Optimal spatial monitoring of populations described by reaction-diffusion models

Abstract

Using spatialised population measurements and related geographic habitat data, it is quite feasible nowadays to derive parsimonious spatially explicit population models and to carry on their parameter estimation. To achieve such goal, reaction-diffusion models are fairly common in conservation biology and agricultural plant health where they are used, for example, for landscape planning or epidemiological surveillance. Unfortunately, if the mathematical methods and computational power are readily available, biological measurements are not. Despite the high throughput of some habitat related remote sensors, the experimental cost of biological measurements are, in our view, one of the worst bottleneck against a widespread usage of reaction-diffusion models. Hence, in this paper, we will recall some classical methods for optimal experimental design that we deem useful to spatial ecologist. Using two case studies, one in landscape ecology and one in conservation biology, we will show how to construct a priori experimental design minimizing variance of parameter estimates, enabling optimal experimental setup with pre and post hoc filtering for accommodating additional constraints.