One of the goals of coastal ecological research is to describe, quantify and predict human effects on coastal ecosystems. Broad cross-systems assessments to classify ecosystem status or condition have been developed, but are not updated frequently, likely because a lot of information and effort is needed to implement them. Such assessments could be more useful if the probability of being in a class indicating status or condition could be predicted using widely available data and information, providing a useful way to interpret changes in underlying predictors by considering their expected impact on ecosystem condition. To illustrate a possible approach, we used chlorophyll-a as an indicator of condition, in place of the intended comprehensive condition assessment. We demonstrated a predictive approach starting with a random forest model to inform variable selection, then used a Bayesian multilevel ordered categorical regression to quantify a coastal trophic state index and predict system status. We initially fit the model using non-informative priors to water quality data (total nitrogen and phosphorus, dissolved inorganic nitrogen and phosphorus, secchi depth) from 2010 and a regional factor. We then updated the model using prior distributions based on posterior parameter distributions from the initial fit and data from 2015. The Bayesian model demonstrates an intuitive way to update a model or analysis with new data while retaining the benefit of prior knowledge and maintaining flexibility to consider new kinds of information. To illustrate how the model could be used, we applied our developed trophic state index and classification to a time series of water quality data from Boston Harbor, a coastal ecosystem that has undergone significant changes in nutrient inputs. The analysis shows how water quality status and trends in Boston Harbor can be understood in the comparative ecological context provided by data from estuaries around the continental US and illustrates how the analytical approach could be used as an interpretive tool by non-practitioners of Bayesian statistics as well as a framework for further model development and analysis.
Keywords: Bayesian; chlorophyll; classification; estuary; nutrients; random forest.