A method was developed to analyze non-independent wildlife data that addresses implications of spatial habitat selection and use. Potential gender and seasonal pattern differences were investigated. Results from Poisson process and nearest neighbor distance analysis indicated a non-random, clustered pattern of locations. Using spatial autocorrelation to account for spatial adjacency resulted in a more random spatial pattern, especially for females in winter and males in summer. These results suggest that the three methods in this study may address slightly different aspects of spatial patterns.
Spatial patterns are the result of spatial processes.
These processes can be described, measured, and evaluated using various methods of point pattern analyses. Spatial autocorrelation as a tool is well suited for studying empirical wildlife data. Wildlife observations typically occur as non-independent data. Traditionally, researchers have assumed these data to be independent or the issue of independence has been totally ignored. This is largely because the techniques for testing and accounting for autocorrelation have not been widely available.