  Separate artificial neural network (ANN) models were developed from data in
two geographical regions and years apart for a marsh-nesting bird, the
red-winged blackbird Agelaius phoeniceus. Each model was independently tested
on the spatially and temporally distinct data from the other region to
determine how generalizable it was. The first model was developed to predict
occurrence of nests in two wetlands on Lake Erie, Ohio in 1995 and 1996. The
second model was developed to predict breeding success in two marshes in
Connecticut, USA in 1969 and 1970. Independent variables were vegetation
durability, stem density, stem/nest height, distance to open water, distance to
edge, and water depth. With input variable relevances, sensitivity analyses and
neural interpretation diagrams we were able to understand how the different
models predicted nest occurrence and breeding success and compare their
differences and similarities. Both models also predicted increasing nest
occurrence/breeding success with increasing water depth under the nest and
increasing distance to edge. However, relationships for prediction differed in
the models. Generalizability of the models was poor except when the marshes had
similar values of important variables in the model. ANN models performed better
than generalized linear models (GLM) on marshes with similar structures.
Generalizability of the models did not differ in nest occurrence and breeding
success data. Extensive testing also showed that the GLMs were not necessarily
more generalizable than ANNs, suggesting that ANN models make good definitions
of a study system but are too specific to generalize well to other ecologically
complex systems unless input variable distributions are very similar.
