Optimal spatial monitoring of populations described by reaction–diffusion models. (7th February 2022)

• Record Type: Journal Article
• Authors: Parisey, Nicolas; Leclerc, Melen; Adamczyk-Chauvat, Katarzyna
• Is Part Of: Journal of theoretical biology. Volume 534(2022)
• Publication Date: 2022-02-07
• Subjects: Optimal design of experiments -- Sampling effort -- Spatial population model -- Optimal survey -- Mechanistic-statistical modelling; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2021.110976
• Languages: English
• ISSNs: 0022-5193

Abstract

Highlights: Reaction–diffusion equations can model many ecological processes. They can be used to infer the spatial dynamics of populations from sampled data. We recall that optimal design of experiments can help with such inference. Optimal sensor locations can then be generated for ecological spatial survey. Abstract: Using spatialised population measurements and related geographic habitat data, it is feasible nowadays to derive parsimonious spatially explicit population models and to carry on their parameter estimation. To achieve such goal, reaction–diffusion models are 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 one of the worst bottleneck against a widespread usage of reaction–diffusion models. Hence 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 under constraints.