Isomorphic chain graphs for modeling spatial dependence in ecological data |
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Authors: | Alix I Gitelman Alan Herlihy |
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Institution: | (1) Statistics Department, Oregon State University, Corvallis, OR, USA;(2) Fisheries and Wildlife Department, Oregon State University, Corvallis, OR, USA |
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Abstract: | Graphical models (alternatively, Bayesian belief networks, path analysis models) are increasingly used for modeling complex
ecological systems (e.g., Lee, In: Ferson S, Burgman M(eds) Quantative methods for conservation biology. Springer, Berlin
Heilin Heideslperk New York, pp.127–147, 2000; Borsuk et al., J Water Res Plann Manage 129:271–282, 2003). Their implementation
in this context leverages their utility in modeling interrelationships in multivariate systems, and in a Bayesian implementation,
their intuitive appeal of yielding easily interpretable posterior probability estimates. However, methods for incorporating
correlational structure to account for observations collected through time and/or space—features of most ecological data—have
not been widely studied; Haas et al. (AI Appl 8:15–27, 1994) is one exception. In this paper, an “isomorphic” chain graph
(ICG) model is introduced to account for correlation between samples by linking site-specific Bayes network models. Several
results show that the ICG preserves many of the Markov properties (conditional and marginal dependencies) of the site-specific
models. The ICG model is compared with a model that does not account for spatial correlation. Data from several stream networks
in the Willamette River valley, Oregon (USA) are used. Significant correlation between sites within the same stream network
is shown with an ICG model. |
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Keywords: | Bayesian belief network Graphical model Spatial correlation |
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