Metabolic costs of spontaneous swimming in Sprattus sprattus L., at different water temperatures

English version

Autoři: Laura Meskendahl ^aff001; René Pascal Fontes ^aff002; Jens-Peter Herrmann ^aff001; Axel Temming ^aff001
Působiště autorů: Institute for Marine Ecosystem- and Fisheries Science, University of Hamburg, Olbersweg, Hamburg, Germany ^aff001; Reederei Laeisz GmbH, Rostock, Germany ^aff002
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0225568

Souhrn

Oxygen uptake (MO₂; mgO₂ fish^-1h^-1) of fish groups was measured at temperatures between 10–19°C in an intermittent-flow respirometer to quantify the metabolic costs of spontaneous swimming patterns in the small clupeid Sprattus sprattus. Movements of individual fish within the school were tracked automatically during respirometry. Oxygen uptake was then related to mean swimming speeds and the number of sharp turns (>90°), which are common behavioural elements of spontaneous swimming in clupeid fish. Different possible model formulations for describing the relationship between respiration and swimming patterns were compared via the AIC. The final model revealed that costs for sharp turns at a frequency of 1 s^-1 doubled the metabolic costs compared to those with zero turns but with likewise a moderate swimming speed of 0.28 body length ^-1. The cost for swimming doubled if the swimming speed was doubled from 0.28 to 0.56 BLs^-1 but increased by a factor of 4.5 if tripled to 0.84 BLs^-1. Costs for transport were minimal at a speed of 0.4 body lengths s^-1 at all temperatures. New basic input parameters to estimate energy losses during spontaneous movements, which occur typically during foraging in this small pelagic fish, are provided.

Klíčová slova:

Animal behavior – Bioenergetics – Fish – Oxygen – Oxygen metabolism – Predation – Swimming – Respirometry

Zdroje

1. Kitchell JF, Stewart DJ, Weininger D. Applications of a Bioenergetics Model to Yellow Perch (Perca flavescens) and Walleye (Stizostedion vitreum vitreum). J Fish Res Board Canada. 1977;34: 1922–1935. doi: 10.1139/f77-258

2. Hewett SW, Johnson BL. Fish bioenergetics model 2, An upgrade of: A generalized bioenergetics model of fish growth for microcomputers. Technical report. University of Wisconsin Sea Grant Institute; 1992.

3. Winberg G. Rate of metabolism and food requirements for fish. Fish Res Board Can Transl Ser. Fisheries Research Board of Canada, Biological Station; 1960;194: 202pp.

4. Koch BYF, Wieser W. Partitioning of energy in fish: can reduction of swimming activity compensate for the cost of production? J Exp Biol. 1983;107: 141–146.

5. Boisclair D, Leggett WC. The Importance of Activity in Bioenergetics Models Applied to Actively Foraging Fishes. Can J Fish Aquat Sci. 1989;46: 1859–1867. doi: 10.1139/f89-234

6. Tang M, Boisclair D, Ménard C, Downing JA. Influence of body weight, swimming characteristics, and water temperature on the cost of swimming in brook trout (Salvelinus fontinalis). Can J Fish Aquat Sci. 2000;57: 1482–1488. doi: 10.1139/f00-080

7. Brachvogel R, Meskendahl L, Herrmann JP, Temming A. Functional responses of juvenile herring and sprat in relation to different prey types. Mar Biol. 2013;160: 465–478. doi: 10.1007/s00227-012-2104-5

8. Meskendahl L. Metabolic rates and feeding behaviour of sprat, Sprattus sprattus L. [Internet]. University of Hamburg. 2013. Available: http://d-nb.info/1032990570/34

9. Videler JJ. Fish swimming. London: Chapman & Hall; 1993. doi: 10.1007/978-94-011-1580-3

10. Boisclair D, Tang M. Empirical analysis of the influence of swimming pattern on the net energetic cost of swimming in fishes. J Fish Biol. 1993;42: 169–183. doi: 10.1111/j.1095-8649.1993.tb00319.x

11. Webb PW. Composition and Mechanics of Routine Swimming of Rainbow Trout, Oncorhynchus mykiss. Can J Fish Aquat Sci. 1991;48: 583–590. doi: 10.1139/f91-074

12. Enders EC, Herrmann JP. Energy costs of spontaneous activity in horse mackerel quantified by a computerised imaging analysis. Arch Fish Mar Res. 2003;50: 205–219.

