AOD trees had relatively larger stature and the presence of stem bleeding where as COD trees had small stature and stem bleeding was absent. Tree stature and the presence of stem bleeding contributed highly to the DAI, allowing differentiation between trees with AOD and COD syndromes.

Trees with smaller crowns in poor condition had greater PDI values. Crown condition and trees size descriptors such as ‘composite crown volume’ contributed positively to the PDI. The suitability of the decline indexes for describing decline severity and type were assessed by comparing decline index scores to manual decline status classifications along with an assessment of descriptor importance and contribution to the decline index models. These decline indexes were derived from unsupervised random forest machine learning models, trained using the collected phenotypic data. Using these data, the Phenotypic Decline Index (PDI) and the Decline Acuteness Index (DAI) were developed to quantitatively describe and differentiate the acute and chronic oak decline severity spectrum. Phenotypic measurements were collected from a total of 174 Quercus robur, surveyed from 9 sites across England and included healthy, AOD, COD and AOD trees in remission. A total of 36 phenotypic measurements describing oak decline status included aspects of tree size, crown condition, the presence of biotic agents and a number of derived composite descriptors. Phenotyping protocols were developed to specifically measure oak decline severity and were based on a comprehensive set of simple to measure phenotypic descriptors. Oak decline syndromes such as Acute Oak Decline (AOD) and Chronic Oak Decline (COD) are becoming increasingly prevalent and with this comes the need for more quantitative, sensitive and standardised visual oak health monitoring.

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