Robustness of the estimators of transition rates for size-classified matrix models

Matrix models are often used to model the dynamics of age-structured or size-structured populations. The Usher model is an important particular case that relies on the following hypothesis: between time steps tt and t+1t+1, individuals either remain in the same class, move up to the following class, or die. There are then two ways of handling data that do not meet this condition: either remove them prior to data analysis or rectify them. These two ways correspond to two estimators of transition parameters. The former, which corresponds to the classical estimator, is obtained from the latter by a data trimming. The two estimators of transition parameters are compared on the basis of their robustness in order to obtain a criterion of choice between the two estimators. The influence curve of both estimators is first computed, then their gross sensitivity and their asymptotic variance. The untrimmed estimator is more robust than the classical one. Its asymptotic variance can be lower or greater than that of the classical estimator depending on the boundaries used for data trimming. The results are applied to a tropical rain forest in French Guiana, with a discussion on the role of the class width.