Rethinking the lake trophic state index

• Main Authors: Farnaz Nojavan A., Betty J. Kreakie, Jeffrey W. Hollister, Song S. Qian
• Format: Article
• Language: English
• Published: PeerJ Inc. 2019-11-01
• Series: PeerJ
• Subjects: Trophic State; Proportional Odds Logistic Regression Model; Bayesian Multilevel Ordered Categorical Regression Model; National Lake Assessment; Eutrophication; Lake
• Online Access: https://peerj.com/articles/7936.pdf

Lake trophic state classifications provide information about the condition of lentic ecosystems and are indicative of both ecosystem services (e.g., clean water, recreational opportunities, and aesthetics) and disservices (e.g., cyanobacteria blooms). The current classification schemes have been criticized for developing indices that are single-variable based (vs. a complex aggregate of multi-variables), discrete (vs. a continuous), and/or deterministic (vs. an inherently random). We present an updated lake trophic classification model using a Bayesian multilevel ordered categorical regression. The model consists of a proportional odds logistic regression (POLR) that models ordered, categorical, lake trophic state using Secchi disk depth, elevation, nitrogen concentration (N), and phosphorus concentration (P). The overall accuracy, when compared to existing classifications of trophic state index (TSI), for the POLR model was 0.68 and the balanced accuracy ranged between 0.72 and 0.93. This work delivers an index that is multi-variable based, continuous, and classifies lakes in probabilistic terms. While our model addresses aforementioned limitations of the current approach to lake trophic classification, the addition of uncertainty quantification is important, because the trophic state response to predictors varies among lakes. Our model successfully addresses concerns with the current approach and performs well across trophic states in a large spatial extent.