Matrix Models for Size-Structured Populations: Unrealistic Fast Growth or Simply Diffusion?

Matrix population models are widely used to study population dynamics but have been criticized because their outputs are sensitive to the dimension of the matrix (or, equivalently, to the class width). This sensitivity is concerning for the population growth rate ([Image: see text]) because this is an intrinsic characteristic of the population that should not depend on the model specification. It has been suggested that the sensitivity of [Image: see text] to matrix dimension was linked to the existence of fast pathways (i.e. the fraction of individuals that systematically move up a class), whose proportion increases when class width increases. We showed that for matrix population models with growth transition only from class [Image: see text] to class [Image: see text], [Image: see text] was independent of the class width when the mortality and the recruitment rates were constant, irrespective of the growth rate. We also showed that if there were indeed fast pathways, there were also in about the same proportion slow pathways (i.e. the fraction of individuals that systematically remained in the same class), and that they jointly act as a diffusion process (where diffusion here is the movement in size of an individual whose size increments are random according to a normal distribution with mean zero). For 53 tree species from a tropical rain forest in the Central African Republic, the diffusion resulting from common matrix dimensions was much stronger than would be realistic. Yet, the sensitivity of [Image: see text] to matrix dimension for a class width in the range 1–10 cm was small, much smaller than the sampling uncertainty on the value of [Image: see text]. Moreover, [Image: see text] could either increase or decrease when class width increased depending on the species. Overall, even if the class width should be kept small enough to limit diffusion, it had little impact on the estimate of [Image: see text] for tree species.