Animal Life Cycle Models (Poikilotherms)

  • Jacques RégnièreEmail author
  • James A. Powell


This chapter discusses the theoretical basis and application of phenology models for poikilothermic animals, with a particular emphasis on insects. Realistic and accurate models make use of the non-linear, unimodal nature of physiological responses to temperature, using the rate-summation paradigm. In addition, the intrinsic (genetic) variation of developmental rates within populations is described and used to generate simulations where life-cycle events are distributed over time among individuals rather than occurring simultaneously within populations. The usefulness of circle maps to understand the impact of climate on poikilotherm life cycles is illustrated. The application of phenology models at landscape scale, and their use in the study of the impacts of climate and climate change on the distribution of poikilotherms are illustrated with two examples.


Life Stage Gypsy Moth Developmental Rate Canadian Regional Climate Model Phenology Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Allen JC, Foltz JL, Dixon WN, Liebhold AM, Colbert JJ, Régnière J, Gray DR, Wilder JW, Christie I (1993) Will the gypsy moth become a pest in Florida? Fla Entomol 76:102–113CrossRefGoogle Scholar
  2. Angilletta MJ, Niewiarowski PH, Navas CA (2002) The evolution of thermal physiology in ectotherms. J Therm Biol 27:249–268CrossRefGoogle Scholar
  3. Arrhenius S (1889) Uber die reaktionsgeschwindigkeit bei der inversion von rohrzucker durcj sauren. Zeitschrift for Physik Chemique 4:226–248Google Scholar
  4. Bentz BJ, Régnière J, Fettig CJ, Hansen EM, Hayes JL, Hicke JA, Kelsey RG, Negrón JF, Seybold SJ (2010) Climate change and bark beetles of the western United States and Canada: direct and indirect effects. Bioscience 60:602–613CrossRefGoogle Scholar
  5. Bolstad PV, Swift L, Collons F, Régnière J (1998) Measured and predicted air temperatures at basin to regional scales in the southern Appalachian mountains. Agric For Meteorol 91:161–176CrossRefGoogle Scholar
  6. Boutin S, Hébert D (2002) Landscape ecology and forest management: developing an effective partnership. Ecol Appl 12:390–397Google Scholar
  7. Brown JH, Gillooly JF, Allen AP, Savage VM, West GB (2004) Toward a metabolic theory of ecology. Ecology 85:1771–1789CrossRefGoogle Scholar
  8. Chen J, Saunders SC, Crow TR, Naiman RJ, Brosofske KD, Mroz GD, Brookshire BL, Franklin JF (1999) Microclimate in forest ecosystem and landscape ecology: variations in local climate can be used to monitor and compare the effects of different management regimes. Bioscience 49:288–297CrossRefGoogle Scholar
  9. de Jong G, van der Have TM (2009) Temperature dependence of development rate, growth rate and size: from biophysics to adaptation. In: Whitman DW, Ananthakrishnan TN (eds) Phenotypic plasticity of insects: mechanisms and consequences. Science Publishers, EnfieldGoogle Scholar
  10. Dixon AF, Honek GA, Keil P, Kotela MAA, Sizling AL, Jarosik V (2009) Relationship between the minimum and maximum temperature thresholds for development in insects. Funct Ecol 23:257–264CrossRefGoogle Scholar
  11. Eyring H (1935) The activated complex and the absolute rate of chemical reactions. Chem Rev 17:65–77CrossRefGoogle Scholar
  12. Gilbert E, Powell JA, Logan JA, Bentz BJ (2004) Comparison of three models predicting developmental milestones given environmental and individual variation. Bull Math Biol 66:1821–1850CrossRefGoogle Scholar
  13. Grist EPM, Gurney WSC (1995) Stage-specificity and the synchronization of life cycles to periodic environmental variation. J Math Biol 34:123–147CrossRefGoogle Scholar
