Climate change models foreseen up to 27% loss in crop yields in Southern Europe by 2080, being water scarcity combined with rising temperatures the major limiting factors for securing food production. Those factors mainly reduce yield during blooming through two cumulative effects: i) altering floral metabolism that impairs pollination, fertilization, seed, and fruit production and ii) reducing pollinating ecological services, that are essential to produce many fruits, vegetables, and oilseeds. While climate change directly declines pollinators, loss of floral chemical homeostasis indirectly also affects pollinators via alteration of attracting and rewarding signals, thus synergistically disrupting pollinator' ecological services, crop yield and quality. Hence, the identification of flower traits for the conferral increased crop tolerance to abiotic stresses is key to secure food production through increased plant resilience and pollinators' services. DARkWIN propose to score pollinators' preferences for flowers as a proxy of the plant well-being status and use these scores to predict plant resilience, crop yield and quality.

DARkWIN's radical new vision will use ‘Living IoT' to quantify the optimization degree of source-sink relationships through plant lifespan by analysing Genotype x Pollinator x Environment (GxPxE) interaction. Pollinator preference will identify the best performing genotypes under environmental pressure.

The radical vision that DARkWIN proposes is using bee-preferences driven by a suite of 130 million years-evolved biological sensors, to assist human-eye in identification, natural selection and redomestication of useful plant/flower traits through multi-omics approach, to secure a sustainable food production under climate change through a new plant breeding technique (NPBT) based on ecological decisions.

DARkWIN's new vision brings the technology to answer a challenging scientific question: Can we use pollinator's choices as a tool to phenotype and naturally select the best plants under environmental pressure as the cornerstone of a radically new plant phenotyping, selection, and breeding technology? An affirmative response will generate two-order of magnitude in improving current plant phenotyping, selection, and breeding approaches against climate change (and beyond) since the new vision is based on i) redomestication of unknown resilient (wild) flower traits, and ii) ecological decisions (animal sensory) acting on those traits.

Using bumblebees as natural plant ‘phenotypers' through an automated high-throughput spatial-temporal geo-positioning device that quantifies pollinator's preference, through is the automated and precise spatio-temporal quantification of ecological interactions between plants and pollinating insects to decode GxE interactions and the underlying physiological/molecular/genetic traits at flowering, which integrates cumulative effects during vegetative stage and end-point agronomical traits.