Logistic regression and continuation ratio models to estimate insect development under variable temperatures

Temperature strongly influences insect development. So a physiological scale, instead of a temporal scale, is classically used to introduce variable temperatures in phenological models. With a linear scale, we must estimate two parameters per stage, the base (temperature threshold) and the number of degree-days. Difficulties arise when neither of these parameters is known. Logistic regression and continuation ratio (CR) models may be applied, especially to stage-frequency data. In this paper, we test these two models by using them to estimate durations of development stages under variable temperatures. Simulations show that logistic regression applied to one stage gives relatively close estimates of development duration only when the base is known. Accuracy of parameter estimates depends only slightly on sample size. The CR model leads to unbiased estimates only in the case of a common base for all stages. When the base varies with stage, the CR model overestimates the durations and gives non-independent estimates. Thus, the continuation ratio model is inappropriate for field studies when the physiological scale varies with stage of development.