Characterizing spatial-temporal tree mortality patterns associated with a new forest disease

Abstract

A new forest disease called Sudden Oak Death, caused by the pathogen Phytophthora ramorum, occurs in coastal hardwood forests in California and Oregon. In this paper, we analyzed the spatial-temporal patterns of overstory oak tree mortality in China Camp State Park, CA over 4 years using the point patterns mapped from high spatial resolution remotely sensed imagery. Both univariate and multivariate spatial point pattern analyses were performed with special considerations paid to the spatial trends illustrated in the mapped point patterns. In univariate spatial point pattern analyses, we investigated inhomogeneous K-functions and Neyman-Scott point processes to characterize and model the spatial dependence among dead oak trees in each year. The results showed that the point patterns of dead oak trees are significantly clustered at different scales and spatial extents through time; and that both the extent and the scale of the clustering patterns decrease with time. In multivariate spatial point pattern analyses, we developed two simulation methods to test the spatial-temporal dependence among dead oak trees over time and the spatial dependence between dead oak trees and California bay trees, an important host for the pathogen. The results showed that new dead oak trees tend to be located within up to 300 m of past dead oak trees; and that a strong spatial association between oak tree mortality and California bay trees exists 150 m away.