A Bayesian hierarchical modeling approach for analyzing observational data from marine ecological studies |
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| Authors: | Song S Qian J Kevin Craig Melissa M Baustian Nancy N Rabalais |
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| Institution: | aNicholas School of the Environment, Duke University, Durham, NC 27708, USA;bFlorida State University Coastal and Marine Laboratory, Florida State University, 3618 Highway 98, St. Teresa, FL 32358-2702, USA;cDepartment of Oceanography and Coastal Sciences, 1231 Energy, Coast and Environment Building, Louisiana State University, Baton Rouge, LA 70803, USA;dLouisiana Universities Marine Consortium, 8124 Highway, 56, Chauvin, LA 70344, USA |
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| Abstract: | We introduce the Bayesian hierarchical modeling approach for analyzing observational data from marine ecological studies using a data set intended for inference on the effects of bottom-water hypoxia on macrobenthic communities in the northern Gulf of Mexico off the coast of Louisiana, USA. We illustrate (1) the process of developing a model, (2) the use of the hierarchical model results for statistical inference through innovative graphical presentation, and (3) a comparison to the conventional linear modeling approach (ANOVA). Our results indicate that the Bayesian hierarchical approach is better able to detect a “treatment” effect than classical ANOVA while avoiding several arbitrary assumptions necessary for linear models, and is also more easily interpreted when presented graphically. These results suggest that the hierarchical modeling approach is a better alternative than conventional linear models and should be considered for the analysis of observational field data from marine systems. |
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| Keywords: | ANOVA Bayesian statistics Gulf of Mexico Hierarchical model Hypoxia |
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