A Bayesian hierarchical modeling approach for analyzing observational data from marine ecological studies.
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.
Qian
Song S
SS
Nicholas School of the Environment, Duke University, Durham, NC 27708, USA. song@duke.edu
Craig
J Kevin
JK
Baustian
Melissa M
MM
Rabalais
Nancy N
NN
D000704:Analysis of Variance; D001499:Bayes Theorem; D008386:Marine Biology / Q000379:methods*; D015233:Models, Statistical; D019370:Observation; D009792:Oceans and Seas