13. Steinhausen MF, Steffensen JF, Andersen NG. The effects of swimming pattern on the energy use of gilthead seabream (Sparus aurata L.). Mar Freshw Behav Physiol. 2010;43: 227–241. doi: 10.1080/10236244.2010.501135

14. Killen SS. Food acquisition and digestion | Energetics of Foraging Decisions and Prey Handling. Encyclopedia of Fish Physiology: From genome to environment. 3rd ed. San Diego: Academic Press; 2011. pp. 1588–1595. doi: 10.1016/B978-0-12-374553-8.00145–3

15. Cardinale M, Arrhenius F. Decreasing weight-at-age of Atlantic herring (Clupea harengus) from the Baltic Sea between 1986 and 1996: A statistical analysis. ICES J Mar Sci. 2000;57: 882–893. doi: 10.1006/jmsc.2000.0575

16. Köster FW, Möllmann C. Trophodynamic control by clupeid predators on recruitment success in Baltic cod? ICES J Mar Sci. 2000;57: 310–323. doi: 10.1006/jmsc.1999.0528

17. Bernreuther M. Investigations on the feeding ecology of Baltic Sea herring (Clupea harengus L.) and sprat (Sprattus sprattus L.) [Internet]. University of Hamburg. 2007. Available: https://www.deutsche-digitale-bibliothek.de/binary/HVK3GSJCGDEYERR6HZ7F7AKNWWIWLXFP/full/1.pdf

18. Krohn MM, Boisclair D. Use of a Stereo-video System to Estimate the Energy Expenditure of Free-swimming Fish. Can J Fish Aquat Sci. 1994;51: 1119–1127. doi: 10.1139/f94-111

19. Leonard JBK, Norieka JF, Kynard B, McCormick SD. Metabolic rates in an anadromous clupeid, the American shad (Alosa sapidissima). J Comp Physiol—B Biochem Syst Environ Physiol. 1999;169: 287–295. doi: 10.1007/s003600050223

20. Steinhausen MF, Steffensen JF, Andersen NG. Tail beat frequency as a predictor of swimming speed and oxygen consumption of saithe (Pollachius virens) and whiting (Merlangius merlangus) during forced swimming. Mar Biol. 2005;148: 197–204. doi: 10.1007/s00227-005-0055-9

21. Blank JM, Farwell CJ, Morrissette JM, Schallert RJ, Block BA. Influence of Swimming Speed on Metabolic Rates of Juvenile Pacific Bluefin Tuna and Yellowfin Tuna. Physiol Biochem Zool. 2007;80: 167–177. doi: 10.1086/510637 17252513

22. Tudorache C, Jordan AD, Svendsen JC, Domenici P, DeBoeck G, Steffensen JF. Pectoral fin beat frequency predicts oxygen consumption during spontaneous activity in a labriform swimming fish (Embiotoca lateralis). Environ Biol Fishes. 2009;84: 121–127. doi: 10.1007/s10641-008-9395-x

23. Steinhausen MF, Steffensen JF, Andersen NG. The relationship between caudal differential pressure and activity of Atlantic cod: A potential method to predict oxygen consumption of free-swimming fish. J Fish Biol. 2007;71: 957–969. doi: 10.1111/j.1095-8649.2007.01563.x

24. Chabot D, Steffensen JF, Farrell AP. The determination of standard metabolic rate in fishes. J Fish Biol. 2016;88: 81–121. doi: 10.1111/jfb.12845 26768973

25. Steffensen JF. Some errors in respirometry of aquatic breathers: How to avoid and correct for them. Fish Physiol Biochem. 1989;6: 49–59. doi: 10.1007/BF02995809 24226899

26. Schleuter D, Haertel-Borer S, Fischer P, Eckmann R. Respiration Rates of Eurasian Perch Perca fluviatilis and Ruffe: Lower Energy Costs in Groups. Trans Am Fish Soc. 2007;136: 45–55. doi: 10.1577/T06-123.1

27. Meskendahl L, Herrmann JP, Temming A. Effects of temperature and body mass on metabolic rates of sprat, Sprattus sprattus L. Mar Biol. 2010;157: 1917–1927. doi: 10.1007/s00227-010-1461-1

28. Herrmann JP, Enders EC. Effect of body size on the standard metabolism of horse mackerel. J Fish Biol. 2000;57: 746–760. doi: 10.1006/jfbi.2000.1348

29. Delcourt J, Denoël M, Ylieff M, Poncin P. Video multitracking of fish behaviour: A synthesis and future perspectives. Fish Fish. 2013;14: 186–204. doi: 10.1111/j.1467-2979.2012.00462.x

30. Pinkiewicz T, Williams R, Purser J. Application of the particle filter to tracking of fish in aquaculture research. Proceedings—Digital Image Computing: Techniques and Applications, DICTA 2008. 2008. pp. 997–1006. doi: 10.1109/DICTA.2008.28

