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Single-pool exponential decomposition models: potential pitfalls in their use in ecological studies.

E Carol Adair1, Sarah E Hobbie, Russell K Hobbie

  • 1Department of Forest Resources, University of Minnesota, Saint Paul, Minnesota 55108, USA. adair@nceas.ucsb.edu

Ecology
|May 14, 2010
PubMed
Summary

Accurate litter decomposition rate (k) estimates are crucial for understanding carbon cycling. This study reveals that common modeling choices can lead to errors, recommending specific methods for more reliable results.

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Area of Science:

  • Ecology
  • Biogeochemistry
  • Environmental Science

Background:

  • Litter decomposition is vital for ecosystem carbon and nutrient cycling.
  • A single-pool negative exponential model is commonly used to estimate decomposition rates (k).
  • Previous research has not fully addressed potential inaccuracies in this common modeling approach.

Purpose of the Study:

  • To identify potential pitfalls in estimating decomposition rates (k) using standard methods.
  • To evaluate the impact of model form and data transformation on k estimates.
  • To provide recommendations for more accurate decomposition rate estimations.

Main Methods:

  • Review of recent decomposition research.
  • Data simulations to test model assumptions and parameter estimations.
  • Analysis of real-world decomposition data.

Main Results:

  • Estimating decomposition rates (k) by allowing initial mass to vary from measured values leads to significant over- or underestimation.
  • Log-transforming data for linear regression yields inaccurate k estimates unless errors are lognormally distributed.
  • Nonlinear regression of untransformed data provides accurate k estimates irrespective of error structure.

Conclusions:

  • Fix initial mass at the measured value and use nonlinear regression with untransformed data for accurate decomposition rate (k) estimation.
  • Treat zero values as missing data when log-transforming data for linear regression.
  • Adopting these recommendations will improve k estimate accuracy and facilitate cross-study comparisons, enhancing understanding of decomposition processes.