Modelling Avian Distributions of the Navarrese Region in Northern Spain with a Geographic Information System
GIS (Geographic information Systems) technology was used for identifying relations between environmental characteristics and the breeding distributions of nine avian species of the Navarrese region in northern Spain. Data overlays of multiple GIS layers derived the explanatory variables for modelling the breeding distributions from logistic regressions. A spatial autocorrelation analysis was conducted to characterize the distribution patterns and to incorporate spatial factors (neighborhood effects) into their analysis. All nine patterns analyzed exhibited a high level of spatial autocorrelation. Accordingly, the basic hypothesis of spatial randomness was rejected in favor of spatial clustering for the sample data. The breeding distributions strongly corresponded with environmental factors on a regional scale and often with a spatial weighting function representing neighborhood effects. Inclusion of the spatial term produced a significant increase in the sensitivity of most models (i.e., their ability to correctly predict a breeding occurrence). The addition of non-climatic factors particularly improved the performance of models for forest species. Although patterns of association between the environment and avian distributions are complex and species specific, some common trends emerged. (1) Climatic variables tended to be less significant than habitat structure variables in models in which both types of variables were specified. (2) Vegetation structure was the most important environmental determinant of the breeding distributions. (3) The breeding distributions which showed the closest association with climate corresponded to species which find part of their Palearctic distribution boundary within the study area. These findings seem to suggest that although climate controls directly where the boundaries of a biogeographic distribution are, habitat is more likely to determine occurrence/absence where climatic conditions are within the permitted ecological tolerance of the species. The use of GIS, spatial autocorrelation statistics and logistic regression proved to be a valid approach to address the analysis of biogeographic distributions, as shown by the significant value of the models in predicting the avian distributions analyzed.
Biology, Geographic Information Sciences, Ornithology, Bird populations -- Spain -- Navarre; Geographic information systems
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