Theoretical Ecology

, Volume 11, Issue 1, pp 39–53 | Cite as

Do yearly temperature cycles reduce species richness? Insights from calanoid copepods

ORIGINAL PAPER
  • 50 Downloads

Abstract

The metabolic theory of ecology (MTE) has explained the taxonomic richness of ectothermic species as an inverse function of habitat mean temperature. Extending this theory, we show that yearly temperature cycles reduce metabolic rates of taxa having short generation times. This reduction is due to Jensen’s inequality, which results from a nonlinear dependency of metabolic rate of organisms on temperature. It leads to a prediction that relatively lower species richness is found in habitats with larger amplitudes of yearly temperature cycles where mean temperatures and other conditions are similar. We show that metabolically driven generation time of a taxon also relates functionally to species richness, and similarly, its yearly cycles reduce richness. We test these hypotheses on marine calanoid copepods with 46,377 records of data collected by scientific cruise surveys in Mediterranean regions, across which the temperature amplitudes vary dramatically. We test both bio-energetic and phenomenological effects of temperature cycles on richness in 86 1° × 1° latitudinal and longitudinal spatial units. The models incorporated the effect of both periodic fluctuations and mean temperature explained 21.6% more variation in the data, with lower AIC, compared to models incorporated only the mean temperature. The study also gives insight into the basis of energetic-equivalence rule in MTE determining richness, which can be governed by generation time of taxon. The results of this study lead to the proposition that amplitude of yearly temperature cycles may contribute to both the longitudinal and the latitudinal differences in species richness and show how the metabolic theory can explain macro-ecological patterns arising from yearly temperature cycles.

Keywords

Metabolic theory of ecology Periodic temperature fluctuation Copepods, species diversity Species richness gradient Jensen’s inequality Generation time 

Notes

Acknowledgements

The authors thank the anonymous reviewers of the manuscript greatly. HR gratefully acknowledges the research support provided by the Mathematical Biology Unit at Okinawa Institute of Science and Technology Graduate University (OIST) of Japan. ML thanks NSERC Discovery and Accelerator grants (ML) a Killam Research Fellowship (ML) and a Canada Research Chair. The authors also thank Dr. Steven Aird at OIST for editing the manuscript.

