Frequently, ecologists are interested in exploring ecological relationships, describing patterns and processes, or making spatial or temporal predictions. These purposes often can be addressed by modeling the relationship between some outcome or response and a set of features or explanatory variables. Some examples from ecology include:

• analyzing bioclimatic factors affecting the presence of a species in the landscape,

• mapping forest types from remotely sensed data,

• predicting forest attributes over large geographic areas,

• identifying suitable wildlife habitat,

• making sense of complex ecological data sets with hundreds of variables,

• predicting microhabitat affecting fish species distributions,

• developing screening tests for unwanted plant species,

• monitoring and mapping landcover change through time,

• using environmental variables to model the distribution of vegetation alliances,

• assessing biological indicators of environmental factors affecting fish habitat, and

• identifying fuels characteristics for fire spread models.

Modeling ecological data poses many challenges. The response as well as the explanatory variables may be continuous or discrete. The relationships that need to be deciphered are often nonlinear and involve complex interactions between explanatory variables. Missing values for both explanatory and response variables are not uncommon, and outliers almost always exist. In addition, ecological problems usually demand methods that are both easy to implement and easy to interpret. Frequently, many different statistical tools are employed to handle unique problems posed by the various scenarios. This diverse set of tools might include multiple or logistic regression, log linear models, analysis of variance, survival models, and the list continues. Classification and regression trees, however, offer a single tool to work with all these challenges. This article describes classification and regression trees in general, the major concepts guiding their construction, some of the many issues a modeler may face in their use, and, finally, recent extensions to their methodology. The intent of the article is to simply familiarize the reader with the terminology and general concepts behind this set of tools.

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