  14. Haila Y (1995) A conceptual genealogy of fragmentation research: from island biogeography to landscape ecology. Ecol Appl 12:321–334Google Scholar
  15. Hutchinson MF (1995) Stochastic space-time weather models from ground-based data. Agric For Meteorol 73:237–264CrossRefGoogle Scholar
  16. Isaaks EH, Srivastava RM (1989) An introduction to geostatistics. Oxford University Press, New YorkGoogle Scholar
  17. Janisch E (1932) The influence of temperature on the life-history of insects. Trans R Soc Entomol Lond 80:137–168CrossRefGoogle Scholar
  18. Jenkins JL, Powell JA, Logan JA, Bentz BJ (2001) Low seasonal temperatures promote life cycle synchronization. Bull Math Biol 63:573–595CrossRefGoogle Scholar
  19. Knies JI, Kingsolver JG (2010) Erroneous Arrhenius: modified Arrhenius model best explains the temperature dependence of ectotherm fitness. Am Nat 176:227–233CrossRefGoogle Scholar
  20. Liebhold AM, Halverson JA, Elmes GA (1992) Gypsy moth invasion in North America: a quantitative analysis. J Biogeogr 19:513–520CrossRefGoogle Scholar
  21. Logan JA (1988) Toward an expert system for development of pest simulation models. Environ Entomol 17:359–376Google Scholar
  22. Logan JA, Powell JA (2001) Ghost forests, global warming, and the mountain pine beetle. Am Entomol 47:160–173Google Scholar
  23. McGarigal K, Cushman SA (2002) Comparative evaluation of experimental approaches to the study of habitat fragmentation effects. Ecol Appl 12:335–345CrossRefGoogle Scholar
  24. Montgomery ME (1990) Variation in the suitability of tree species for the gypsy moth. In: Gottschalk KW, Tivery MJ, Smith SI (eds) Proceedings U.S. Department of Agriculture Interagency Gypsy Moth Research Review. USDA Forest Service General technical report NE 146Google Scholar
  25. Music B, Caya D (2007) Evaluation of the hydrological cycle over the Mississippi River basin as simulated by the Canadian regional climate model (CRCM). J Hydrometeorol 8:969–988CrossRefGoogle Scholar
  26. Nalder IA, Wein RW (1998) Spatial interpolation of climatic normals: test of a new method in the Canadian boreal forest. Agric For Meteorol 9:211–225CrossRefGoogle Scholar
  27. Nietschke BS, Magarey RD, Bochert DM, Calvin DD, Jones E (2007) A developmental database to support insect phenology models. Crop Prot 26:1444–1448CrossRefGoogle Scholar
  28. Nylin S, Gotthard K (1998) Plasticity in life-history traits. Ann Rev Entomol 63:63–84CrossRefGoogle Scholar
  29. Powell JA, Bentz BJ (2009) Connecting phenological predictions with population growth rates for mountain pine beetle, an outbreak insect. Landsc Ecol 24:657–672CrossRefGoogle Scholar
  30. Powell JA, Logan JA (2005) Insect seasonality: circle map analysis of temperature-driven life cycles. Theor Popul Biol 67:161–179CrossRefGoogle Scholar
  31. Powell JA, Jenkins J, Logan JA, Bentz BJ (2000) Seasonal temperature alone can synchronize life cycles. Bull Math Biol 62:977–998CrossRefGoogle Scholar
  32. Racsko P, Szeidl L, Semonov M (1991) A serial approach to local stochastic weather models. Ecol Model 57:27–41CrossRefGoogle Scholar
  33. Régnière J, Bolstad PV (1994) Statistical simulation of daily air temperature patterns in eastern North America to forecast seasonal events in insect pest management. Environ Entomol 23:1368–1380Google Scholar
  34. Régnière J, Sharov A (1999) Simulating temperature-dependent ecological processes at the sub-continental scale: male gypsy moth flight phenology as an example. Int J Biometeorol 42:146–152CrossRefGoogle Scholar
