Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-21T23:28:13.370Z Has data issue: false hasContentIssue false
  • English
  • Français

AN ALGORITHM FOR TEMPERATURE-DEPENDENT GROWTH RATE SIMULATION12

Published online by Cambridge University Press:  31 May 2012

R. E. Stinner ,
A. P. Gutierrez  and
G. D. Butler Jr.
R. E. Stinner
Affiliation:
Department of Entomology, North Carolina State University, Raleigh
A. P. Gutierrez
Affiliation:
Division of Biological Control, University of California, Berkeley
G. D. Butler Jr.
Affiliation:
Western Cotton Research Laboratory, U.S. Department of Agriculture, Phoenix, Arizona
Get access Rights & Permissions [Opens in a new window]

Abstract

With the current advances in insect population modelling, the need for more accurate simulation of temperature-dependent growth rates has become vital. The day-degree concept, with its linear temperature–rate relationship, has not been adequate for simulation of field populations under highly variable temperature conditions. Similarly, several of the non-linear relationships proposed in the past (Janisch’s catenary, parabola) have also been inadequate. All of these relationships produce large errors at temperature extremes.

This paper presents a comparison of various functions which have been used for developmental time estimation and an algorithm for a sigmoid function which can be used in simulations having either a calendar or a physiological time base. Validation of the algorithm is presented for three insect species.

Type
Articles
Information
The Canadian Entomologist , Volume 106 , Issue 5 , May 1974 , pp. 519 - 524
Copyright
Copyright © Entomological Society of Canada 1974

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Blunck, H. 1914. Die Entwicklung des Dytiscus marginalis L., vom Ei bis zur Imago. Z. wiss. Zool. 111: 76–151.Google Scholar
Conway, G. R., Glass, N. R., and Wilcox, J. C.. 1970. Fitting nonlinear models to biological data by Marquardt's algorithm. Ecology 51: 503–507.CrossRefGoogle Scholar
Crozier, W. J. 1926. On curves of growth, especially in relation to temperature. J. gen. Physiol. 10: 53–73.CrossRefGoogle ScholarPubMed
Eubank, W. P. et al. 1973. The significance and thermodynamics of fluctuating versus statis thermal environment on Heliothis zea egg development rates. Environ. Ent. 2: 491–496.CrossRefGoogle Scholar
Janisch, E. 1925. Uber die Temperaturabhangigbeit biologischer Vorgange und ihre kurvenmassige Analyse. Arch. ges. Physiol. 209: 414–436.CrossRefGoogle Scholar
Krogh, A. 1914. On the influence of temperature on the rate of embryonic development. Z. allg. Physiol. 16: 163–177.Google Scholar
Luckmann, W. H. 1963. Measurements of the incubation period of corn earworm eggs. J. econ. Ent. 56: 60–62.CrossRefGoogle Scholar
Marquardt, D. W. 1963. An algorithm for least-squares estimation of non-linear parameters. J. Soc. Lud. app. Math 11: 431–441.CrossRefGoogle Scholar
Marquardt, D. W. 1966. Least squares estimation of nonlinear parameters. IBM SHARE Library Distribution No. 309401, NLIN 2 August 1966.Google Scholar
Messenger, P. S. 1964. The influence of rhythmically fluctuating temperatures on the development and reproduction of the spotted alfalfa aphid, Therioaphis maculata. J. econ. Ent. 57: 71–76.CrossRefGoogle Scholar
Messenger, P. S. and Flitters, N. E.. 1958. Effect of constant temperature environments on the egg stage of three species of Hawaiian fruit flies. Ann. ent. Soc. Am. 51: 109–119.CrossRefGoogle Scholar
Messenger, P. S. and Flitters, N. E.. 1959. Effect of variable temperature environments on egg development of three species of fruit flies. Ann. ent. Soc. Am. 52: 191–204.CrossRefGoogle Scholar
Robertson, T. B. 1923. The chemical basis of growth and senescence. Lippincott, Philadelphia. 389 pp.Google Scholar
Sanderson, E. D. 1910. The relation of temperature to the growth of insects. J. econ. Ent. 3: 113–140.CrossRefGoogle Scholar