References

  1. Allen AP, Brown JH, Gillooly JF (2002) Global biodiversity, biochemical kinetics, and the energetic-equivalence rule. Science 297(5586):1545–1548CrossRefPubMedGoogle Scholar
  2. Allen AP, Gillooly JF, Savage VM, Brown JH (2006) Kinetic effects of temperature on rates of genetic divergence and speciation. Proc Natl Acad Sci 103(24):9130–9135CrossRefPubMedPubMedCentralGoogle Scholar
  3. Andersen V, Devey C, Gubanova A, Picheral M, Melnikov V, Tsarin S, Prieur L (2004) Vertical distributions of zooplankton across the Almeria–Oran frontal zone (Mediterranean Sea). J Plankton Res 26(3):275–293CrossRefGoogle Scholar
  4. Benyahya L, Caissie D, St-Hilaire A, Ouarda TB, Bobée B (2007) A review of statistical water temperature models. Canadian Water Resour J 32(3):179–192CrossRefGoogle Scholar
  5. Bollens, G. R., and M. R. Landry. 2000. Biological response to iron fertilization in the eastern equatorial Pacific (Iron Ex II). II. Mesozooplankton abundance, biomass, depth distribution and grazing. Mar Ecol Progress Ser 201(43-56)Google Scholar
  6. Bradford-Grieve JM (2002) Colonization of the pelagic realm by calanoid copepods. Hydrobiologia 485(1–3):223–244CrossRefGoogle Scholar
  7. Burgmer T, Hillebrand H (2011) Temperature mean and variance alter phytoplankton biomass and biodiversity in a long-term microcosm experiment. Oikos 120(6):922–933CrossRefGoogle Scholar
  8. Cardillo M, Orme CDL, Owens IPF (2005) Testing for latitudinal bias in diversification rates: an example using new world birds. Ecology 86(9):2278–2287CrossRefGoogle Scholar
  9. Clarke A, Crame JA (2003) The importance of historical processes in global patterns of diversity. In: Blackburn TM, Gaston KJ (eds) Macroecology concepts and consequences. Blackwell Scientific, Oxford, pp 130–151Google Scholar
  10. Colwell RK, Lees DC (2000) The mid-domain effect: geometric constraints on the geography of species richness. Trends in ecology & evolution 15(2):70–6Google Scholar
  11. Currie DJ, Mittelbach GG, Cornell HV, Kaufman DM, Kerr JT, Oberdorff T (2004) Predictions and tests of climate-based hypotheses of broad-scale variation in taxonomic richness. Ecol Lett 7:1121–1134CrossRefGoogle Scholar
  12. Damuth J (1987) Interspecific allometry of population density in mammals and other animals: the independence of body mass and population energy-use. Biol J Linn Soc 31(3):193–246CrossRefGoogle Scholar
  13. Deibel D, Lowen B (2012) A review of the life cycles and life-history adaptations of pelagic tunicates to environmental conditions. ICES J Mar Sci 69(3):358–369CrossRefGoogle Scholar
  14. Dowle EJ, Morgan-Richards M, Trewick SA (2013) Molecular evolution and the latitudinal biodiversity gradient. Heredity 110(6):501Google Scholar
  15. Fox JW (2013) The intermediate disturbance hypothesis should be abandoned. Trends Ecol Evol 28(2):86–92CrossRefPubMedGoogle Scholar
  16. Gillman LN, Keeling DJ, Ross HA, Wright SD. (2009) Latitude, elevation and the tempo of molecular evolution in mammals. Proceedings of the Royal Society of London B: Biological Sciences 276(1671):3353–9Google Scholar
  17. Gillooly JF, Brown JH, West GB, Savage VM, Charnov EL (2001) Effects of size and temperature on metabolic rate. Science 293(5538):2248–2251CrossRefPubMedGoogle Scholar
  18. Hawkins BA, Albuquerque FS, Araujo MB, Beck J, Bini LM, Cabrero-Sanudo FJ et al (2007) A global evaluation of metabolic theory as an explanation for terrestrial species richness gradients. Ecology 88(8):1877–1888CrossRefPubMedGoogle Scholar
  19. Hays GC, Kennedy H, Frost BW (2001) Individual variability in diel vertical migration of a marine copepod: why some individuals remain at depth when others migrate. Limnol Oceanogr:2050–2054Google Scholar
  20. Huntley ME, Lopez MD (1992) Temperature-dependent production of marine copepods: a global synthesis. Am Nat:201–242Google Scholar
