisms to substrate availability also become relevant83. On one hand, this is beneficial for resolution of microbial processes in fine-grained ocean circulation models where flow can vary on the order of days. However, incorporating another constraint, such as proteome allocation69, is necessary to inform these choices. For example, considering enzymatic allocation in combination with energetics allowed for the prediction of both the division of nitrification into a two-step process in mixed environments and the combined, complete pathway in one organism (comammox) in biofilms, which preceded observations of the latter84,85,86.
Uncertainty in distributions of metabolism lies not only in the length of a metabolic pathway, but also in the degree of metabolic versatility (metabolic mixotrophy). Such versatility characterizes key players in large-scale biogeochemistry, such as nitrite-oxidizing bacteria and photoheterotrophs87,88,89. Mixotrophic lifestyles can increase the fitness of populations in their environments, impacting overall ecosystem function90. In one sense, the approach here provides a prediction of where we might expect such mixotrophy by resolving stable coexistences of diverse metabolisms. In Fig. 3, for example, syntrophic coexistence occurs at depth among heterotrophs, ammonia oxidizers, and nitrite oxidizers, and future work could investigate what determines which combinations of these coexistences remain as passive interactions, which develop into mutualistic dependencies as active interactions91, and which evolve into mixotrophic phenotypes or endosymbionts. Additionally, by considering the potential to carry out a metabolism as a trait, we can use the current framework along with an additional constraint to investigate implications of metabolic mixotrophy. For example, Coles et al.17 impose a trade-off between the degree of metabolic diversity of a single functional type and growth rate, enabling the exploration the consequences of distribution of metabolism on the biogeochemical state.
Also, the metabolic functional type approach resolves only active functional biomass, while evidence suggests that less than 10% to more than 75% of the microbial community may be inactive92. Some seemingly inactive populations may slowly metabolize over long timescales, requiring longer model integration times and careful attention to their loss rates for resolution, while some populations are periodically active as revealed by high-resolution observations in time37.
The proposed modeling approach relies on estimates of the limiting uptake rates of required substrates. In lieu of suitable theoretical descriptions, the use of empirically derived uptake kinetic parameters still employs the benefits of the redox-informed yields (Supplementary Note 2). However, underlying physical constraints to substrate acquisition can in principle be exploited to develop more universally applicable descriptions for a variety of substrates and contexts. Uptake kinetics are complex, but for many limiting resources, encounter effectively controls the uptake, and the physics of encounter has been relatively well described. For example, uptake rates estimated from diffusive supply of substrate, cellular geometry, and membrane physiology64,65,66 have been empirically supported93. For organic matter, future work is needed to develop suitable descriptions of consumption rates, whether empirical or theoretical. For example, descriptions require attention to the hydrolysis of organic compounds by extracellular enzymes and the ecology of sinking marine particles—the diffusion of monomer away from the particle, within-particle transport, and dynamic ecological interactions on particle surfaces, among other processes11,74,94,95.
In Fig. 3, the electron-balanced description consists of an average stoichiometry and electron fraction for one sinking pool of organic matter in the ocean, which, as mentioned above, is not sufficient to accurately resolve the carbon storage that is shaped by a distribution of turnover rates. As one of the many factors impacting the rates, an energetics-based perspective can serve as a tool for further deciphering organic matter complexity. For example, organic matter may be partially organized by the nominal oxidation state of its carbon atoms, which relates to a measure of free energy and accessibility96,97. This could be used to improve the phenomenological description of organic matter in models as labile vs. non-labile, for example, with a more mechanistic underpinning.
Descriptions of phytoplankton are currently much more sophisticated than of bacteria and archaea in models, reflecting a longer history of comprehensive sets of observations. However, further work could develop simple descriptions of photoautotrophic metabolisms from underlying energetics by connecting the supply of photons to available energy for biosynthesis within the cell. Many biogeochemical models account for an inefficiency of phytoplankton metabolism with a parameter that dictates their excretions of dissolved organic matter98. Incorporating this excretion into an energetic framework would enhance studies of phytoplankton ecology, such as studies of photoautotrophic-heterotrophic interactions in the ocean surface or photoautotrophic-chemoautotrophic interactions at the base of the euphotic zone where some phytoplankton excrete nitrite due to incomplete reduction of nitrate99.
As a more radical extension, can we progress past population modeling and model microbial consortia as one aggregate community biomass34,100? This may improve resolution of time-varying metabolic versatility. However, if both steps of nitrification were a part of such a consortium, would the characteristic accumulation of nitrite be predicted (Supplementary Fig. 2)? We leave these questions for future research and conclude that the best choice for the degree of resolution of metabolism will depend on the specific research question and the available observations.
We have described a useful approach for understanding and anticipating microbial control of biogeochemical cycling that is suitable for global applications. The approach aims to represent microbial growth and respiration explicitly and consistently from knowledge of chemical gradients in the environment, towards a goal of building an independently constructed theoretical ecosystem model that can then be compared to observations. Describing microbial communities with underlying energetic constraints connects metabolisms dynamically with global geochemical distributions, such as those of carbon dioxide, oxygen, and biologically available nitrogen. This deepens our understanding of microbial ecosystems and enables the incorporation of the feedbacks of microbial activity to changes in global biogeochemistry and the climate system. More

