Abstract

Seed germination is a complex biological process which is influenced by various environmental and genetic factors. The effects of temperature on plant development are the basis for models used to predict the timing of germination. Estimation of the cardinal temperatures, including base, optimum, and maximum, is essential because rate of development increases between base and optimum, decreases between optimum and maximum, and ceases above the maximum and below the base temperature. Nonlinear growth curves can be specified to model the time course of germination at various temperatures. Quantiles of such models are regressed on temperature to estimate cardinal quantities. Bootstrap simulation techniques may then be employed to assure the statistical accuracy of these estimates and to provide approximate nonparametric confidence intervals. A statistical approach to modelling germination is presented and application is demonstrated with reference to replicated experiments designed to determine the effect of temperature gradient on germination of three populations of an introduced weed species common crupina (Crupina vulgaris Pers.).

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Apr 28th, 9:00 AM

ESTIMATION OF CARDINAL TEMPERATURES IN GERMINATION DATA ANALYSIS

Seed germination is a complex biological process which is influenced by various environmental and genetic factors. The effects of temperature on plant development are the basis for models used to predict the timing of germination. Estimation of the cardinal temperatures, including base, optimum, and maximum, is essential because rate of development increases between base and optimum, decreases between optimum and maximum, and ceases above the maximum and below the base temperature. Nonlinear growth curves can be specified to model the time course of germination at various temperatures. Quantiles of such models are regressed on temperature to estimate cardinal quantities. Bootstrap simulation techniques may then be employed to assure the statistical accuracy of these estimates and to provide approximate nonparametric confidence intervals. A statistical approach to modelling germination is presented and application is demonstrated with reference to replicated experiments designed to determine the effect of temperature gradient on germination of three populations of an introduced weed species common crupina (Crupina vulgaris Pers.).