31. Lochmatter T, Roduit P, Cianci C, Correll N, Jacot J, Martinoli A. SwisTrack—A flexible open source tracking software for multi-agent systems. 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS. 2008. doi: 10.1109/IROS.2008.4650937

32. Kalman RE. A New Approach to Linear Filtering and Prediction Problems. J Basic Eng. 1960;82: 35–45. doi: 10.1115/1.3662552

33. Haralick RM, Sternberg SR, Zhuang X. Image Analysis Using Mathematical Morphology. IEEE Trans Pattern Anal Mach Intell. 1987;PAMI-9: 532–550. doi: 10.1109/TPAMI.1987.4767941 21869411

34. Barriga-Rivera A, Suaning GJ. Digital image processing for visual prosthesis: Filtering implications. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. 2011. pp. 4860–4863. doi: 10.1109/IEMBS.2011.6091204

35. Ohlberger J, Staaks G, Hölker F. Effects of temperature, swimming speed and body mass on standard and active metabolic rate in vendace (Coregonus albula). J Comp Physiol B Biochem Syst Environ Physiol. 2007;177: 905–916. doi: 10.1007/s00360-007-0189-9 17641899

36. Videler JJ, Nolet BA. Costs of swimming measured at optimum speed: Scale effects, differences between swimming styles, taxonomic groups and submerged and surface swimming. Comp Biochem Physiol. 1990;97A: 91–99. doi: 10.1016/0300-9629(90)90155-L

37. Ohlberger J, Staaks G, Van Dijk PLM, Hölker F. Modelling energetic costs of fish swimming. J Exp Zool Part A Comp Exp Biol. 2005;303A: 657–664. doi: 10.1002/jez.a.181 16013050

38. Ohlberger J, Staaks G, Hölker F. Swimming efficiency and the influence of morphology on swimming costs in fishes. J Comp Physiol B Biochem Syst Environ Physiol. 2006;176: 17–25. doi: 10.1007/s00360-005-0024-0 16177894

39. Korsmeyer KE, Steffensen JF, Herskin J. Energetics of median and paired fin swimming, body and caudal fin swimming, and gait transition in parrotfish (Scarus schlegeli) and triggerfish (Rhinecanthus aculeatus). J Exp Biol. 2002;205: 1253–1263. 11948202

40. Papadopoulos A. Hydrodynamics-based functional forms of activity metabolism: A case for the power-law polynomial function in animal swimming energetics. PLoS One. 2009;4: e4852. doi: 10.1371/journal.pone.0004852 19333397

41. Ware DM. Bioenergetics of Pelagic Fish: Theoretical Change in Swimming Speed and Ration with Body Size. J Fish Res Board Canada. 1978;35: 220–228. doi: 10.1139/f78-036

42. Schmidt-Nielsen K. Locomotion: Energy cost of swimming, flying, and running. Science. 1972. pp. 222–228. doi: 10.1126/science.177.4045.222 4557340

43. R Development Core Team R. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing [Internet]. R Foundation for Statistical Computing. Vienna; 2011. doi: 10.1016/S1015-9584(10)60025-5

44. Burnham KP, Anderson DR. Multimodel inference: understanding AIC and BIC in model selection. Sociol Methods Res. Sage Publications Sage CA: Thousand Oaks, CA; 2004;33: 261–304.

45. Burnham KP, Anderson DR, Huyvaert KP. AIC model selection and multimodel inference in behavioral ecology: Some background, observations, and comparisons. Behav Ecol Sociobiol. 2011;65. doi: 10.1007/s00265-010-1029-6

46. Mazerolle MJ. Improving data analysis in herpetology: Using Akaike’s information criterion (AIC) to assess the strength of biological hypotheses. Amphib Reptil. 2006;27: 169–180. doi: 10.1163/156853806777239922

47. Ritz C, Streibig JC. Nonlinear Regresion with R. Springer. 2008. doi: 10.1017/CBO9781107415324.004

48. Ware DM. Growth, Metabolism, and Optimal Swimming Speed of a Pelagic Fish. J Fish Res Board Canada. 1975;32: 33–41. doi: 10.1139/f75-005

49. Hein AM, Keirsted KJ. The rising cost of warming waters: Effects of temperature on the cost of swimming in fishes. Biol Lett. 2012;8: 266–269. doi: 10.1098/rsbl.2011.0885 22031723

50. Bonga SEW. The stress response in fish. Physiol Rev. 1997;77: 591–637. doi: 10.1152/physrev.1997.77.3.591 9234959

51. Davis LE, Schreck CB. The Energetic Response to Handling Stress in Juvenile Coho Salmon. Trans Am Fish Soc. 1997;126: 248–258. doi: 10.1577/1548-8659(1997)126<0248:TERTHS>2.3.CO;2