Experimental limitations and implications
Many factors are known to contribute to mollusk shell strength including thickness, microstructure, previous shell damage, and ornamentation19,21,29,30,31. Thus, it is challenging to separate the influence of one feature from another when testing the strength of real shells. Fortunately, there are alternative methods by which to model shells22,32,33,34. 3D printing is an effective tool to create model shells that exhibit brittle behavior in compression, serving as an accurate first-order approximation for shell breakage (Fig. 2). 3D printed models are not created to replicate shell microstructure; rather they serve to normalize confounding factors encountered with real shells, like size, variations in thickness, and taphonomy/degradation of microstructure, to isolate variables of interest while maintaining the predominantly brittle behavior seen in real shells. As a result, the failure of different 3D printed shapes under bulk mechanical compression can be used for relative comparisons between morphologies without needing to reproduce the exact magnitude of load to failure of real shells22,33 (see “Methods”, Fig. 2). Natural shells ultimately break after cracking through microstructural layers. Analogously, in this study, 3D printed shells cracked predominantly through layers of printed materials, not along printing boundaries, demonstrating an appropriate proxy for shell failure (Figs. 1, 2).
Resistance to compression by a predator can be tested directly using a variety of loading tests, the effectiveness of which has been demonstrated repeatedly by investigators studying topics ranging from predation to climate change22,33,34. Compression experiments of bivalve shells, specifically, have been used extensively for this purpose35,36,37,38,39,40,41,42,43,44,45,46. In this study, the mechanical strength of model bivalve shell shapes was analyzed to understand potential defensive value against durophagous (crushing) predators using hypothetical flat teeth and jaws to crush prey (see Crofts and Summers (2014)22 for examples of flat crushing morphologies) (Figs. 1, 2). These experiments are most analogous to small shell-crushers with flat dentitions, like fishes (e.g., guitar fishes, stingrays, etc.), rather than large predators like the modern walrus or extinct marine reptiles (e.g., placodonts, mosasaurs) which are so much larger than their prey that the differences in shell strength resulting from shape are likely insignificant. Bivalves are also preyed upon by many other predators including asteroids, gastropods, birds, and mammals47. The ability to escape is likely dependent upon predator capabilities, where not all escape mechanisms are equally effective against different predators. The results of these experiments are not intended to be general proxies of predation resistance—they are only applicable as a proxy for predators that use flat crushing dentitions. Additionally, it is important to note that compression of a shell by vertebrate predators is a different mechanical process from compression by an arthropod, as claws localize forces differently than teeth and jaws48. Therefore, the experiments used in this study are not meant to model predation by invertebrate durophages. This experimental setup represents an idealized case of shell compression assuming consistent shell thickness and a common predator using a quasistatic loading regime to isolate the influence of shell shape. Therefore, this study makes no conclusions as to the effects of shell shape on strength under conditions of point loading by claws or impact. Furthermore, while bivalve shape is undoubtedly influenced by many factors including fabrication (shell growth and construction)49, phylogenetics, location of soft tissue, etc., these experiments were designed to study an idealized case in which defense against vertebrate shell crushing predators is the most important functional constraint on shell shape.
Advantageously, the use of mathematically generated theoretical bivalve shells, rather than real shells, enables testing a range of shapes—some of which can be or have been found in nature—and others that have yet to exist. For example, these methods enable physical testing of morphologies which are only found in the fossil record. Fossils cannot be used for mechanical experimentation due to changes in the integrity of the shell structure resulting from the fossilization process. Furthermore, testing shapes which do not exist due to biological constraints like fabrication, can provide valuable insights when combined with the study of shapes that have evolved naturally. For example, testing theoretical shapes can reveal morphologies that perform better than natural morphologies in specific functional settings. Thus, testing shapes which perform well, but do not exist, is one way to identify the potential influence of evolutionary constraints such as fabrication (shell growth patterns), phylogenetics, or other necessary biological functions. In this study, the extremely perpendicular-elongate shells represent theoretical shapes that are not seen in nature (likely due to space limitations for soft tissue). Developing theoretical physical models also allows for the elimination of many confounding variables that cannot be avoided when testing real shells. The effects of variations in shell thickness and ornamentation, which also play a role in shell strength, can thus be separated from the strength imparted by gross shell shape when theoretical models are generated. However, because overall shell strength is derived from a combination of these many factors (microstructure, thickness, etc.31.) the conclusions from these experiments represent a first-order approximation of the effects of shape on shell strength alone. Because the models for this study were generated mathematically, and are therefore not exact replicas of specific taxa, the discussion below aims to address potential tradeoffs based on general shell shapes that could be further studied for specific bivalve taxa in future experiments.
Shell shape and strength
Three parameters of shell shape were modified for this study. (1) Generating curve shape is both a modeling parameter and biological feature of mollusk shells27. In mathematical models, generating curve shape describes the shape that is rotated around an axis to create a surface that represents the overall shell shape. Biologically, the generating curve refers to the portion of the shell upon which new material is secreted by the mantle. In bivalves, this is the shape of the commissure. Thus, by changing the shape of the generating curve in a model bivalve, i.e., changing the shape of the ellipse that generates the surface of the shell, models can be perpendicular-elongated (a  > b) or parallel-elongated (a  More

1.
Pfäffle, M., Littwin, N., Muders, S. V. & Petney, T. N. The ecology of tick-borne diseases. Int. J. Parasitol. 43, 1059–1077 (2013).
Article  Google Scholar 
2.
Han, B. A. & Yang, L. Predicting novel tick vectors of zoonotic disease. in ICML Workshop on #Data4Good: Machine Learning in Social Good Applications 71–75 (2016).

3.
de la Fuente, J., Estrada-Pena, A., Venzal, J. M., Kocan, K. M. & Sonenshine, D. E. Overview: ticks as vectors of pathogens that cause disease in humans and animals. Front. Biosci. 13, 6938–6946 (2008).
Article  Google Scholar 

4.
Michelet, L. et al. High-throughput screening of tick-borne pathogens in Europe. Front. Cell. Infect. Microbiol. 4, 103 (2014).
Article  Google Scholar 

5.
Estrada-Peña, A. & de la Fuente, J. The ecology of ticks and epidemiology of tick-borne viral diseases. Antiviral Res. 108, 104–128 (2014).
Article  CAS  Google Scholar 

6.
Paul, R. E. L. et al. Environmental factors influencing tick densities over seven years in a French suburban forest. Parasit. Vectors 9, 309 (2016).
Article  CAS  Google Scholar 

7.
Randolph, S. E. Tick-borne disease systems emerge from the shadows: the beauty lies in molecular detail, the message in epidemiology. Parasitology 136, 1403 (2009).
CAS  Article  Google Scholar 

8.
Jore, S. et al. Multi-source analysis reveals latitudinal and altitudinal shifts in range of Ixodes ricinus at its northern distribution limit. Parasit. Vectors 4, 1–11 (2011).
Article  Google Scholar 

9.
Bernstein, L. et al. Climate Change 2007: Synthesis Report. Contribution of working groups I, II and III to the fourth assessment report of the Intergovernmental Panel on Climate Change. Intergovernmental Panel on Climate Change [Core Writing Team, Pachauri, R.K and Reisinger, A. (eds.)] https://www.ipcc.ch/pdf/assessment-report/ar4/syr/ar4_syr_full_report.pdf (2007).

