Phenotypic Index Measures for Oak Decline Severity
Agrilus exit hole density (m^-2)
Estimated bleed prevalence (%)
Calculate Decline Indexes
Crown condition
Crown production efficiency
Crown surface area (m^2)
Estimated crown volume (m^3)
Descriptor contributions
Live crown ratio
Make analysis table
Multidimensional scaling
Min-max scaling
Phenotyping template
Prepare phenotype data
Read phenptyping sheet
Objects exported from other packages
Random forest analysis
Site adjustment
Site adjustment factors
Oak declines are complex disease syndromes and consist of many visual indicators that include aspects of tree size, crown condition and trunk condition. This can cause difficulty in the manual classification of symptomatic and non-symptomatic trees from what is in reality a broad spectrum of oak tree health condition. Two phenotypic oak decline indexes have been developed to quantitatively describe and differentiate oak decline syndromes in Quercus robur. This package provides a toolkit to generate these decline indexes from phenotypic descriptors using the machine learning algorithm random forest. The methodology for generating these indexes is outlined in Finch et al. (2121) <doi:10.1016/j.foreco.2021.118948>.