Comparing different environmental variables in predictive models of bird distribution

The response variable (often the presence of a species) in predictive habitat models relies on a set of environmental predictors. Among all known environmental predictors, vegetation has the most effect on species abundance and on their habitat preferences, due to the wide range of necessary resources that it provides for the survival of bird species. However, other predictors, in turn, affect bird distribution, and some times they play a more important role in habitat selection, depending on the natural history and ecological needs of the bird species. In this regard, different analyses have been conducted to predict the distribution, and define habitat suitability (such as discriminant function analysis, General Linear Models, and ANOVA). In this study, all three analytical designs were used to investigate the relationship of seven bird species to the major environmental gradients in the study area, to find out the significance of each of these factors on habitat selection. GIS has been used to prepare spatial distributional data, and to overlay and calculate different aspects of the environmental factors. The results suggest that potential individual habitat patches play a small role compared to the landscape (entire corresponding habitat patches), when considering vegetation. The influence of built up areas is significant for all the species, and the proximity to the sea shore is very significant for at least one of the species, however, it is not neutral for all other species