A multi-scale spatial analysis method for point data

This paper presents a nearest neighbor method for the spatial analysis of data collected from discrete field sampling sites. The method was applied to point counts of birds at permanent survey sites in the Nicolet National Forest of northeastern Wisconsin. The spatial analysis method we developed uses a Monte Carlo randomization approach to test for non-randomness not only of the mean nearest neighbor distance between n points but also the mean second nearest, third nearest,..., to (n–1)th nearest distances to reveal spatial information at multiple scales. Because the bird survey sites are not randomly distributed throughout the forest, the survey sites at which a given species was recorded were compared with random samples drawn from the total survey sites rather than from all possible points within the forest. More refined analyses restricted the randomization by (a) habitat type, in order to separate the effects of non-randomly distributed habitat types on species' distributions; and (b) north-south regions of the forest, in order to account for regional gradients in distribution which were evident for some species. Spatial patterns among the sites at which the birds were detected reveal information about the scale at which the birds are distributed in their environment and provide a more complete picture of multi-scale bird population dynamics.