Submitted by Benjamin Blackman on
Flowering and reproduction are highly regulated processes in composite plants like sunflower which produce disks that are clusters of many individual flowers. Environmental cues like light and temperature interact with the circadian clock regulate what time of the season buds first start to develop, and the same integration of internal and external signals likely occurs as new whorls individual florets open daily to present pollen and receptive stigmas at reproductive maturity. We are working to understand the molecular mechanisms underlying this process by mapping genetic changes segregating among cultivated sunflower lines that alter the timing of these events. To do so and to obtain information about pollinator visitation, we are collecting time-lapse video data for a large genetic mapping cross of wild sunflowers that we are growing in this year in Davis. Students will be involved in scoring these images for traits, and while the fieldwork is still ongoing, there may be opportunities to assist with data collection and other aspects of the project in the field depending on their schedule. There is also potential opportunity for students who have savvy with machine learning / image analysis / pattern recognition software to develop methods for automated scoring of these data.
The undergraduate researchers will contribute to the scoring of floral time-lapse image data collected in the field. The students may also have the opportunity to score additional traits on sunflowers out in field conditions or be involved in DNA extractions of tissue collected for genotyping. They may also develop methods for automated image analysis of pollinator visitation. The student is encouraged to join weekly Blackman lab group meetings as well.
Students with strong interests in plant-environment interaction, evolution, and ecology will find the experience most rewarding. Attention to detail and good record keeping skills are essential. The student should be comfortable and enthusiastic about intermittently working in greenhouse, growth chamber, or field conditions for extended periods, and they will be expected to follow guidelines for safely doing so. Students with experience in image analysis / machine learning / computer programming and interested in applying or developing tools for pollinator visitation video scoring should note that in their applications.