Polli-markers: spectral and chemical biomarkers for detecting cryptic early plant pollination responses

Pollination is essential for plant reproduction, ecosystem resilience and human health. Yet, our capability to map pollination service delivery in real-time across large areas remains poor. Determining where and when flowers are pollinated is vital to mitigate widespread pollination deficits, increase plant health and yield, and support pollinator management. Hence, innovative approaches are urgently needed for establishing scalable predictive bioindicators of plant pollination status with the goal of achieving real-time landscape-scale monitoring. Here we present two parallel controlled pollination assays in which we characterise the post-pollination petal physiology of a world leading flowering crop, Brassica napus, using in-situ close-range hyperspectral reflectance and semi-untargeted metabolomics. This multiomics approach coupled with supervised machine learning and biomarker detection reveals cryptic changes in the UV petal reflectance spectrum which are predictive of pollination status, representing a novel set of candidate pollination bioindicators (‘polli-markers’), and our high-resolution time series enables prediction of when this pollination event occurred. It also reveals an associated set of candidate metabolites, including flavonoids and senescence markers, shedding light on the functional pathways related to our polli-markers. This study provides key insights into floral development, enabling a transformative step towards predicting, mapping and quantifying pollination service delivery at the landscape scale.