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Survival analysis approach to insect life table analysis and hypothesis testing: with particular reference to Russian wheat aphid (Diuraphis noxia (Mordvilko)) populations

Published online by Cambridge University Press:  27 November 2009

Z.S. Ma
Z.S. Ma*
Affiliation:
Department of Entomology, University of Idaho, Moscow, ID83844, USA
*
E-mail: ma@vandals.uidaho.edu
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Abstract

The goal of this paper is to examine and demonstrate that survival analysis, which has been a de facto standard in biomedical research since the 1990s but has not been widely adopted in entomology yet, should possess similar potential in entomological research. The following three objectives are set to achieve this goal: (i) addressing a fundamental issue – censoring or incomplete observations; (ii) demonstrating the application of survival analysis to analyze insect life tables; and (iii) applying survival analysis for hypothesis testing. The data used to demonstrate the applications is from our laboratory experiments, which recorded the development, survival and reproduction of 1800 Russian wheat aphids (Diuraphis noxia (Mordvilko), RWA) under 25 treatments of temperature and plant-growth stage. With regard to the first two objectives, besides examining the near ubiquitous existence of censoring in insect population research, we constructed and analyzed life tables of 1800 RWA individuals with survival analysis. We further demonstrate that there could be very significant differences in life table parameters, such as median development times with and without considering censoring. To the best of our knowledge, this is the first recognition in entomology that censoring, which is hardly avoidable, can cause significant systematical bias (ranging between 4–25%; table 1) in insect development data analysis. As for the third objective, the study shows that four statistics from survival analysis can be applied to testing the effects of covariates, such as temperature and plant-growth stage, on development and survival of the Russian wheat aphid. The advantages of survival analysis include the handling of censored observations, survival probabilities in the form of rigorous survivor function vs. simple survival rates, dynamic modeling of covariates effects on development and survival with a unified model structure, etc. The methods demonstrated in this article should also be useful for entomological research beyond insect demography, such as bioassay, assessment of natural enemies, the studies of insect behaviors, etc.

Keywords

insect demographysurvival analysisfailure time analysisinsect developmentlife tablespopulation dynamicsRussian wheat aphid
Type
Research Paper
Information
Bulletin of Entomological Research , Volume 100 , Issue 3 , June 2010 , pp. 315 - 324
Copyright
Copyright © Cambridge University Press 2009

