REU: Using Mathematical Models of Pollinator Movement and Pollen Deposition to Predict Gene Flow in Plants

点击翻译按钮获取中文摘要

The spatial pattern of pollen dispersal in insect-pollinated plants affects both plant reproductive success and the rate at which novel or introduced genes will spread through plant populations. Currently however, there is a lack of ability to combine information about pollinator movement and pollen deposition to make quantitative predictions about pollen dispersal. The proposed research will develop quantitative tools for predicting gene flow in plants using partial differential equation (P.D.E.) models of pollinator movement and pollen deposition. Field experiments designed to measure the effect of plant spatial arrangement on pollinator movement behavior will be conducted using the mustard Brassica campestris and its three major insect pollinators. The results of these experiments will then be used to build P.D.E. models of insect movement. Additional experiments will utilize a marker gene to measure the rate of pollen deposition as pollinators visit a sequence of mustard plants. Finally, information about insect movement and pollen deposition will be integrated to generate expected pollen deposition patterns for a variety of plant arrangements. The expected patterns will be compared to the observed dispersal of a marker gene in field experiments conducted by Drs. Peter Kareiva and Robin Manasse at the University of Washington. The results of this study will be important in understanding the spatial structure of genetic neighborhoods and the effect of plant density dispersion on plant reproductive success, and in predicting and controlling the spread of introduced genes.