10.
Kovats, R. S., Campbell-Lendrum, D. H., McMichael, A. J., Woodward, A. & Cox, J. S. Early effects of climate change: do they include changes in vector-borne disease?. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 356, 1057–1068 (2001).
CAS  PubMed  PubMed Central  Google Scholar 

11.
Gage, K. L., Burkot, T. R., Eisen, R. J. & Hayes, E. B. Climate and vectorborne diseases. Am. J. Prev. Med. 35, 436–450 (2008).
Article  Google Scholar 

12.
Medlock, J. M. et al. Driving forces for changes in geographical distribution of Ixodes ricinus ticks in Europe. Parasit. Vectors 6, 1–11 (2013).
Article  Google Scholar 

13.
Andreassen, A. et al. Prevalence of tick borne encephalitis virus in tick nymphs in relation to climatic factors on the southern coast of Norway. Parasit. Vectors 5, 1–12 (2012).
Article  Google Scholar 

14.
Soleng, A. et al. Distribution of Ixodes ricinus ticks and prevalence of tick-borne encephalitis virus among questing ticks in the Arctic Circle region of northern Norway. Ticks Tick. Borne. Dis. 9, 97–103 (2018).
CAS  Article  Google Scholar 

15.
Kjelland, V. et al. Tick-borne encephalitis virus, Borrelia burgdorferi sensu lato, Borrelia miyamotoi, Anaplasma phagocytophilum and Candidatus Neoehrlichia mikurensis in Ixodes ricinus ticks collected from recreational islands in southern Norway. Ticks Tick. Borne. Dis. 9, 1098–1102 (2018).
Article  Google Scholar 

16.
Paulsen, K. M. et al. Prevalence of tick-borne encephalitis virus in Ixodes ricinus ticks from three islands in north-western Norway. APMIS 123, 759–764 (2015).
Article  Google Scholar 

17.
Kjær, L. J. et al. A large-scale screening for the taiga tick, Ixodes persulcatus, and the meadow tick, Dermacentor reticulatus, in southern Scandinavia, 2016. Parasit. Vectors 12, 338 (2019).
Article  Google Scholar 

18.
Oechslin, C. P. et al. Prevalence of tick-borne pathogens in questing Ixodes ricinus ticks in urban and suburban areas of Switzerland. Parasit. Vectors 10, 558 (2017).
Article  CAS  Google Scholar 

19.
Becker, N. S. et al. Recurrent evolution of host and vector association in bacteria of the Borrelia burgdorferi sensu lato species complex. BMC Genom. 17, 734 (2016).
Article  CAS  Google Scholar 

20.
Bowman, A. S. & Nuttall, P. A. Ticks: Biology, Disease and Control (Cambridge University Press, Cambridge, 2004).
Google Scholar 

21.
Hasle, G. et al. Transport of ticks by migratory passerine birds to Norway. J. Parasitol. 95, 1342–1351 (2009).
PubMed  Google Scholar 

22.
Klitgaard, K. et al. Screening for multiple tick-borne pathogens in Ixodes ricinus ticks from birds in Denmark during spring and autumn migration seasons. Ticks Tick. Borne. Dis. 10, 546–552 (2019).
PubMed  Google Scholar 

23.
Skarphédinsson, S. et al. Detection and identification of Anaplasma phagocytophilum, Borrelia burgdorferi, and Rickettsia helvetica in Danish Ixodes ricinus ticks. APMIS 115, 225–230 (2007).
PubMed  Google Scholar 

24.
Fraenkel, C.-J., Garpmo, U. & Berglund, J. Determination of novel Borrelia genospecies in Swedish Ixodes ricinus ticks. J. Clin. Microbiol. 40, 3308–3312 (2002).
CAS  PubMed  PubMed Central  Google Scholar 

25.
Wilhelmsson, P. et al. Prevalence, diversity, and load of Borrelia species in ticks that have fed on humans in regions of Sweden and Åland Islands, Finland with different Lyme borreliosis incidences. PLoS ONE 8, e81433 (2013).
ADS  PubMed  PubMed Central  Google Scholar 

26.
Vennestrøm, J., Egholm, H. & Jensen, P. M. Occurrence of multiple infections with different Borrelia burgdorferi genospecies in Danish Ixodes ricinus nymphs. Parasitol. Int. 57, 32–37 (2008).
PubMed  Google Scholar 

27.
Kjelland, V., Stuen, S., Skarpaas, T. & Slettan, A. Prevalence and genotypes of Borrelia burgdorferi sensu lato infection in Ixodes ricinus ticks in southern Norway. Scand. J. Infect. Dis. 42, 579–585 (2010).
CAS  PubMed  Google Scholar 

28.
Klitgaard, K., Kjær, L. J., Isbrand, A., Hansen, M. F. & Bødker, R. Multiple infections in questing nymphs and adult female Ixodes ricinus ticks collected in a recreational forest in Denmark. Ticks Tick. Borne. Dis. 10, 1060–1065 (2019).
PubMed  Google Scholar 

29.
Maraspin, V., Ruzic-Sabljic, E. & Strle, F. Lyme borreliosis and Borrelia spielmanii. Emerg. Infect. Dis. 12, 1177–1177 (2006).
PubMed  PubMed Central  Google Scholar 

30.
Rudenko, N., Golovchenko, M., Grubhoffer, L. & Oliver, J. H. Updates on Borrelia burgdorferi sensu lato complex with respect to public health. Ticks Tick. Borne. Dis. 2, 123–128 (2011).
Article  Google Scholar 

31.
Fertner, M. E., Mølbak, L., Pihl, T. P. B., Fomsgaard, A. & Bødker, R. First detection of tick-borne “Candidatus Neoehrlichia mikurensis” in Denmark 2011. Eurosurveillance 17, 20096 (2012).
Google Scholar 

