Breeding for disease resistance or tolerance is a viable option for disease management programmes and is important for the continued success and resilience of planted forests. Red needle cast (RNC) is a disease that affects radiata pine (Pinus radiata) and is caused by Phytophthora pluvialis. Knowledge is still very limited regarding the potential for genetic tolerance to this pathogen. The application of controlled screening techniques is clearly required. Using a detached needle assay, we screened 392 clonally replicated individuals (clones) from an elite P. radiata population for quantitative tolerance to RNC. Data was highly skewed and required logarithmic data transformation and Poisson distributions for the estimation of best linear unbiased predictions. These estimates revealed a broad range in susceptibility/tolerance to RNC, and enabled the identification of clones that were clearly susceptible and clones that were clearly tolerant. There was a high correlation between the number and length of lesions that developed in response to inoculation with P. pluvialis. Broad-sense heritability estimates were low to moderate, indicating that there is potential for improving tolerance through breeding. These results provide evidence that breeding for tolerance to P. pluvialis is possible, although continued work into understanding and minimising causes for variance are required.

A simple systems model is proposed to understand and quantify the onset and epidemiology of red needle cast in radiata pine. This disease is impacting much of the New Zealand forestry estate being driven through the production of self-replicating spores which are dispersed with water. The model is at present deterministic, not spatially or age-structured, nor dependent on environmental or seasonal effects. This model shows the clear existence of calculable thresholds for disease proliferation and elimination, showing it has captured the essential components of the biological mechanisms. It is to be used to identify thresholds for infection to spread or retract. Further it will provide a base model from which we can fit and then predict experimental outcomes.