Estimating litter decomposition rate in single-pool models using nonlinear beta regression.

ABSTRACT: Litter decomposition rate (k) is typically estimated from proportional litter mass loss data using models that assume constant, normally distributed errors. However, such data often show non-normal errors with reduced variance near bounds (0 or 1), potentially leading to biased k estimates. We compared the performance of nonlinear regression using the beta distribution, which is well-suited to bounded data and this type of heteroscedasticity, to standard nonlinear regression (normal errors) on simulated and real litter decomposition data. Although the beta model often provided better fits to the simulated data (based on the corrected Akaike Information Criterion, AIC(c)), standard nonlinear regression was robust to violation of homoscedasticity and gave equally or more accurate k estimates

SUBMITTER: Laliberte E 

PROVIDER: S-EPMC3458010 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature