Waste to hurry: Dynamic energy budgets explain the need of wasting to fully exploit blooming resources

Oikos

Volumn 122, Issue 3, 2013, Pages 348-357

  Kooijman, S A L M ^a  

^a VU UNIVERSITY AMSTERDAM   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

CARBOHYDRATE; CHEMICAL COMPOSITION; COMPARATIVE STUDY; ENERGY BUDGET; FILTER FEEDING; FOOD AVAILABILITY; LIPID; PLANKTIVORE; PROTEIN;

ANIMALIA; CHORDATA; INVERTEBRATA; THALIA (UROCHORDATA); VERTEBRATA;

EID: 84874330717     PISSN: 00301299     EISSN: 16000706     Source Type: Journal    
DOI: 10.1111/j.1600-0706.2012.00098.x     Document Type: Article

References (65)

1. Augustine S. et al. 2011a. Developmental energetics of zebrafish, Danio rerio. Comp. Physiol. Biochem. A 159: 275-283.
2. Augustine S. et al. 2011b. Stochastic feeding in fish larvae and their metabolic handling of starvation. J. Sea Res. 66: 411-418.
3. Baas J. et al. 2010. A review of DEB-theory in assessing toxic effects of mixtures. Sci. Total Environ. 408: 3740-3745.
4. Brandt B. W. and Kooijman S. A. L. M. 2000. Two parameters account for the flocculated growth of microbes in biodegradation assays. Biotech. Bioeng. 70: 677-684.
5. Brandt B. W. et al. 2003. A general model for multiple substrate biodegradation. application to co-metabolism of non structurally analogous compounds. Water Res. 37: 4843-4854.
6. Capellini I. et al. 2010. Phylogeny and metabolic scaling in mammals. Ecology 91: 2783-2793.
7. Everett J. D. et al. 2011. Three-dimensional structure of a swarm of the salp Thalia democratica within a cold-core eddy off southeast Australia. J. Geophys. Res. 116: C12046. doi:10.1029/2011JC007310.
8. Fablet R. et al. 2011. Shedding light on fish otolith biominer ali sation using a bioenergetic appraoch. PLoS One 6: e27055.
9. Félix A.-A. and Braendle C. 2010. The natural history of Caenorhabditis elegans. Curr. Biol. 20: R965-R969.
10. Glazier D. S. 2005. Beyond the 3/4-power law: variation in the intra- and interspecific scaling of metabolic rate in animals. Biol. Rev. 80: 1-52.
11. Glazier D. S. 2010. A unifying explanation for diverse metabolic scaling in animals and plants. Biol. Rev. 85: 111-138.
12. Hanegraaf P. P. F. et al. 2000. A mathematical model for yeast respiro-fermentative physiology. Yeast 16: 423-437.
13. Harold F. M. 1986. The vital force. A study of bioenergetics. - Freeman.
14. Jager T. and Klok C. 2010. Extrapolating toxic effects on individuals to the population level; the role of dynamic energy budgets. Phil. Trans. R. Soc. B 365: 3531-3540.
15. Jager T. et al. 2006. Making sense of ecotoxicological test results: towards application of process-based models. Ecotoxicology 15: 305-314.
16. Kolokotrones T. et al. 2010. Curvature in metabolic scaling. Nature 464: 753-756.
17. Kooi B. W. et al. 2004. Consequences of symbiosis for food web dynamics. J. Math. Biol. 3: 227-271.
18. Kooijman S. A. L. M. 1985. Toxicity at population level. - Cairns J. (ed.), Multispecies toxicity testing. Pergamon Press, pp. 143-164.
19. Kooijman S. A. L. M. 1986a. Energy budgets can explain body size relations. J. Theor. Biol. 121: 269-282.
20. Kooijman S. A. L. M. 1986b. Population dynamics on the basis of budgets. - Metz J. A. J. and Diekmann O. (eds), The dynamics of physiologically structured populations. Springer Lecture Notes in Biomathematics. Springer, pp. 266-297.
21. Kooijman S. A. L. M. 1998. The synthesizing unit as model for the stoichiometric fusion and branching of metabolic uxes. Biophys. Chem. 73: 179-188.
22. Kooijman S. A. L. M. 2001. Quantitative aspects of metabolic organization; a discussion of concepts. Phil. Trans. R. Soc. B 356: 331-349.
23. Kooijman S. A. L. M. 2006. Pseudo-faeces production in bivalves. J. Sea Res. 56: 103-106.
24. Kooijman S. A. L. M. 2009. Social interactions can affect feeding behaviour of fish in tanks. J. Sea Res. 62: 175-178.
25. Kooijman S. A. L. M. 2010. Dynamic energy budget theory for metabolic organisation. - Cambridge Univ. Press.
26. Kooijman S. A. L. M. 2012a. Energy budgets. - Hastings A. and Gross L. (eds), Sourcebook in theoretical ecology. Univ. of California Press.
27. Kooijman S. A. L. M. 2012b. Yolky eggs prepare for metabolic acceleration. J. Math. Biol. in press.
28. Kooijman S. A. L. M. and Metz J. A. J. 1983. On the dynamics of chemically stressed populations; the deduction of population consequences from effects on individuals. Ecotox. Environ. Saf. 8: 254-274.
29. Kooijman S. A. L. M. and Hengeveld R. 2005. The symbiontic nature of metabolic evolution. - Reydon T. A. C. and Hemerik L. (eds), Current themes in theoretical biology: a Dutch perspective. Springer, pp. 159-202.
30. Kooijman S. A. L. M. and Segel L. A. 2005. How growth affects the fate of cellular substrates. Bull. Math. Biol. 67: 57-77.
31. Kooijman S. A. L. M. and Troost T. A. 2007. Quantitative steps in the evolution of metabolic organisation as specified by the dynamic energy budget theory. Biol. Rev. 82: 1-30.
