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.
Despite the increasing relevance of flowers in sensing the environmental stress, plant phenotyping platforms aim at identifying genetic traits of resilience and selecting the best individuals by assessing the physiological status of the plants, usually through remote sensing-assisted vegetative indexes, but find strong bottlenecks in quantifying flower traits and in accurate genotype-to-phenotype prediction, and therefore impairing the success of the breeding process and the delivery of crop varieties with enhanced resistance.
However, as the transport of the energetic compounds produced by photosynthesis from the leaves (sources) to the flowers (sinks) is reduced in low-resilient plants, flowers are better indicators than leaves of plant well-being. Indeed, the chemical composition of flowers changes in response to heat and drought, as it does the amount of pollen and nectar that flowers produce, which ultimately serve as food resources for the pollinators.
The DARkWIN project proposes to track and rank pollinators’ preferences for flowers of a tomato population that allows the genetic mapping of traits of resistance when the plants are exposed to heat and drought. The preferences of the insects will serve as a measure of functional source-to-sink chemical relationships that benefit the tolerance of the plant and ultimately the crop yield. To achieve this goal, DARkWIN is developing a pollinator-assisted phenotyping and selection platform for automated quantification of Genotype x Pollinator x Environment interactions through a bumblebee geo-positioning system based on Radio-Frequency Identification technology. Pollinator-assisted selection for agriculture is being validated by a multi-omics dataset of unprecedented dimensions in a population of tomato breeding lines, including floral metabolic, transcriptomic, and ionomic traits, as well as mapping candidate genes, linking floral traits, pollinator preferences, and plant resilience. Moreover, DARkWIN will deliver tomato F1 pre-commercial varieties based on the natural biological process of pollinator driven selection under climate change conditions.
This radical new approach can change the current paradigm of plant phenotyping and selection, and find new paths for crop breeding assisted by ecological decisions.