32.
Quarsten, H. et al. Candidatus Neoehrlichia mikurensis and Borrelia burgdorferi sensu lato detected in the blood of Norwegian patients with erythema migrans. Ticks Tick. Borne. Dis. 8, 715–720 (2017).
CAS  Article  Google Scholar 

33.
Stuen, S., Granquist, E. G. & Silaghi, C. Anaplasma phagocytophilum—a widespread multi-host pathogen with highly adaptive strategies. Front. Cell. Infect. Microbiol. 3, 31 (2013).
Article  CAS  Google Scholar 

34.
Fomsgaard, A. et al. Tick-borne encephalitis virus, Zealand, Denmark, 2011. Emerg. Infect. Dis. 19, 1171–1173 (2013).
Article  Google Scholar 

35.
Jensen, P. M. et al. Transmission differentials for multiple pathogens as inferred from their prevalence in larva, nymph and adult of Ixodes ricinus (Acari: Ixodidae). Exp. Appl. Acarol. 71, 171–182 (2017).
Article  Google Scholar 

36.
Lundkvist, Å., Wallensten, A., Vene, S. & Hjertqvist, M. Tick-borne encephalitis increasing in Sweden, 2011. Eurosurveillance 16, 19981 (2011).
Article  Google Scholar 

37.
Svensson, J., Hunfeld, K.-P. & Persson, K. E. M. High seroprevalence of Babesia antibodies among Borrelia burgdorferi-infected humans in Sweden. Ticks Tick. Borne. Dis. 10, 186–190 (2019).
Article  Google Scholar 

38.
Mørch, K., Holmaas, G., Frolander, P. S. & Kristoffersen, E. K. Severe human Babesia divergens infection in Norway. Int. J. Infect. Dis. 33, 37–38 (2015).
Article  Google Scholar 

39.
Uhnoo, I. et al. First documented case of human babesiosis in Sweden. Scand. J. Infect. Dis. 24, 541–547 (2009).
Article  Google Scholar 

40.
Dumler, J. S., Barat, N. C., Barat, C. E. & Bakken, J. S. Human granulocytic anaplasmosis and macrophage activation. Clin. Infect. Dis. 45, 199–204 (2007).
CAS  Article  Google Scholar 

41.
Nilsson, K., Elfving, K. & Påhlson, C. Rickettsia helvetica in patient with meningitis, Sweden, 2006. Emerg. Infect. Dis. 16, 490–492 (2010).
CAS  Article  Google Scholar 

42.
Frivik, J. O., Noraas, S., Grankvist, A., Wennerås, C. & Quarsten, H. En mann i 60-årene fra Sørlandet med intermitterende feber (In Norwegian). Tidsskr. Den Nor. legeforening 137, (2017).

43.
Grankvist, A. et al. Infections with the tick-borne bacterium ‘Candidatus Neoehrlichia mikurensis’ mimic noninfectious conditions in patients with B cell malignancies or autoimmune diseases. Clin. Infect. Dis. 58, 1716–1722 (2014).
CAS  Article  Google Scholar 

44.
Welinder-Olsson, C., Kjellin, E., Vaht, K., Jacobsson, S. & Wenneras, C. First case of human ‘Candidatus Neoehrlichia mikurensis’ infection in a febrile patient with chronic lymphocytic leukemia. J. Clin. Microbiol. 48, 1956–1959 (2010).
Article  Google Scholar 

45.
Rizzoli, A. et al. Ixodes ricinus and its transmitted pathogens in urban and peri-urban areas in Europe: new hazards and relevance for public health. Front. Public Health 2, 251 (2014).
Article  Google Scholar 

46.
Michelitsch, A., Wernike, K., Klaus, C., Dobler, G. & Beer, M. Exploring the reservoir hosts of tick-borne encephalitis virus. Viruses vol. 11 (2019).

47.
Keesing, F. et al. Reservoir competence of vertebrate hosts for Anaplasma phagocytophilum. Emerg. Infect. Dis. 18, 2013–2016 (2012).
Article  Google Scholar 

48.
Zhan, L. et al. Anaplasma phagocytophilum in livestock and small rodents. Vet. Microbiol. 144, 405–408 (2010).
ADS  Article  Google Scholar 

49.
Portillo, A., Santibáñez, P., Palomar, A. M., Santibáñez, S. & Oteo, J. A. Candidatus Neoehrlichia mikurensis, Europe. New Microbes New Infect. 22, 30–36 (2018).
CAS  Article  Google Scholar 

50.
Jenkins, A. et al. Detection of Candidatus Neoehrlichia mikurensis in Norway up to the northern limit of Ixodes ricinus distribution using a novel real time PCR test targeting the groEL gene. BMC Microbiol. 19, 199 (2019).
PubMed  PubMed Central  Google Scholar 

51.
Obiegala, A. & Silaghi, C. Candidatus Neoehrlichia mikurensis—recent insights and future perspectives on clinical cases, vectors, and reservoirs in Europe. Curr. Clin. Microbiol. Rep. 5, 1–9 (2018).
Google Scholar 

52.
Yabsley, M. J. & Shock, B. C. Natural history of zoonotic Babesia: role of wildlife reservoirs. Int. J. Parasitol. Parasites Wildl. 2, 18–31 (2013).
PubMed  Google Scholar 

53.
Sprong, H. et al. Ixodes ricinus ticks are reservoir hosts for Rickettsia helvetica and potentially carry flea-borne Rickettsia species. Parasit. Vectors 2, 41 (2009).
PubMed  PubMed Central  Google Scholar 

54.
Jaenson, T. G. T. et al. Risk indicators for the tick Ixodes ricinus and Borrelia burgdorferi sensu lato in Sweden. Med. Vet. Entomol. 23, 226–237 (2009).
CAS  PubMed  Google Scholar 

55.
Hudson, P. J. et al. Tick-borne encephalitis virus in northern Italy: molecular analysis, relationships with density and seasonal dynamics of Ixodes ricinus. Med. Vet. Entomol. 15, 304–313 (2001).
MathSciNet  CAS  Article  Google Scholar 

