Further building blocks

Abstract

We concentrate on model-based methods of estimating abundance in this book. There are advantages and disadvantages to doing this. If you were interested in the process determining the population state (the spatial distribution of animals, for example), this would be a reason to use model-based methods. Model-based methods can also lead to substantial improvement in precision, because some of the variation in density is “explained” by the model instead of being assigned to variance. The main reason you might not want to use model-based inference is that you never know the true process determining the population state, and if the state model you assume is not a good approximation to it, inferences may be biased. With the availability of flexible state models and adequate diagnostics, this is much less of a problem than it used to be, although there are still sometimes difficulties. In particular, the independence assumptions implicit in many state models can be unrealistic, and modelling dependence can be difficult. Unless care is taken, this can result in model-based estimates of variance that are too small and corresponding confidence intervals that are too narrow. A compromise approach is to use model-based methods for point estimation, and nonparametric, and essentially design-based methods for interval estimation.