%0 Journal Article
%A Mazerolle, M.J.
%T Improving data analysis in herpetology: Using Akaike's information criterion (AIC) to assess the strength of biological hypotheses
%B Amphibia-Reptilia
%D 2006
%V 27
%P 169-180
%N 2
%Z cited By 130; doi=(10.1163/156853806777239922)
%X In ecology, researchers frequently use observational studies to explain
a given pattern, such as the number of individuals in a habitat patch,
with a large number of explanatory (i.e., independent) variables.
To elucidate such relationships, ecologists have long relied on hypothesis
testing to include or exclude variables in regression models, although
the conclusions often depend on the approach used (e.g., forward,
backward, stepwise selection). Though better tools have surfaced
in the mid 1970's, they are still underutilized in certain fields,
particularly in herpetology. This is the case of the Akaike information
criterion (AIC) which is remarkably superior in model selection (i.e.,
variable selection) than hypothesis-based approaches. It is simple
to compute and easy to understand, but more importantly, for a given
data set, it provides a measure of the strength of evidence for each
model that represents a plausible biological hypothesis relative
to the entire set of models considered. Using this approach, one
can then compute a weighted average of the estimate and standard
error for any given variable of interest across all the models considered.
This procedure, termed model-averaging or multimodel inference, yields
precise and robust estimates. In this paper, I illustrate the use
of the AIC in model selection and inference, as well as the interpretation
of results analysed in this framework with two real herpetological
data sets. The AIC and measures derived from it is should be routinely
adopted by herpetologists. © Koninklijke Brill NV 2006.
Scopus
%K Akaike information criterion; herpetofauna; hypothesis testing; numerical
model; regression analysis
%U http://www.scopus.com/inward/record.url?eid=2-s2.0-33746589181&partnerID=40&md5=83b14470f4a20d8748f68e587f3725eb
%F Mazerolle2006a
%3 BibTeX type = ARTICLE