56.
Nazzi, F. et al. Ticks and Lyme borreliosis in an alpine area in northeast Italy. Med. Vet. Entomol. 24, 220–226 (2010).
CAS  PubMed  Google Scholar 

57.
Hubalek, Z., Halouzka, J. & Juricova, Z. Longitudinal surveillance of the tick Ixodes ricinus for Borreliae. Med. Vet. Entomol. 17, 46–51 (2003).
CAS  Article  Google Scholar 

58.
Lindström, A. & Jaenson, T. G. T. Distribution of the common tick, Ixodes ricinus (Acari: Ixodidae), in different vegetation types in southern Sweden. J. Med. Entomol. 40, 375–378 (2003).
Article  Google Scholar 

59.
Mejlon, H. A. & Jaenson, T. G. T. Jaenson (1993) Seasonal prevalence of Borrelia burgdorferi in Ixodes ricinus in different vegetation types in Sweden. Scand. J. Infect. Dis. 25, 449–456 (2009).
Article  Google Scholar 

60.
Tack, W. et al. Local habitat and landscape affect Ixodes ricinus tick abundances in forests on poor, sandy soils. For. Ecol. Manag. 265, 30–36 (2012).
Google Scholar 

61.
Walhström, L. K. & Kjellander, P. Ideal free distribution and natal dispersal in female roe deer. Oecologia 103, 302–308 (1995).
ADS  PubMed  Google Scholar 

62.
Zeman, P. Objective assessment of risk maps of tick-borne encephalitis and Lyme borreliosis based on spatial patterns of located cases. Int. J. Epidemiol. 26, 1121–1129 (1997).
CAS  PubMed  Google Scholar 

63.
Jat, M. K. & Mala, S. Application of GIS and space-time scan statistic for vector born disease clustering. In ICEGOV ’17 Proceedings of the 10th International Conference on Theory and Practice of Electronic Governance (2017) https://doi.org/10.1145/3047273.3047361.

64.
Hönig, V. et al. Model of risk of exposure to Lyme borreliosis and tick-borne encephalitis virus-infected ticks in the border area of the Czech Republic (South Bohemia) and Germany (Lower Bavaria and Upper Palatinate). Int. J. Environ. Res. Public Health 16, 1173 (2019).
PubMed Central  Google Scholar 

65.
Randolph, S. E. & Rogers, D. J. Fragile transmission cycles of tick-borne encephalitis virus may be disrupted by predicted climate change. Proc. Biol. Sci. 267, 1741–1744 (2000).
CAS  PubMed  PubMed Central  Google Scholar 

66.
Stefanoff, P. et al. A Predictive model has identified tick-borne encephalitis high-risk areas in regions where no cases were reported previously, Poland, 1999–2012. Int. J. Environ. Res. Public Health 15, 677 (2018).
PubMed Central  Google Scholar 

67.
Kjær, L. J. et al. Predicting and mapping human risk of exposure to Ixodes ricinus nymphs using climatic and environmental data, Denmark, Norway and Sweden, 2016. Eurosurveillance 24, 1800101 (2019).
PubMed Central  Google Scholar 

68.
Kjær, L. J. et al. Predicting the spatial abundance of Ixodes ricinus ticks in southern Scandinavia using environmental and climatic data. Sci. Rep. 9, 18144 (2019).
ADS  Google Scholar 

69.
Kjær, L. J. et al. Spatial data of Ixodes ricinus instar abundance and nymph pathogen prevalence, Scandinavia, 2016–2017. Collection https://doi.org/10.6084/m9.figshare.c.4938270.v1 (2020).
Article  Google Scholar 

70.
Kjær, L. J. et al. Spatial data of Ixodes ricinus instar abundance and nymph pathogen prevalence, Scandinavia, 2016–2017. Sci. Data 7, 1–7 (2020).
Google Scholar 

71.
Scharlemann, J. P. W. et al. Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data. PLoS ONE 3, e1408 (2008).
ADS  Article  Google Scholar 

72.
Corine Land Cover 2006 raster data. European Environment Agency https://www.eea.europa.eu/data-and-maps/data/clc-2006-raster (2010).

73.
Klitgaard, K., Chriél, M., Isbrand, A., Jensen, T. K. & Bødker, R. Identification of Dermacentor reticulatus ticks carrying Rickettsia raoultii on migrating jackal, Denmark. Emerg. Infect. Dis. 23, 2072–2074 (2017).
Article  Google Scholar 

74.
Moutailler, S. et al. Co-infection of ticks: the rule rather than the exception. PLoS Negl. Trop. Dis. 10, e0004539 (2016).
Article  CAS  Google Scholar 

75.
Reye, A. L. et al. Prevalence of tick-borne pathogens in Ixodes ricinus and Dermacentor reticulatus ticks from different geographical locations in Belarus. PLoS ONE 8, e54476 (2013).
ADS  CAS  Article  Google Scholar 

76.
R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing https://www.r-project.org (2018).

77.
Cowling, D. W., Gardner, I. A. & Johnson, W. O. Comparison of methods for estimation of individual-level prevalence based on pooled samples. Prev. Vet. Med. 39, 211–225 (1999).
CAS  Article  Google Scholar 

78.
ESRI. ArcGIS Desktop: Release 10.6.1. Redlands, CA: Environmental Systems Research Institute. (2017).

79.
Kulldorff M. and Information Management Services, I. SaTScanTM v9.6: Software for the spatial and space-time scan statistics www.satscan.org, 2018.

80.
Kleinman, K. rsatscan: Tools, classes, and methods for interfacing with SaTScan stand-alone software. (2015).

81.
Kulldorff, M. A spatial scan statistic. Communications in Statistics – Theory and Methods vol. 26 https://www.tandfonline.com/doi/abs/10.1080/03610929708831995 (1997).