  21. IBSS (n.d.). Institute of Biology of the Southern Seas. Retrieved from: http://www.Iobis.Org/, or http://www.Marbef.Org/data/imis.Php?Module=dataset&dasid=1675 in march 2013
  22. Jensen JLWV (1906) Sur les fonctions convexes et les inégalités entre les valeurs moyennes. Acta Mathematica 30(1):175–193CrossRefGoogle Scholar
  23. Kadane JB, Lazar NA (2004) Methods and criteria for model selection. J Am Stat Assoc 99(465):279–290CrossRefGoogle Scholar
  24. Kiørboe T, Sabatini M (1994) Reproductive and life cycle strategies in egg-carrying cyclopoid and free-spawning calanoid copepods. J Plankton Res 16(10):1353–1366CrossRefGoogle Scholar
  25. Lande R, Engen S, Saether BE (2003) Stochastic population dynamics in ecology and conservation. Oxford University Press, Oxford, p 212CrossRefGoogle Scholar
  26. Landry MR (1975) Seasonal temperature effects and predicting development rates of marine copepod eggs. Limnol Oceanography 20(3):434–440CrossRefGoogle Scholar
  27. Louys, J., Wilkinson, D. M., and L. C. Bishop. 2012. Ecology needs a paleontological perspective. In: Paleontology in ecology and conservation. Springer Berlin Heidelberg, p. 23–38Google Scholar
  28. LU (n.d.), Lebanese University Retrieved from: http://www.iobis.org/. in March 2016
  29. Maas AE, Wishner KF, Seibel BA (2012) Metabolic suppression in thecosomatous pteropods as an effect of low temperature and hypoxia in the eastern tropical North Pacific. Mar Biol 159(9):1955–1967CrossRefGoogle Scholar
  30. Mackas DL, Sefton H, Miller CB, Raich A (1993) Vertical habitat partitioning by large calanoid copepods in the oceanic subarctic Pacific during spring. Prog Oceanogr 32(1):259–294CrossRefGoogle Scholar
  31. Mauchline J, Mauchline J (1998) The biology of calanoid copepods. Academic press U.K, p 710Google Scholar
  32. Mayhew PJ, Bell MA, Benton TG, McGowan AJ (2012) Biodiversity tracks temperature over time. Proc Natl Acad Sci 109(38):15141–15145CrossRefPubMedPubMedCentralGoogle Scholar
  33. Moran PAP (1950) A test for serial independence of residuals. Biometrika 37:178–181CrossRefPubMedGoogle Scholar
  34. Nowaczyk A, Carlotti F, Thibault-Botha D, Pagano M (2011) Distribution of epipelagic metazooplankton across the Mediterranean Sea during the summer BOUM cruise. Biogeosciences 8(8):2159CrossRefGoogle Scholar
  35. Oberdoff T, Guégan JF, Hugueny B (1995) Global scale patterns of fish species richness in rivers. Ecography 18(4):345–352CrossRefGoogle Scholar
  36. Patanasatienkul T, Revie CW, Davidson J, Sanchez J (2014) Mathematical model describing the population dynamics of Ciona intestinalis, a biofouling tunicate on mussel farms in Prince Edward Island, Canada. Manag Biol Invasions 5(1):39–54CrossRefGoogle Scholar
  37. Pianka ER (1966) Latitudinal gradients in species diversity: a review of concepts. Am Nat 100(910):33–46CrossRefGoogle Scholar
  38. Price CA, Weitz JS, Savage VM, Stegen J, Clarke A, Coomes DA, Dodds PS, Etienne RS, Kerkhoff AJ, McCulloh K, Niklas KJ (2012) Testing the metabolic theory of ecology. Ecology Letters 15(12):1465–1474Google Scholar
  39. Rajakaruna H, Lewis M (2017) Temperature cycles affect colonization potential of calanoid copepods. J Theor Biol 419:77–89CrossRefPubMedGoogle Scholar
  40. Record NR, Pershing AJ, Maps F (2012) First principles of copepod development help explain global marine diversity patterns. Oecologia 170(2):289–295CrossRefPubMedGoogle Scholar
  41. Renema W, Bellwood DR, Braga JC, Bromfield K, Hall R, Johnson KG, Pandolfi JM (2008) Hopping hotspots: global shifts in marine biodiversity. Science 321(5889):654–657CrossRefPubMedGoogle Scholar
  42. Rohde K (1992) Latitudinal gradients in species diversity: the search for the primary cause. Oikos 65(3):514–527CrossRefGoogle Scholar
  43. Rolland J, Condamine FL, Jiguet F, Morlon H (2014) Faster speciation and reduced extinction in the tropics contribute to the mammalian latitudinal diversity gradient. PLoS Biol 12:e1001775CrossRefPubMedPubMedCentralGoogle Scholar