52. Marras S, Killen SS, Lindström J, McKenzie DJ, Steffensen JF, Domenici P. Fish swimming in schools save energy regardless of their spatial position. Behav Ecol Sociobiol. 2015;69: 219–226. doi: 10.1007/s00265-014-1834-4 25620833

53. Nilsson LAF, Thygesen UH, Lundgren B, Nielsen BF, Nielsen JR, Beyer JE. Vertical migration and dispersion of sprat (Sprattus sprattus) and herring (Clupea harengus) schools at dusk in the Baltic Sea. Aquat Living Resour. 2003;16: 324–371. doi: 10.1016/S0990-7440(03)00039-1

54. Colby PJ. Response of the alewives, Alosa pseudoharengus, to environmental change. In: Chavin W, Thomas C., editors. Responses of fish to environmental changes. Springfield, IL: Charles C. Thomas; 1973. pp. 163–198. Available: http://pubs.er.usgs.gov/publication/81415

55. Schaefer KM. Lethal Temperatures and the Effect of Temperature Change on Volitional Swimming Speeds of Chub Mackerel, Scomber japonicus. Copeia. 1986;1: 39–44. doi: 10.2307/1444885

56. Klumb RA, Rudstam LG, Mills EL. Comparison of Alewife Young-of-the-Year and Adult Respiration and Swimming Speed Bioenergetics Model Parameters: Implications of Extrapolation. Trans Am Fish Soc. 2004;132: 1089–1103. doi: 10.1577/t03-038

57. Stewart DJ, Binkowski FP. Dynamics of Consumption and Food Conversion by Lake Michigan Alewives: An Energetics-Modeling Synthesis. Trans Am Fish Soc. 1986;115: 643–661. doi: 10.1577/1548-8659(1986)115

58. Rudstam LG. Exploring the dynamics of herring consumption in the Baltic: applications of an energetic model of fish growth. Kieler Meeresforsch Sonderh. 1988;6: 312–322.

59. Claireaux G, Couturier C, Groison A-L. Effect of temperature on maximum swimming speed and cost of transport in. J Exp Biol. 2006;209: 3420–3428. doi: 10.1242/jeb.02346 16916977

60. Whitney NM, Lear KO, Gaskins LC, Gleiss AC. The effects of temperature and swimming speed on the metabolic rate of the nurse shark (Ginglymostoma cirratum, Bonaterre). J Exp Mar Bio Ecol. 2016;477: 40–46. doi: 10.1016/j.jembe.2015.12.009

61. Johnston IA, Temple GK. Thermal plasticity of skeletal muscle phenotype in ectothermic vertebrates and its significance for locomotory behaviour. J Exp Biol. 2002;205: 2305–2322. temperature activite physiologie comportement muscle nage adaptation evolution plasticite caracteristiques morphologiques 12110664

62. Sirois P, Boisclair D. The influence of prey biomass on activity and consumption rates of brook trout. J Fish Biol. 1995;46: 787–805. doi: 10.1111/j.1095-8649.1995.tb01602.x

63. Speers-Roesch B, Norin T, Driedzic WR. The benefit of being still: energy savings during winter dormancy in fish come from inactivity and the cold, not from metabolic rate depression. Proc. R. Soc. B 2018; 285: 20181593. doi: 10.1098/rspb.2018.1593 30185640

64. Zimmermann C, Kunzmann A. Baseline respiration and spontaneous activity of sluggish marine tropical fish of the family Scorpaenidae.Mar. Ecol. Prog Ser. 2001; 219, 229–239.

65. Blake RW. Functional design and burst-and-coast swimming in fishes. Can J Zool. 1983;61: 2491–2494. Available: www.nrcresearchpress.com

66. Blake RW. On the efficiency of energy transformations in cells and animals. In: Blake RW, editor. Efficiency and economy in animal physiology. Cambridge: Cambridge University Press; 1992. pp. 13–32. doi: 10.1017/CBO9780511565588.003

67. Boggs CH. Bioenergetics and growth of Northern Anchovy Engraulis mordax. Fish Bull. 1991;89: 555–566.

68. Van Der Lingen CD. Respiration rate of adult pilchard Sardinops sagax in relation to temperature, voluntary swimming speed and feeding behaviour. Mar Ecol Prog Ser. 1995;129: 41–54. doi: 10.3354/meps129041

69. Macy WK, Durbin AG, Durbin EG. Metabolic rate in relation to temperature and swimming speed, and the cost of filter feeding in Atlantic menhaden, Brevoortia tyrannus. Fish Bull. 1999;97: 282–293.

Metabolic costs of spontaneous swimming in Sprattus sprattus L., at different water temperatures

Souhrn

Klíčová slova:

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