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Laboratory Estimation of Net Trophic Transfer Efficiencies of PCB Congeners to Lake Trout Salvelinus namaycush from Its Prey
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Rethinking the lake trophic state index.

Farnaz Nojavan A1, Betty J Kreakie2, Jeffrey W Hollister2

  • 1ORISE, Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, Atlantic Coastal Environmental Sciences Division, Narragansett, RI, United States of America.

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|November 20, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new Bayesian model for classifying lake trophic states using multiple variables. This approach provides a more continuous and probabilistic assessment of lake health, improving upon existing methods.

Keywords:
Bayesian Multilevel Ordered Categorical Regression ModelEutrophicationLakeNational Lake AssessmentProportional Odds Logistic Regression ModelTrophic State

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Area of Science:

  • Ecology
  • Environmental Science
  • Statistical Modeling

Background:

  • Lake trophic state classifications are crucial for understanding lentic ecosystem health and services.
  • Current classification schemes are limited by single-variable, discrete, and deterministic approaches.
  • Existing methods face criticism for not fully capturing complex ecological dynamics.

Purpose of the Study:

  • To develop an updated lake trophic classification model addressing limitations of current schemes.
  • To create a multi-variable, continuous, and probabilistic index for lake trophic state.
  • To improve the accuracy and robustness of lake ecosystem assessments.

Main Methods:

  • Utilized a Bayesian multilevel ordered categorical regression framework.
  • Employed proportional odds logistic regression (POLR) to model ordered, categorical lake trophic states.
  • Incorporated Secchi disk depth, elevation, nitrogen (N), and phosphorus (P) concentrations as predictors.

Main Results:

  • The POLR model achieved an overall accuracy of 0.68 compared to existing Trophic State Index (TSI) classifications.
  • Balanced accuracy for the model ranged from 0.72 to 0.93 across different trophic states.
  • The developed index is multi-variable based, continuous, and provides probabilistic classifications.

Conclusions:

  • The new Bayesian model offers a more comprehensive and accurate method for lake trophic state classification.
  • This approach addresses key limitations of previous single-variable, discrete, and deterministic indices.
  • The model's probabilistic nature and uncertainty quantification enhance its utility for ecological assessments across large spatial scales.