A METHOD FOR ROBUST-CALIBRATION OF ECOLOGICAL MODELS UNDER DIFFERENT TYPES OF UNCERTAINTY

This paper is concerned with the inverse problem in ecological modelling: how to update information (if any) on parameter uncertainty by using the information on the actual system. In the probabilistic (Bayesian) context this implies estimating the posterior probability distribution of the parameters on the basis of prior information and the probability distribution of measurements. In other contexts the procedure may result in a set or a possibility distribution (fuzzy representation) in parameter space. Although the solution to the inverse problem is apparently straightforward, to be practical it requires either fairly restrictive assumptions or it makes large computational demands. The paper presents an algorithm to solve the inverse problem in various contexts which is generally applicable and computationally efficient. The method is illustrated on various test functions and an actual case study. For calibration problems with a moderate number of dimensions (or: higher-dimensional problems that can be reduced to these) the new algorithm provides a robust and relatively efficient way to characterize posterior parameter distributions or sets. The algorithm requires a number of function evaluations proportional to the logarithm of the search volume, while for conventional random search this number increases proportional to search volume itself. For inherently high dimensional problems the present approach is still relatively efficient, but slow in absolute numbers of function evaluations.