82.
Han, J. et al. Using Gini coefficient to determining optimal cluster reporting sizes for spatial scan statistics. Int. J. Health Geogr. 15, 27 (2016).
Article  Google Scholar 

83.
Kuhn., M., Contributions from Jed Wing, Steve Weston, Andre Williams, Chris Keefer, Allan Engelhardt, T., Cooper, Zachary Mayer, Brenton Kenkel, the R Core Team, Michael Benesty, Reynald Lescarbeau, A. Z. & Luca Scrucca, Yuan Tang, C. C. and T. H. caret: Classification and regression training. R package version 6.0-81. https://CRAN.R-project.org/package=caret. (2018).

84.
Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).
CAS  Article  Google Scholar 

85.
Vapnik, V., Golowich, S. E. & Smola, A. Support vector method for function approximation, regression estimation, and signal processing. in Advances in Neural Information Processing Systems 9 (eds. Mozer, M., Jordan, M. & Petsche, T.) 281–287 (MIT Press., 1997).

86.
Sanz, H., Valim, C., Vegas, E., Oller, J. M. & Reverter, F. SVM-RFE: selection and visualization of the most relevant features through non-linear kernels. BMC Bioinform. 19, 432 (2018).
Article  Google Scholar 

87.
Ghojogh, B., Ca, B., Crowley, M. & Ca, M. The theory behind overfitting, cross validation, rRegularization, bagging, and boosting: tTutorial. https://arxiv.org/abs/1905.12787 [stat.ML] 1–23 (2019).

88.
Skarphédinsson, S., Jensen, P. M. & Kristiansen, K. Survey of tickborne infections in Denmark. Emerg. Infect. Dis. 11, 1055–1061 (2005).
Article  Google Scholar 

89.
Quarsten, H., Skarpaas, T., Fajs, L., Noraas, S. & Kjelland, V. Tick-borne bacteria in Ixodes ricinus collected in southern Norway evaluated by a commercial kit and established real-time PCR protocols. Ticks Tick. Borne. Dis. 6, 538–544 (2015).
CAS  Article  Google Scholar 

90.
Wilhelmsson, P. et al. Prevalence and diversity of Borrelia species in ticks that have bitten humans in Sweden. J. Clin. Microbiol. 48, 4169–4176 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

91.
Strnad, M., Hönig, V., Růžek, D., Grubhoffer, L. & Rego, R. O. M. Europe-wWide meta-analysis of Borrelia burgdorferi sensu lato prevalence in questing Ixodes ricinus ticks. Appl. Environ. Microbiol. 83, 3838 (2017).
Google Scholar 

92.
Mysterud, A. et al. Tick abundance, pathogen prevalence, and disease incidence in two contrasting regions at the northern distribution range of Europe. Parasit. Vectors 11, 309 (2018).
PubMed  PubMed Central  Google Scholar 

93.
Severinsson, K., Jaenson, T. G., Pettersson, J., Falk, K. & Nilsson, K. Detection and prevalence of Anaplasma phagocytophilum and Rickettsia helvetica in Ixodes ricinus ticks in seven study areas in Sweden. Parasit. Vectors 3, 66 (2010).
PubMed  PubMed Central  Google Scholar 

94.
Karlsson, M. E. & Andersson, M. O. Babesia species in questing Ixodes ricinus, Sweden. Ticks Tick. Borne. Dis. 7, 10–12 (2016).
PubMed  Google Scholar 

95.
Øines, Ø., Radzijevskaja, J., Paulauskas, A. & Rosef, O. Prevalence and diversity of Babesia spp. in questing Ixodes ricinus ticks from Norway. Parasit. Vectors 5, 156 (2012).
PubMed  PubMed Central  Google Scholar 

96.
Andersson, M., Bartkova, S., Lindestad, O. & Råberg, L. Co-Infection with ‘Candidatus Neoehrlichia mikurensis’ and Borrelia afzelii in Ixodes ricinus Ticks in Southern Sweden. Vector-Borne Zoonotic Dis. 13, 438–442 (2013).
PubMed  Google Scholar 

97.
Pedersen, B. N. et al. Distribution of Neoehrlichia mikurensis in Ixodes ricinus ticks along the coast of Norway: the western seaboard is a low-prevalence region. Zoonoses Public Health https://doi.org/10.1111/zph.12662 (2019).
Article  PubMed  Google Scholar 

98.
Kantsø, B., Bo Svendsen, C., Moestrup Jensen, P., Vennestrøm, J. & Krogfelt, K. A. Seasonal and habitat variation in the prevalence of Rickettsia helvetica in Ixodes ricinus ticks from Denmark. Ticks Tick. Borne. Dis. 1, 101–103 (2010).
PubMed  Google Scholar 

99.
Solano-Gallego, L., Sainz, Á., Roura, X., Estrada-Peña, A. & Miró, G. A review of canine babesiosis: the European perspective. Parasit. Vectors 9, 336 (2016).
Article  CAS  Google Scholar 

100.
Randolph, S. E. The shifting landscape of tick-borne zoonoses: tick-borne encephalitis and Lyme borreliosis in Europe. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 356, 1045–1056 (2001).
CAS  PubMed  PubMed Central  Google Scholar 

101.
Sumilo, D. et al. Tick-borne encephalitis in the Baltic States : Identifying risk factors in space and time. Int. J. Med. Microbiol. 296(Suppl), 76–79 (2006).
PubMed  Google Scholar 

102.
Sumilo, D. et al. Socio-economic factors in the differential upsurge of tick-borne encephalitis in Central and Eastern Europe. Rev. Med. Virol. 18, 81–95 (2008).
PubMed  Google Scholar 

103.
Randolph, S. E., Green, R. M., Peacey, M. F. & Rogers, D. J. Seasonal synchrony : the key to tick-borne encephalitis foci identified by satellite data. Parasitology 121, 15–23 (2000).
PubMed  Google Scholar 

104.
Halos, L. et al. Ecological factors characterizing the prevalence of bacterial tick-borne pathogens in Ixodes ricinus ticks in pastures and woodlands. Appl. Environ. Microbiol. 76, 4413–4420 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

