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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Estimating litter decomposition rate in single-pool models using nonlinear beta regression.

Etienne Laliberté1, E Carol Adair, Sarah E Hobbie

  • 1School of Plant Biology, The University of Western Australia, Crawley, Western Australia, Australia.

Plos One
|October 11, 2012
PubMed
Summary

Estimating litter decomposition rates (k) using standard models can be biased. Comparing nonlinear beta regression with standard methods shows similar accuracy, but both models should be used for robust litter decomposition rate estimations.

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Area of Science:

  • Ecology
  • Environmental Science
  • Biogeochemistry

Background:

  • Litter decomposition rate (k) estimation commonly uses models assuming constant, normally distributed errors.
  • Proportional litter mass loss data often exhibit non-normal errors and reduced variance near bounds (0 or 1), potentially biasing k estimates.

Purpose of the Study:

  • To compare the performance of nonlinear regression using the beta distribution with standard nonlinear regression (normal errors) for estimating litter decomposition rates (k).
  • To evaluate the impact of study length and measurement frequency on the accuracy of k estimates.

Main Methods:

  • Nonlinear regression using the beta distribution was compared to standard nonlinear regression (normal errors) on simulated and real litter decomposition data.
  • Model performance was assessed using the corrected Akaike Information Criterion (AIC(c)) and accuracy of k estimates.

Main Results:

  • Standard nonlinear regression was robust to heteroscedasticity and provided equally or more accurate k estimates than nonlinear beta regression on simulated data.
  • k estimates were most accurate when study length captured 50-80% mass loss with at least 5 measurements.
  • Model choice had minimal impact on k estimates during mid to late decomposition stages, but estimates diverged for high decomposition rates.

Conclusions:

  • A pragmatic approach of comparing both beta and normal models, or using model averaging, is recommended for robust litter decomposition rate estimation.
  • Study design, particularly duration and measurement frequency, significantly influences the accuracy of k estimates.