Effective management of estuaries requires a comprehensive assessment of estuary condition. However, data available for such an assessment is often comprised of limited and disparate measures collected at point locations. A traditional approach is that specific metrics, such as oxygen and nutrient concentrations, can be regarded as measures of estuary health. However, integrating this data to evaluate an estuary’s overall ecological condition is difficult. Chiu et al. (2011; 2013) present a different perspective, in which the observed metrics are response variables whose values reflect an unobserved latent explanatory variable (the Latent Health Factor Index, or LHFI) as an indicator of the estuary’s health. This latent variable can in turn be modeled as a response variable that depends on other factors, such as watershed characteristics. The research presented here utilized five water quality measures (bottom dissolved oxygen, surface chlorophyll a concentration, near-surface light transmissivity, and the total concentrations of surface nitrogen and phosphate) that were collected in 2011 from six regions of San Francisco Bay area as part of the Environmental Protection Agency’s National Coastal Condition Assessment. These metrics were used in a hierarchical Bayesian model to determine the LHFI values for the six San Francisco Bay regions.
Results/Conclusions
The hierarchical model presented here considered the LHFI as the response variable in an ANOVA context, where the explanatory factor consisted of regions of San Francisco Bay. The LHFI for each region was in turn combined with parameters indexing each metric’s identity (dissolved oxygen, nitrogen, etc.) to serve as predictors of the observed metric values. A Monte Carlo Markov Chain simulation was run for 5,000 samples of the LFHI and associated parameters. The resulting parameter distributions showed that the South Bay and Lower South Bay had the poorest LHFI, a result consistent with other assessments of the region. Advantages of the LHFI approach are that it provides a statistically rigorous numerical assessment of a region’s heath for which credible intervals can be evaluated to judge the uncertainty present in the region’s LHFI. Additionally, the contributing metrics can be evaluated as to which are important contributors to the health score. Finally, such models can be extended to include watershed characteristics, such as urbanization, so that their influence on estuary LHFI score can also be evaluated.