32. Kooijman S. A. L. M. et al. 1991. Microbial dynamics on the basis of individual budgets. Antonie van Leeuwenhoek 60: 159-174.
33. Kooijman S. A. L. M. et al. 2003. Quantitative steps in symbiogenesis and the evolution of homeostasis. Biol. Rev. 78: 435-463.
34. Kooijman S. A. L. M. et al. 2004. Dynamic energy budget representations of stoichiometric constraints to population models. Ecology 85: 1230-1243.
35. Kooijman S. A. L. M. et al. 2007. A new class of non-linear stochastic population models with mass conservation. Math Biosci. 210: 378-394.
36. Kooijman S. A. L. M. et al. 2011. Scenarios for acceleration in fish development and the role of metamorphosis. J. Sea Res. 66: 419-423.
37. Leeuwen I. M. M. v. et al. 2002. A mathematical model that accounts for the effects of caloric restriction on body weight and longevity. Biogerontology 3: 373-381.
38. Leeuwen I. M. M. v. et al. 2003. The embedded tumor: host physiology is important for the interpretation of tumor growth. Br. J. Cancer 89: 2254-2263.
39. Lika K. and Kooijman S. A. L. M. 2011. The comparative topology of energy allocation in budget models. J. Sea Res. 66: 381-391.
40. Lika K. et al. 2011a. The 'covariation method' for estimating the parameters of the standard dynamic energy budget model I: philosophy and approach. J. Sea Res. 66: 270-277.
41. Lika K. et al. 2011b. The 'covariation method' for estimating the parameters of the standard dynamic energy budget model II: properties and preliminary patterns. J. Sea Res. 66: 278-288.
42. López-Urrutia A. et al. 2003. Food limitation and growth in temperate epipelagic appendicularians (Tunicata). Mar. Ecol. Prog. Ser. 252: 143-157.
43. Lorena A. et al. 2010. Stylized facts in microalgal growth interpretation in a DEB context. Phil. Trans. R. Soc. B 365: 3509-3521.
44. Muller E. B. et al. 2001. Stoichiometric food quality and herbivore dynamics. Ecol. Lett. 4: 519-529.
45. Muller E. B. et al. 2009. Dynamic energy budgets in syntrophic symbiotic relationships between heterotrophic hosts and photoautotrophic symbionts. J. Theor. Biol. 259: 44-57.
46. Nisbet R. M. et al. 2000. From molecules to ecosystems through dynamic energy budget models. J. Anim. Ecol. 69: 913-926.
47. Pecquerie L. et al. 2009. Modelling fish growth and reproduction in the context of the dynamic energy budget theory to predict environmental impact on anchovy spawning duration. J. Sea Res. 62: 93-105.
48. Pecquerie L. et al. 2010. The impact of metabolism on stable isotope dynamics: a theoretical framework. Phil. Trans. R. Soc. B 365: 3455-3468.
49. Pecquerie L. et al. 2012. Reconstructing individual food and growth histories from calci_ed structures of acquatic organisms. Mar. Ecol. Prog. Ser. 447: 151-164.
50. Qian H. and Beard D. A. 2006. Metabolic futile cycles and their functions: a systems analysis of energy and control. Syst. Biol. (Stevenage) 153: 192-200.
51. Savage V. M. et al. 2004. The predominance of quarter-power scaling in biology. Funct. Ecol. 18: 357-282.
52. Sieg A. E. et al. 2009. Mammalian metabolic allometry: do intraspecific variation, phylogeny, and regression models matter? Am. Nat. 174: 720-733.
53. Sousa T. et al. 2006. The thermodynamics of organisms in the context of DEB theory. Phys. Rev. E 74(051901): 1-15.
54. Sousa T. et al. 2008. From empirical patterns to theory: a formal metabolic theory of life. Phil. Trans. R. Soc. B 363: 2453-2464.
55. Sousa T. et al. 2010. Formalised DEB theory restores coherence in core biology. Phil. Trans. R. Soc. B 365: 3413-3428.
56. Stein R. and Blum J. J. 1978. On the analysis of futile cycles in metabolism. J. Theor. Biol. 72: 487-522.
57. Steinberg D. 1963. Fatty acid metabolization - mechanisms of regulation and metabolic consequences. - Grant J. K. and Popjak G. (eds), The control of lipid metabolism. Academic Press, pp. 111-144.
58. Troost T. A. et al. 2005a. Ecological specialization of mixotrophic plankton in a mixed water column. Am. Nat. 166: E45-E61.
59. Troost T. A. et al. 2005b. When do mixotrophs specialize? Adaptive dynamics theory applied to a dynamic energy budget model. Math. Biosci. 193: 159-182.
60. Van der Meer J. 2006. An introduction to dynamic energy budget (DEB) models with special emphasis on parameter estimation. J. Sea Res. 56: 85-102.
61. Van der Meer J. 2007. Laws on growth and heat. Trends Ecol. Evol. 22: 392-393.
62. Walsberg G. E. and Hoffman T. C. M. 2005. Direct calorimetry reveals large errors in respirometric estimates of energy expen diture. J. Exp. Biol. 208: 1035-1043.
63. White C. R. et al. 2007. Allometric exponents do not support a universal metabolic allometry. Ecology 88: 315-323.
64. White C. et al. 2009. Phylogenetically informed analysis of the allometry of mammalian basal metabolic rate supports neither geometric nor quarter-power scaling. Evolution 63: 2658-2667.
65. White C. R. et al. 2011. A manipulative test of competing theories for metabolic scaling in animals. Am. Nat. 178: 746-754.