  35. Régnière J, St-Amant R (2007) Stochastic simulation of daily air temperature and precipitation from monthly normals in North America north of Mexico. Int J Biometeorol 51:415–430CrossRefGoogle Scholar
  36. Régnière J, Nealis VG, Porter K (2009) Climate suitability and management of the gypsy moth invasion into Canada. Biol Invasions 11:135–148CrossRefGoogle Scholar
  37. Régnière J, Powell JA, Bentz BJ, Nealis VG (2012a) Effects of temperature on development, survival and reproduction of insects: experimental design, data analysis and modeling. J Insect Physiol 58:634–647CrossRefGoogle Scholar
  38. Régnière J, St-Amant R, Duval P (2012b) Predicting insect distributions under climate change for physiological responses: Spruce budworm as an example. Biol Invasions 14:1571–1586CrossRefGoogle Scholar
  39. Richardson CW (1981) Stochastic simulation of daily precipitation, temperature and solar radiation. Water Resour Res 17:182–190CrossRefGoogle Scholar
  40. Richardson CW, Wright DA (1984) WGEN: a model for generating daily weather variables. US Department of Agriculture, Washington, DC, Agricultural Research Service 8Google Scholar
  41. Royama T (1984) Population dynamics of the spruce budworm, Choristoneura fumiferana (Clem.). Ecol Monogr 54:429–462CrossRefGoogle Scholar
  42. Russo JM, Liebhold AW, Kelley AGW (1993) Mesoscale weather data as input to a gypsy moth (Lepidoptera: Lymantriidae) phenology model. J Econ Entomol 86:838–844Google Scholar
  43. Ryszkowski L (2001) Landscape ecology in agroecosystems management. Advances in agroecology. CRC Press, Boca RatonCrossRefGoogle Scholar
  44. Safranyik L, Carroll AL, Régnière J, Langor DW, Riel WG, Shore TL, Peter B, Cooke BJ, Nealis V, Taylor SW (2010) Potential for range expansion of mountain pine beetle into the boreal forest of North America. Can Entomol 142:415–442CrossRefGoogle Scholar
  45. Schoolfield RM, Sharpe PJH, Magnuson CE (1981) Non-linear regression of biological temperature-dependent rate models based on absolute reaction-rate theory. J Theor Biol 88:719–731CrossRefGoogle Scholar
  46. Sharov AA, Pijanowski BC, Liebhold AM, Gage SH (1999) What affects the rate of gypsy moth (Lepidoptera: Lymantriidae) spread: winter temperature or forest susceptibility? Agric For Entomol 1:37–45CrossRefGoogle Scholar
  47. Sharpe PJH, DeMichele DW (1977) Reaction kinetics of poikilotherm development. J Theor Biol 64:649–670CrossRefGoogle Scholar
  48. Sharpe PJH, Curry GL, DeMichele DW, Cole CL (1977) Distribution model of organism development times. J Theor Biol 66:21–38CrossRefGoogle Scholar
  49. Wang JY (1960) A critique of the heat unit approach to plant response studies. Ecology 41:785–790CrossRefGoogle Scholar
  50. Whiteman CD (2000) Mountain meteorology: fundamentals and applications. Oxford University Press, New YorkGoogle Scholar
  51. Wilks DS (1999) Simultaneous stochastic simulation of daily precipitation, temperature and solar radiation at multiple sites in complex terrain. Agric For Meteorol 96:85–101CrossRefGoogle Scholar
  52. Wolda H (1988) Insect seasonality: why? Annu Rev Ecol Syst 19:1–18Google Scholar
  53. Worner SP (1992) Performance of phenological models under variable temperature regimes: consequences of the Kaufmann or rate summation effect. Environ Entomol 21:689–699Google Scholar
  54. Yurk BP, Powell JA (2010) Modeling the effects of developmental variation on insect phenology. Bull Math Biol 76:1334–1360CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2013

Authors and Affiliations

  1. 1.Natural Resources CanadaCanadian Forest ServiceQuebec CityCanada
  2. 2.Department of Mathematics and StatisticsUtah State UniversityLoganUSA

Personalised recommendations