LiT: Life History Responses to Genetic and Environmental Perturbations in Arabidopsis thaliana

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In order to flower during favorable conditions, plants use information from multiple environmental signals, such as day length, temperature, and winter chilling. However, global warming will alter not only temperatures, but also the timing and length of the growing season, and may thus create a mismatch between formerly accurate cues and the actual timing of those events. For example, warmer winters may not allow sufficient chilling to trigger flowering, resulting in delayed flowering despite earlier spring warming. This project will therefore explore the sensitivity of seasonal timing to genetic and environmental change. The study will take advantage of information and tools available for the genetic model species Arabidopsis thaliana, a weedy annual relative of crops such as canola, cabbage, broccoli, turnips, and mustard. It will combine a predictive mathematical model with field and controlled chamber experiments. The research will ask: 1. How does genetic variation in winter chilling requirements affect flowering time in natural seasonal environments? 2. How does natural selection act upon this variation in natural environments? 3. How will higher temperatures predicted by global warming scenarios affect seasonal timing of flowering? Predictions of the model will be tested experimentally in controlled chamber environments simulating effects of warming temperatures predicted for the end of the 21st century. If the model is accurate, warming temperatures and reduced winter chilling will alter seasonal timing and the relative timing of different genetic variants. Accurate timing of plant seasonal events - such as leaf emergence, flowering, fruiting and senescence - is critical for crop and forestry yields, as well as for the survival of wild species in the face of climate change. The results of this study will therefore be important for predicting how crops, weeds, forest trees, and wild plant communities will respond to global warming. The modeling approach will be a significant advance from traditional crop models and may improve their generality and predictive ability. This project will provide career opportunities and training for two female scientists: a postdoctoral researcher and a Ph.D researcher returning to the workforce after a career interruption. It will also provide research opportunities to several undergraduates, and will support development of a classroom module on plant responses to climate change for Rhode Island high school teachers.