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Dynamic design of ecological monitoring networks for non‐Gaussian spatio‐temporal data
Author(s) -
Wikle Christopher K.,
Royle J. Andrew
Publication year - 2005
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.718
Subject(s) - computer science , kalman filter , sampling (signal processing) , component (thermodynamics) , gaussian , gaussian process , conditional probability distribution , poisson distribution , ecology , data mining , econometrics , statistics , artificial intelligence , mathematics , filter (signal processing) , physics , quantum mechanics , biology , computer vision , thermodynamics
Many ecological processes exhibit spatial structure that changes over time in a coherent, dynamical fashion. This dynamical component is often ignored in the design of spatial monitoring networks. Furthermore, ecological variables related to processes such as habitat are often non‐Gaussian (e.g. Poisson or log‐normal). We demonstrate that a simulation‐based design approach can be used in settings where the data distribution is from a spatio‐temporal exponential family. The key random component in the conditional mean function from this distribution is then a spatio‐temporal dynamic process. Given the computational burden of estimating the expected utility of various designs in this setting, we utilize an extended Kalman filter approximation to facilitate implementation. The approach is motivated by, and demonstrated on, the problem of selecting sampling locations to estimate July brood counts in the prairie pothole region of the U.S. Copyright © 2005 John Wiley & Sons, Ltd.