Nonlinear Regression Applications in Modeling Over-Dispersion of Bird Populations

Abstract

The aim of this study was to statistically evaluate bird populations in Akdogan Lakes by means of Poisson and negative binomial regression models. The over-dispersion value in Poisson regression was much higher than 1.0 (33.827). In contrast, the value of over-dispersion in the negative binomial regression was very close to 1.0 (1.598). Therefore, the parameter estimates were interpreted considering the negative binomial regression. When spring season was considered as a reference parameter, the change in population densities in other seasons was not statistically significant. The population changes in other habitats were not statistically significant, when reed area was considered as a reference parameter. The change in the population density of 13 ordo groups is non significant when the Anseriformes order was evaluated as reference parameter. The population change in the Gruiiformes population was 11.951 times higher compared with the change in reference parameter and the change was statistically significant (p <0.01). As a result, it is recommendable to use negative binomial regression with the scope of removing over-dispersion problem in the bird population modeling.