Advertisement

From Cohort Data to Life Table Parameters Via Stochastic Modeling

  • Moshe Braner
  • Nelson G. HairstonJr.
Part of the Lecture Notes in Statistics book series (LNS, volume 55)

Abstract

We develop a new method through which parameters such as the duration of stages and the mortality rates within them can be deduced from data on abundance of the stages over time in one cohort of individuals. This method involves modeling of the development process, modeling of the sampling process with its inherent errors, a statistical approach based on the models, and a numerical algorithm designed to perform the statistical estimation on real (noisy) data. We discuss the meaning of development time in the face of mortality, and illustrate the use and the validity of the method with real data from cohorts where the development times were independently measured in situ.

Keywords

Development Time Cohort Analysis Life Table Parameter Standard Normal Cumulative Distribution Function Cohort Development 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aksnes & Høisaeter. 1987. Obtaining life table data from stage—frequency distributional statistics. Limnol Oceanogr. 32: 514–517.CrossRefGoogle Scholar
  2. Ashford, J. R., K. L. Q. Read, & G. G. Vickers. 1970. A system of stochastic models applicable to studies of animal population dynamics. J. Anim. Ecol39: 29–50.CrossRefGoogle Scholar
  3. Braner, M. 1988. Dormancy, dispersal and staged development: Ecological and evolutionary aspects of structured populations in random environments. Ph.D. Thesis, Cornell University.Google Scholar
  4. Geiling, W. T. & R. S. Campbell. 1972. The effect of temperature on the development rate of the major life stages of Diaptomus pallidusHerrick. Limnol Oceanogr. 17: 304–306.CrossRefGoogle Scholar
  5. Hairston, N. G., Jr. W. E. Walton, & K. T. Li. 1983. The causes and consequences of sex—specific mortality in a freshwater copepod. Limnol Oceanogr. 28: 935–947.CrossRefGoogle Scholar
  6. Hairston, N. G., Jr. & S. Twombly. 1985. Obtaining life table data from cohort analyses: A critique of current methods. Limnol Oceanogr. 30: 886–893.CrossRefGoogle Scholar
  7. Hairston, N. G., Jr., M. Braner & S. Twombly. 1987. Perspective on prospective methods for obtaining life table data. Limnol Oceanogr. 32: 517–520.CrossRefGoogle Scholar
  8. Kempton, R. A. 1979. Statistical analysis of frequency data obtained from sampling an insect population grouped by stages. Pp. 401–418 in Statistical Distributions in Ecological Work, J. K. Ord et al., (eds.), International Cooperative Publishing House, Fairland, Maryland.Google Scholar
  9. Landry, M. R. 1975. The relationship between temperature and the development of life stages of the marine copepod Acartia clausiGiesbr. Limnol Oceanogr. 20: 854–857.CrossRefGoogle Scholar
  10. Manly, B. F. J. 1974a. Estimation of stage-specific survival rates and other parameters for insect populations developing through several stages. Oecologia15: 277–285.CrossRefGoogle Scholar
  11. Manly, B. F. J. 1974b. A comparison of methods for the analysis of insect stage—frequency data. Oecologia17: 335–348.CrossRefGoogle Scholar
  12. Manly, B. F. J. 1976. Extensions to Kiritani and Nakasuji’s method for analyzing insect stage-frequency data. Res. Pop. Ecol17: 191–199.CrossRefGoogle Scholar
  13. Manly, B. F. J. 1977. A further note on Kiritani and Nakasuji’s model for stage-frequency data including comments on the use of Tukey’s Jackknife technique for estimating variance. Res. Pop. Ecol18: 177–186.CrossRefGoogle Scholar
  14. Manly, B. F. J. 1985. Further improvements to a method for analyzing stage-frequency data. Res. Pop. Ecol27: 325–332.CrossRefGoogle Scholar
  15. Mills, N. J. 1981. The estimation of mean duration from stage frequency data. Oecologia51: 206–211.CrossRefGoogle Scholar
  16. Mood, A. M., F. A. Graybill, & D. C. Boes. 1974. Introduction to the Theory of Statistics, third edition. McGraw—Hill, New York. 564 pp.MATHGoogle Scholar
  17. Press, W. H., B. P. Flannery, S. A. Teukolski, & W. T. Vetterling. 1986. Numerical Recipes: the Art of Scientific Computing. Cambridge University Press. 818 pp.Google Scholar
  18. Itead, K. L. Q. & J. R. Ashford. 1968. A system of models for the life cycle of a biological organism. Biometrika55: 211–221.MathSciNetCrossRefGoogle Scholar
  19. Rigler, F. H. & J. M. Cooley. 1974. The use of field data to derive population statistics of multivoltine copepods. Limnol Oceanogr. 19: 636–655.CrossRefGoogle Scholar
  20. Woodroofe, M. 1975. Probability with Applications. McGraw—Hill. 372 pp.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1989

Authors and Affiliations

  • Moshe Braner
    • 1
  • Nelson G. HairstonJr.
    • 1
  1. 1.Section of Ecology and SystematicsCornell UniversityIthacaUSA

Personalised recommendations