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References

BMDP (1993) BMDP Statistical Software Manuals. Release 7, vol. 1 & 2. BMDP Inc.Google Scholar
Braner, M. & Hairston, N.G. (1989) From cohort data to life table parameters via stochastic modeling. pp. 81–92in McDonald, L.L.Manly, B.F.J., Lockwood, J.A. & Logan, J.A. (Eds) Estimation and Analysis of Insect Populations. Berlin, Germany, Springer-Verlag.Google Scholar
Breslow, N. (1974) A generalized Kruskal-Wallis test for comparing k samples subject to unequal patterns of censorship. Biometrika 57, 579–594.Google Scholar
Carey, J.R. (1993) Applied Demography for Biologists: With Special Emphasis on Insects. 206 pp. Oxford, UK, Oxford University Press.Google Scholar
Carey, J.R. (2001) Insect biodemography. Annual Review of Entomology 46, 79–110.Google Scholar
Chiang, C.L. (1960) A stochastic study of life tables and its applications: I. Probability distribution of the biometric functions. Biometrics 16, 618–635.Google Scholar
Cox, D.R. & Oakes, D. (1984) Analysis of Survival Data. 182 pp. London, UK, Chapman & Hall.Google Scholar
Curry, G.L. & Feldman, R.M. (1987) Mathematical Foundations of Population Dynamics. 246 pp. College Station, Texas, USA, Texas A & M University Press.Google Scholar
Fox, G.A. & Kendall, B.E. (2006) Consequences of heterogeneity in survival probability in a population of Florida scrub-jays. Journal of Animal Ecology 75, 921–927.Google Scholar
Hald, A. (2003) A History of Probability and Statistics and Their Applications before 1750. 586 pp. New York, USA, Wiley-InterScience.Google Scholar
He, F. & Alfaro, R.I. (2000) White pine weevil attack on white spruce: a survival time analysis. Ecological Applications 10(1), 225–232.CrossRefGoogle Scholar
Hougaard, P. (2000) Analysis of Multivariate Survival Data. 560 pp. Berlin, Germany, Springer-Verlag.Google Scholar
Ibrahim, J.G., Chen, M.H. & Sinha, D. (2005) Bayesian Survival Analysis. 481 pp. Berlin, Germany, Springer-Verlag.Google Scholar
Kalbfleisch, J.D. & Prentice, R.L. (2002) The Statistical Analysis of Failure Time Data. 2nd edn.462 pp. New York, USA, Wiley-InterScience.Google Scholar
Kaplan, E.L. & Meier, P. (1958) Nonparametric estimation from incomplete observations. Journal of American Statistical Association 53, 457–481.Google Scholar
Lawless, J.F. (2003) Statistical Models and Methods for Lifetime Data. 2nd edn.630 pp. New York, USA, John Wiley & Sons.Google Scholar
Logan, J.A. (1988) Toward an Expert System for development of pest simulation models. Environmental Entomology 17(2), 359–376.Google Scholar
Ma, Z.S. (1997) Survival analysis and demography of Russian wheat aphid populations. PhD dissertation, University of Idaho, Moscow, Idaho.Google Scholar
Ma, Z.S. & Bechinski, E.J. (2008a) Developmental and phenological modeling of Russian wheat aphid. Annals of Entomological Society of America 101(2), 351–361.CrossRefGoogle Scholar
Ma, Z.S. & Bechinski, E.J. (2008b) A survival analysis based simulation model for Russian wheat aphid. Ecological Modeling 216(2), 323–332CrossRefGoogle Scholar
Ma, Z.S. & Bechinski, E.J. (2009) Accelerated failure time modeling of the development and survival of Russian wheat aphid, Diuraphis noxia (Mordvilko). Population Ecology 51(4), 543–548.Google Scholar
Ma, Z.S. & Krings, A.W. (2008a) Multivariate survival analysis (I): shared frailty approaches to reliability and dependence modeling. 21 pp. in Proceedings of 2008 IEEE-AIAA AeroSpace Conference. March 1–8, 2008, BigSky, Montana, USA.CrossRefGoogle Scholar
Ma, Z.S. & Krings, A.W. (2008b) Multivariate Survival Analysis (II): An Overview of Multi-State Models in Biomedicine and Engineering Reliability. 6 pp. in Proceedings of 2008 IEEE Biomedical Engineering and Informatics, BMEI, 28–30 May 2008, Sanya, China.Google Scholar
Mantel, N. (1966) Evaluation of survival data and two new rank order statistics arising in its consideration. Cancer Chemotherapy Reports 50, 163–170.Google ScholarPubMed
Morris, R.F. (1959) Single factor analysis in population dynamics. Ecology 40, 580–588.Google Scholar
Prentice, R.L. & Marek, P. (1979) A quantitative discrepancy between censored data rank tests. Biometrics 35, 861–867.CrossRefGoogle Scholar
Regniere, J. (1984) A method of describing and using variability in development rates for the simulation of insect phenology. Canadian Entomologists 116, 1367–1376.Google Scholar
Schaalje, G.B. & van der Varrt, H.R. (1989) Relationships among recent models for insect population dynamics with variable rates of development. Journal of Mathematical Biology 27, 399–428.Google Scholar
Schaalje, G.B., Johnson, D.L. & van der Varrt, H.R. (1992) Application of competing risks theory to the analysis of effects of Nosema locustae and N. cuneatum on development and mortality of migratory locusts. Environmental Entomology 21(5), 939–948.Google Scholar
Southwood, T.R.E. & Henderson, P.A. (2000) Ecological Methods. 3rd edn.592 pp. New York, USA, Wiley-Blackwell.Google Scholar
Tanhuanpa, M. & Ruohhoma, M. (2001) High larval predation rate in non-outbreaking populations of a geometrid moth. Ecology 82(1), 281–289.Google Scholar
Tarone, R.E. & Ware, J. (1977) Tests for trend in life table analysis. Biometrika 64(1), 156–160CrossRefGoogle Scholar
Varley, G.C. & Gradwell, G.R. (1970) Recent advances in insect population dynamics. Annual Review of Entomology 15, 1–24.Google Scholar
Velema, H.P., Hemerik, L., Hoddle, M.S. & Luck, R.F. (2005) Brochosome influence on parasitisation efficiency of Homalodisca coagulata (Say) (Hemiptera: Cicadellidae) egg masses by Gonatocerus ashmeadi Girault (Hymenoptera: Mymaridae). Ecological Entomology 30, 485–496.Google Scholar
Wagner, T.L., Wu, H., Sharpe, P.J.H. & Coulson, R.N. (1984) Modeling distribution of insect development time: a literature review and application of the Weibull function. Annals of Entomological Society of America 77, 475–487.Google Scholar
Young, L.J. & Young, J.H. (1991) Alternative view of statistical hypothesis testing. Environmental Entomology 20(5), 1241–1245.Google Scholar
Zadoks, J.C., Chang, T.T. & Konzak, C.F. (1974) A decimal code for the growth stages of cereal. Weed Research 14, 415–421.CrossRefGoogle Scholar
Zens, M.S. & Peart, D.R. (2003) Dealing with death data: individual hazards, mortality and bias. Trends in Ecology and Evolution 18(7), 366–373.CrossRefGoogle Scholar