  44. Rombouts I, Beaugrand G, Ibaňez F, Chiba S, Legendre L (2011) Marine copepod diversity patterns and the metabolic theory of ecology. Oecologia 166(2):349–355CrossRefPubMedGoogle Scholar
  45. Rombouts I, Beaugrand G, Ibaňez F, Gasparini S, Chiba S, Legendre L (2009) Global latitudinal variations in marine copepod diversity and environmental factors. Proc R Soc B Biol Sci 276(1670):3053–3062CrossRefGoogle Scholar
  46. Ruel JJ, Ayres MP (1999) Jensen’s inequality predicts effects of environmental variation. Trends Ecol Evol 14(9):361–366CrossRefPubMedGoogle Scholar
  47. Savage VM (2004) Improved approximations to scaling relationships for species, populations, and ecosystems across latitudinal and elevational gradients. J Theor Biol 227(4):525–534CrossRefPubMedGoogle Scholar
  48. Savage VM, Gillooly JF, Brown JH, West GB, Charnov EL (2004) Effects of body size and temperature on population growth. Am Nat 163(3):429–441CrossRefPubMedGoogle Scholar
  49. Seibel, B. A., Schneider, J. L., Kaartvedt, S., Wishner, K. F., and K. L. Daly. 2016. Hypoxia tolerance and metabolic suppression in oxygen minimum zone Euphausiids: implications for ocean deoxygenation and biogeochemical cycles. Integ Compar BiolGoogle Scholar
  50. Shurin JB, Winder M, Adrian R, Keller WB, Matthews B, Paterson AM, Paterson MJ, Pinel-Alloul B, Rusak JA, Yan ND (2010) Environmental stability and lake zooplankton diversity—contrasting effects of chemical and thermal variability. Ecol Lett 13(4):453–463CrossRefPubMedGoogle Scholar
  51. Sommer U (1995) An experimental test of the intermediate disturbance hypothesis using cultures of marine phytoplankton. Limnol Oceanogr 40:1271–1277CrossRefGoogle Scholar
  52. Spalding MD, Fox HE, Allen GR, Davidson N, Ferdaña ZA, Finlayson MAX, Robertson J (2007) Marine ecoregions of the world: a bioregionalization of coastal and shelf areas. Bioscience 57(7):573–583CrossRefGoogle Scholar
  53. Subramoniam T (2016) Sexual biology and reproduction in crustaceans. Academic Press, p 526Google Scholar
  54. Svetlichny LS, Hubareva ES, Erkan F, Gucu AC (2000) Physiological and behavioral aspects of Calanus euxinus females (Copepoda: Calanoida) during vertical migration across temperature and oxygen gradients. Mar Biol 137(5–6):963–971CrossRefGoogle Scholar
  55. Terborgh J (1973) On the notion of favorableness in plant ecology. Am Nat 107(956):481–501CrossRefGoogle Scholar
  56. Thomas JA, Welch JJ, Lanfear R, Bromham L (2010) A generation time effect on the rate of molecular evolution in invertebrates. Molecular biology and evolution 27(5):1173–80Google Scholar
  57. Townsend CR, Scarsbrook MR, Dolédec S (1997) The intermediate disturbance hypothesis, refugia, and biodiversity in streams. Limnol Oceanogr 42(5):938–949CrossRefGoogle Scholar
  58. Weir JT, Schluter D (2007) The latitudinal gradient in recent speciation and extinction rates of birds and mammals. Science 315(5818):1574–1576CrossRefPubMedGoogle Scholar
  59. Willig MR, Kaufman DM, Stevens RD (2003) Latitudinal gradients of biodiversity: pattern, process, scale, and synthesis. Ann Rev Ecol Evol S 34:273–309CrossRefGoogle Scholar
  60. WOA (n.d.). World Ocean Atlas. NOAA. http://www.nodc.noaa.gov/OC5/SELECT/ woaselect/ woaselect. html
  61. Wright S, Keeling J, Gillman L (2006) The road from Santa Rosalia: a faster tempo of evolution in tropical climates. Proceedings of the National Academy of Sciences 103:7718–7722Google Scholar
  62. Wright SD, Gillman LN, Ross HA, Keeling DJ (2010) Energy and the tempo of evolution in amphibians. Global Ecology and Biogeography 19:733–740Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.Mathematical Biology UnitOkinawa Institute of Science and Technology Graduate UniversityOkinawaJapan
  2. 2.Centre for Mathematical Biology, Department of Biological SciencesUniversity of AlbertaEdmontonCanada
  3. 3.Centre for Mathematical Biology, Department of Mathematics and Statistical SciencesUniversity of AlbertaEdmontonCanada

Personalised recommendations