Despite the variety of statistical methods available for static modeling of
plant distribution, few studies directly compare methods on a common data
set. In this paper, the predictive power of Generalized Linear Models (GLM)
versus Canonical Correspondence Analysis (CCA) models of plant distributio
n in the Spring Mountains of Nevada, USA, are compared. Results show that G
LM models give better predictions than CCA models because a species-specifi
c subset of explanatory variables can be selected in GLM, while in CCA, all
species are modeled using the same set of composite environmental variable
s (axes). Although both techniques can be readily ported to a Geographical
Information System (GIS), CCA models are more readily implemented for many
species at once. Predictions from both techniques rank the species models i
n the same order of quality; i.e. a species whose distribution is well mode
led by GLM is also well modeled by CCA and vice-versa. In both cases, speci
es for which model predictions have the poorest accuracy are either disturb
ance or fire related, or species for which too few observations were availa
ble to calibrate and evaluate the model. Each technique has its advantages
and drawbacks. In general GLM will provide better species specific-models,
but CCA will provide a broader overview of multiple species, diversity, and
plant communities.