Characterization of three-dimensional spatial aggregation and association patterns of brown rot symptoms within intensively mapped sour cherry trees.

Background And Aims: Characterization of spatial patterns of plant disease can provide insights into important epidemiological processes such as sources of inoculum, mechanisms of dissemination, and reproductive strategies of the pathogen population. Whilst two-dimensional patterns of disease (among plants within fields) have been studied extensively, there is limited information on three-dimensional patterns within individual plant canopies. Reported here are the detailed mapping of different symptom types of brown rot (caused by Monilinia laxa) in individual sour cherry tree (Prunus cerasus) canopies, and the application of spatial statistics to the resulting data points to determine patterns of symptom aggregation and association.

Methods: A magnetic digitizer was utilized to create detailed three-dimensional maps of three symptom types (blossom blight, shoot blight and twig canker) in eight sour cherry tree canopies during the green fruit stage of development. The resulting point patterns were analysed for aggregation (within a given symptom type) and pairwise association (between symptom types) using a three-dimensional extension of nearest-neighbour analysis.

Key Results: Symptoms of M. laxa infection were generally aggregated within the canopy volume, but there was no consistent pattern for one symptom type to be more or less aggregated than the other. Analysis of spatial association among symptom types indicated that previous year's twig cankers may play an important role in influencing the spatial pattern of current year's symptoms. This observation provides quantitative support for the epidemiological role of twig cankers as sources of primary inoculum within the tree.

Conclusions: Presented here is a new approach to quantify spatial patterns of plant disease in complex fruit tree canopies using point pattern analysis. This work provides a framework for quantitative analysis of three-dimensional spatial patterns within the finite tree canopy, applicable to many fields of research.