Periodic Reporting for period 2 - DARkWIN (Pollinator-assisted plant natural selection and breeding under climate change pressure)
Période du rapport: 2024-01-01 au 2024-12-31
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.
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.
The second year of the project has been mainly devoted to the achievement of technological objectives, although some scientific advances have also been produced. A main technological objective achieved has been the design, development and construction of a geo-positioning system that allows the automated quantification of pollinator preferences through the dynamic analysis of number and duration of insect visits to the flowers of individual plants. This innovative system is based on RFID technology and is composed of antennas placed on the plants, passive transponders placed on the insects, readers collecting the RFID signals in the plant x pollinator interactions, and an ad hoc developed software for the acquisition and processing of RFID data. This software also performs statistical analyses, produces graphics with the different RFID parameters that quantify the pollinator preferences for the different plants. Those parameters feed another software to assist breeding programs based on pollinator foraging decisions. Upon optimization and validation, those components have been being integrated into an experimental unit and scaled to the phenotyping platform. The design, construction and delivery of the DARkWIN phenotyping platform based on ecological interactions has been fully achieved as a main objective of the project. In this case, the ecological interaction is the mutualistic relation between the plants and the pollinators, in which the insects pollinate the plants while receiving a nutritious food from them, as a reward. The phenotyping platform is hosted by a greenhouse with six independent climatic sectors and automated fertigation system to grow until 1000 plants, of which 336 will be RFID-monitored through 42 readers connected to a computer. The platform is located at the CEBAS-CSIC experimental field, in Murcia (Spain).
A tomato mapping population has been phenotyped at the platform under control and climate change (low irrigation+high temperature), by bee choice, agronomic, physiological and morphological traits. A ranking of genotypes for resilience under water x temperature stress based on pollinator and conventional agro-physiological parameters have been established. The information above mentioned is being analysed to build models aiming at better understanding those interactions and to enable the pollinator foraging decisions as a useful tool for selecting traits of interest for breeding purposes under suboptimal environments like salinity, water scarcity and nutrient deficiencies. As a proof of concept, the development of an unprecedented set of new tomato F1 hybrids based on pollinator-driven selection of parental lines under combined drought-high temperature stress is in progress.
A tomato mapping population has been phenotyped at the platform under control and climate change (low irrigation+high temperature), by bee choice, agronomic, physiological and morphological traits. A ranking of genotypes for resilience under water x temperature stress based on pollinator and conventional agro-physiological parameters have been established. The information above mentioned is being analysed to build models aiming at better understanding those interactions and to enable the pollinator foraging decisions as a useful tool for selecting traits of interest for breeding purposes under suboptimal environments like salinity, water scarcity and nutrient deficiencies. As a proof of concept, the development of an unprecedented set of new tomato F1 hybrids based on pollinator-driven selection of parental lines under combined drought-high temperature stress is in progress.
During the second year of the project, the following main results have been obtained:
- Design, optimization and validation of a new RFID geo-positioning prototype for monitoring and quantifying plant x pollinator interactions, including data acquisition and processing software.
- New phenotyping platform for identifying floral traits at population scale enabling a new plant selection and breeding technique based on ecological decisions.
- A pollinator-assisted breeding software.
- New leaf and flower physiological and biochemical traits linking plant resilience, pollinator preferences and crop productivity under suboptimal conditions.
- Tomato mapping population phenotyped, ranking of resilience under climate change established and promising lines selected based on pollinator-related parameters.
Through re-domesticating crops and rescuing ancestral traits in new varieties, this new technology will contribute to a development of a more resilient and sustainable agriculture protecting biodiversity and ecosystems.
Once key exploitable results are being obtained (geo-positioning system and phenotyping platform services) and validated (models linking pollinator’s decisions and crop plant resilience) respect the working hypothesis, a roadmap for protection and exploitation is being established. Specific workshops have been organized with interested parties and potential customers, reaching a significant impact and interest among the scientific and the industrial (mainly plant breeding companies) sectors.
- Design, optimization and validation of a new RFID geo-positioning prototype for monitoring and quantifying plant x pollinator interactions, including data acquisition and processing software.
- New phenotyping platform for identifying floral traits at population scale enabling a new plant selection and breeding technique based on ecological decisions.
- A pollinator-assisted breeding software.
- New leaf and flower physiological and biochemical traits linking plant resilience, pollinator preferences and crop productivity under suboptimal conditions.
- Tomato mapping population phenotyped, ranking of resilience under climate change established and promising lines selected based on pollinator-related parameters.
Through re-domesticating crops and rescuing ancestral traits in new varieties, this new technology will contribute to a development of a more resilient and sustainable agriculture protecting biodiversity and ecosystems.
Once key exploitable results are being obtained (geo-positioning system and phenotyping platform services) and validated (models linking pollinator’s decisions and crop plant resilience) respect the working hypothesis, a roadmap for protection and exploitation is being established. Specific workshops have been organized with interested parties and potential customers, reaching a significant impact and interest among the scientific and the industrial (mainly plant breeding companies) sectors.