  Although ARTMAP and ART-based models were introduced in early 70's they were
not used in characterizing and classifying ecological observations. ART-based
models have been extensively used for classification models based on satellite
imagery. This report, to our knowledge, is the first application of ART-based
methods and specifically ARTMAP for predicting habitat selection and spatial
distribution of species. We compare the performance of ARTMAP to assess the
breeding success of three bird species (Lanius senator, Hippolais pallida, and
Calandrella brachydactyla) based on multi-spectral satellite imagery and
environmental variables. ARTMAP is superior both in terms of performance
(percent correctly classified - pcc = 1.00) and generalizability (pcc >0.96) to
those of feedforward multilayer backpropogation (>0.87, >0.65), linear and
quadratic discriminant analysis (>0.48, >0.46) and k-nearest neighbor (>0.82,
>0.66) methods. Compared to other methods, ARTMAP is able to incorporate new
observations with far less computational effort and can easily add data to
already trained models.
