@ARTICLE {LaliberteAdairHobbie2012,
AUTHOR = {Laliberte, E. and Adair, E.C. and Hobbie, S.E.},
TITLE = {Estimating Litter Decomposition Rate in Single-Pool Models Using
Nonlinear Beta Regression},
JOURNAL = {PLoS ONE},
YEAR = {2012},
VOLUME = {7},
NUMBER = {9},
NOTE = {cited By 1},
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, AICc),
standard nonlinear regression was robust to violation of homoscedasticity
and gave equally or more accurate k estimates as nonlinear beta regression.
Our simulation results also suggest that k estimates will be most
accurate when study length captures mid to late stage decomposition
(50-80% mass loss) and the number of measurements through time is
≥5. Regression method and data transformation choices had the smallest
impact on k estimates during mid and late stage decomposition. Estimates
of k were more variable among methods and generally less accurate
during early and end stage decomposition. With real data, neither
model was predominately best; in most cases the models were indistinguishable
based on AICc, and gave similar k estimates. However, when decomposition
rates were high, normal and beta model k estimates often diverged
substantially. Therefore, we recommend a pragmatic approach where
both models are compared and the best is selected for a given data
set. Alternatively, both models may be used via model averaging to
develop weighted parameter estimates. We provide code to perform
nonlinear beta regression with freely available software. © 2012
Laliberté et al.},
ART_NUMBER = {e45140},
DOCUMENT_TYPE = {Article},
DOI = {10.1371/journal.pone.0045140},
KEYWORDS = {accuracy; agricultural parameters; Akaike information criterion; analytical
error; article; beta distributed error; controlled study; data processing;
heteroscedasticity; homoscedasticity; litter decomposition; litter
mass loss; maximum likelihood method; nonlinear beta regression analysis;
nonlinear regression analysis; simulation; statistical model; statistical
parameters; variable normal error, Biodegradation, Environmental;
Carbon Cycle; Computer Simulation; Ecosystem; Models, Biological;
Nonlinear Dynamics; Regression Analysis},
SOURCE = {Scopus},
URL = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84866648685&partnerID=40&md5=ffba6322b9941c3641c887001c377525},
}