105.
Sjörs, H. Nordisk växtgeografi (in Swedish) (Bonniers, Scandinavian University Books, 1967).
Google Scholar  More

1.
Cavicchioli, R., Amils, R., Wagner, D. & McGenity, T. Life and applications of extremophiles. Environ. Microbiol. 13, 1903–1907 (2011).
Article  Google Scholar 
2.
Riesch, R., Tobler, M. & Plath, M. Extremophile Fishes (Springer, New York, 2015).
Google Scholar 

3.
Wharton, D. A. Life at the Limits: Organisms in Extreme Environments (Cambridge University Press, Cambridge, 2007).
Google Scholar 

4.
Lear, K. O. et al. Divergent field metabolic rates highlight the challenges of increasing temperatures and energy limitation in aquatic ectotherms. Oecologia 193, 311–323 (2020).
ADS  Article  Google Scholar 

5.
Elliott, K. H. et al. High flight costs, but low dive costs, in auks support the biomechanical hypothesis for flightlessness in penguins. Proc. Natl. Acad. Sci. 110, 9380–9384 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

6.
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).
Article  Google Scholar 

7.
Clarke, A. & Johnston, N. M. Scaling of metabolic rate with body mass and temperature in teleost fish. J. Anim. Ecol. 68, 893–905 (1999).
Article  Google Scholar 

8.
Schulte, P. M. The effects of temperature on aerobic metabolism: towards a mechanistic understanding of the responses of ectotherms to a changing environment. J. Exp. Biol. 218, 1856–1866 (2015).
Article  Google Scholar 

9.
Kleiber, M. Body size and metabolism. ENE 1, 315–353 (1932).
Google Scholar 

10.
Glazier, D. S. A unifying explanation for diverse metabolic scaling in animals and plants. Biol. Rev. 85, 111–138 (2010).
Article  PubMed  Google Scholar 

11.
Jerde, C. L. et al. Strong evidence for an intraspecific metabolic scaling coefficient near 0.89 in fish. Front. Physiol. 10, 1166 (2019).
Article  PubMed  PubMed Central  Google Scholar 

12.
van der Meer, J. Metabolic theories in ecology. Trends Ecol. Evol. 21, 136–140 (2006).
Article  PubMed  Google Scholar 

13.
Luongo, S. M. & Lowe, C. G. Seasonally acclimated metabolic Q10 of the California horn shark, Heterodontus francisci. J. Exp. Mar. Bio. Ecol. 503, 129–135 (2018).
Article  Google Scholar 

14.
White, C. R., Alton, L. A. & Frappell, P. B. Metabolic cold adaptation in fishes occurs at the level of whole animal, mitochondria and enzyme. Proc. R. Soc. B Biol. Sci. 279, 1740–1747 (2011).
Article  CAS  Google Scholar 

15.
Krogh, A. The Quantitative Relation Between Temperature and Standard Metabolism in Animals (Internationale Zeitschrift fuÈr Physikalisch-Chemische Biologie, New York, 1914).
Google Scholar 

16.
Messamah, B., Kellermann, V., Malte, H., Loeschcke, V. & Overgaard, J. Metabolic cold adaptation contributes little to the interspecific variation in metabolic rates of 65 species of Drosophilidae. J. Insect Physiol. 98, 309–316 (2017).
CAS  Article  PubMed  Google Scholar 

17.
Holeton, G. F. Metabolic cold adaptation of polar fish: fact or artefact?. Physiol. Zool. 47, 137–152 (1974).
Article  Google Scholar 

18.
Steffensen, J. F. Metabolic cold adaptation of polar fish based on measurements of aerobic oxygen consumption: fact or artefact? Artefact!. Comp. Biochem. Physiol. A. 132, 789–795 (2002).
Article  Google Scholar 

19.
Peck, L. S. A cold limit to adaptation in the sea. Trends Ecol. Evol. 31, 13–26 (2016).
Article  PubMed  Google Scholar 

20.
Chabot, D., Steffensen, J. F. & Farrell, A. P. The determination of standard metabolic rate in fishes. J. Fish Biol. 88, 81–121 (2016).
CAS  Article  PubMed  Google Scholar 

21.
Lawson, C. L. et al. Powering ocean giants : the energetics of shark and ray megafauna. Trends Ecol. Evol. 34, 1–13 (2019).
MathSciNet  Article  Google Scholar 

22.
Lowe, C. Metabolic rates of juvenile scalloped hammerhead sharks (Sphyrna lewini). Mar. Biol. 139, 447–453 (2001).
Article  Google Scholar 

23.
Payne, N. L. et al. A new method for resolving uncertainty of energy requirements in large water breathers: the ‘mega-flume’ seagoing swim-tunnel respirometer. Methods Ecol. Evol. 6, 668–677 (2015).
Article  Google Scholar 

24.
Byrnes, E. E., Lear, K. O., Morgan, D. L. & Gleiss, A. C. Respirometer in a box: development and use of a portable field respirometer for estimating oxygen consumption of large-bodied fishes. J. Fish Biol. 96, 1045–1050 (2020).
CAS  Article  PubMed  Google Scholar 

25.
MacNeil, M. A. et al. Biology of the greenland shark Somniosus microcephalus. J. Fish Biol. 80, 991–1018 (2012).
CAS  Article  PubMed  Google Scholar 

26.
Edwards, J. E. et al. Advancing research for the management of long-lived species: a case study on the Greenland shark. Front. Mar. Sci. 6, 12 (2019).
Article  Google Scholar 

27.
Augustine, S., Lika, K. & Kooijman, S. A. L. M. Comment on the ecophysiology of the Greenland shark, Somniosus microcephalus. Polar Biol. 40, 2429–2433 (2017).
Article  Google Scholar 

28.
Nielsen, J. et al. Eye lens radiocarbon reveals centuries of longevity in the Greenland shark (Somniosus microcephalus). Science 353, 702–704 (2016).
ADS  CAS  Article  PubMed  Google Scholar 

29.
Watanabe, Y. Y., Lydersen, C., Fisk, A. T. & Kovacs, K. M. The slowest fish: Swim speed and tail-beat frequency of Greenland sharks. J. Exp. Mar. Biol. Ecol. 426–427, 5–11 (2012).
Article  Google Scholar 

30.
Hussey, N. E. et al. Rescaling the trophic structure of marine food webs. Ecol. Lett. 17, 239–250 (2014).
Article  Google Scholar 

31.
Devine, B. M., Wheeland, L. J. & Fisher, J. A. D. First estimates of Greenland shark (Somniosus microcephalus) local abundances in Arctic waters. Sci. Rep. 8, 1–10 (2018).
CAS  Article  Google Scholar 

32.
Wilson, E. E. & Wolkovich, E. M. Scavenging: how carnivores and carrion structure communities. Trends Ecol. Evol. 26, 129–135 (2011).
Article  Google Scholar 

33.
Lear, K. O. et al. Correlations of metabolic rate and body acceleration in three species of coastal sharks under contrasting temperature regimes. J. Exp. Biol. 220, 397–407 (2017).
Article  Google Scholar 

34.
Killen, S. S., Atkinson, D. & Glazier, D. S. The intraspecific scaling of metabolic rate with body mass in fishes depends on lifestyle and temperature. Ecol. Lett. 13, 184–193 (2010).
Article  Google Scholar 

35.
Lear, K. O., Whitney, N. M., Brewster, L. R. & Gleiss, A. C. Treading water: respirometer choice may hamper comparative studies of energetics in fishes. Mar. Freshw. Res. 70, 437–448 (2018).
Article  Google Scholar 

36.
Whitney, N. M., Lear, K. O., Gaskins, L. C. & Gleiss, A. C. The effects of temperature and swimming speed on the metabolic rate of the nurse shark (Ginglymostoma cirratum, Bonaterre). J. Exp. Mar. Bio. Ecol. 477, 40–46 (2016).
Article  Google Scholar 

37.
Sims, D. W. The effect of body size on the standard metabolic rate of the lesser spotted dogfish. J. Fish Biol. 48, 542–544 (1996).
Article  Google Scholar 

38.
Semmens, J. M., Payne, N. L., Huveneers, C., Sims, D. W. & Bruce, B. D. Feeding requirements of white sharks may be higher than originally thought. Sci. Rep. 3, 10–13 (2013).
Article  CAS  Google Scholar 

39.
Giacomin, M., Schulte, P. M. & Wood, C. M. Differential effects of temperature on oxygen consumption and branchial fluxes of urea, ammonia, and water in the dogfish shark (Squalus acanthias suckleyi). Physiol. Biochem. Zool. 90, 627–637 (2017).
Article  PubMed  PubMed Central  Google Scholar 

40.
Lowe, C. G. Bioenergetics of free-ranging juvenile scalloped hammerhead sharks (Sphyrna lewini) in Kāne’ohe Bay, Ō’ahu, HI. J. Exp. Mar. Biol. Ecol. 278, 141–156 (2002).
Article  Google Scholar 

41.
Ezcurra, J. M., Lowe, C. G., Mollet, H. F., Ferry, L. A. & O’Sullivan, J. B. Oxygen consumption rate of young-of-the-year white sharks, Carcharodon carcharias during transport to the Monterey Bay Aquarium. Glob. Perspect. Biol. Life Hist. 1, 17–26 (2012).
Article  Google Scholar 

42.
Barnett, A. et al. The utility of bioenergetics modelling in quantifying predation rates of marine apex predators: ecological and fisheries implications. Sci. Rep. 7, 12982 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

43.
Watanabe, Y. Y., Payne, N. L., Semmens, J. M., Fox, A. & Huveneers, C. Swimming strategies and energetics of endothermic white sharks during foraging. J. Exp. Biol. 222, 4 (2019).
Article  Google Scholar 

44.
Secor, S. M. Specific dynamic action: a review of the postprandial metabolic response. J. Comp. Physiol. B 179, 1–56 (2009).
ADS  Article  PubMed  Google Scholar 

45.
Auer, S. K., Dick, C. A., Metcalfe, N. B. & Reznick, D. N. Metabolic rate evolves rapidly and in parallel with the pace of life history. Nat. Commun. 9, 8–13 (2018).
ADS  Article  CAS  Google Scholar 

46.
Drazen, J. C. & Seibel, B. A. Depth-related trends in metabolism of benthic and benthopelagic deep-sea fishes. Limnol. Oceanogr. 52, 2306–2316 (2007).
ADS  CAS  Article  Google Scholar 

47.
Brett, J. R. & Groves, T. D. D. Physiological energetics. Fish Physiol. 8, 280–352 (1979).
Google Scholar 

48.
Widdows, J. Application of calorimetric methods in ecological studies. Therm. Energy. Stud. Cell. Biol. Syst. 1, 182–215 (1987).
Article  Google Scholar 

49.
Armstrong, J. B. & Schindler, D. E. Excess digestive capacity in predators reflects a life of feast and famine. Nature 476, 84–87 (2011).
CAS  Article  PubMed  Google Scholar 

50.
Stirling, I. & McEwan, E. Caloric value of whole ringed seals (Phoca hispida) in relation to Polar Bear (Ursus maritimus) ecology and hunting behavior. Can. J. Zool. 53, 1021–1027 (1975).
CAS  Article  PubMed  Google Scholar 

51.
Furey, N. B., Hinch, S. G., Mesa, M. G. & Beauchamp, D. A. Piscivorous fish exhibit temperature-influenced binge feeding during an annual prey pulse. J. Anim. Ecol. 85, 1307–1317 (2016).
Article  PubMed  Google Scholar 

52.
Svendsen, M. B. S., Bushnell, P. G. & Steffensen, J. F. Design and setup of intermittent-flow respirometry system for aquatic organisms. J. Fish Biol. 88, 26–50 (2016).
CAS  Article  PubMed  Google Scholar 

53.
Clark, T. D., Sandblom, E. & Jutfelt, F. Aerobic scope measurements of fishes in an era of climate change: respirometry, relevance and recommendations. J. Exp. Biol. 216, 2771–2782 (2013).
Article  PubMed  Google Scholar 

54.
Leclerc, L.-M.E. et al. A missing piece in the Arctic food web puzzle? Stomach contents of Greenland sharks sampled in Svalbard, Norway. Polar Biol. 35, 1197–1208 (2012).
Article  Google Scholar  More