Hagy James D, Kreakie Betty J, Pelletier Marguerite C, Nojavan Farnaz, Kiddon John A, Oczkowski Autumn J
Atlantic Coastal Environmental Science Division, Center for Environmental Measurement and Modeling, Office of Research and Development, US Environmental Protection Agency. 27 Tarzwell Drive, Narragansett, RI 02882.
Ecol Indic. 2022 Sep 1;142:1-12. doi: 10.1016/j.ecolind.2022.109267.
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.
海岸生态研究的目标之一是描述、量化和预测人类对海岸生态系统的影响。已开展了广泛的跨系统评估以对生态系统状态或状况进行分类，但更新不频繁，可能是因为实施这些评估需要大量信息和精力。如果能够使用广泛可得的数据和信息预测处于表明状态或状况的类别中的概率，此类评估可能会更有用，这为通过考虑其对生态系统状况的预期影响来解释潜在预测因子的变化提供了一种有用方法。为说明一种可能的方法，我们使用叶绿素a作为状况指标，以替代预期的综合状况评估。我们展示了一种预测方法，首先使用随机森林模型来指导变量选择，然后使用贝叶斯多级有序分类回归来量化海岸营养状态指数并预测系统状态。我们最初使用非信息先验对2010年的水质数据（总氮和磷、溶解无机氮和磷、塞氏深度）和一个区域因子拟合模型。然后我们使用基于初始拟合的后验参数分布和2015年数据的先验分布更新模型。贝叶斯模型展示了一种直观的方法，可利用新数据更新模型或分析，同时保留先验知识的益处并保持考虑新类型信息的灵活性。为说明该模型的使用方式，我们将我们开发的营养状态指数和分类应用于波士顿港水质数据的时间序列，波士顿港是一个海岸生态系统，其养分输入发生了显著变化。分析表明，如何在美国大陆周边河口数据提供的比较生态背景下理解波士顿港的水质状况和趋势，并说明了这种分析方法如何可被贝叶斯统计非从业者用作解释工具以及进一步模型开发和分析的框架。
