High-Resolution Gene Flow Model for Assessing Environmental Impacts of Transgene Escape Based on Biological Parameters and Wind Speed

Increased pollen source area does not always enhance the risk of pollen dispersal and gene flow in Oryza sativa L

Pollen dispersal is one of the main ways of gene flow. In the past years, rice pollen dispersal and gene flow have been well studies. However, there is much dispute whether the risk of pollen dispersal and gene flow continuously increases with the source area. A Lagrangian stochastic model was used to simulate the pollen depositions at different distances from different pollen source areas. The field experiments showed a good fit in the pollen depositions. The larger the source area, the more the pollen grains were deposited at each distance, with the pollen dispersal distance increasing accordingly. However, this effect gradually leveled off as the source area increased. In the large-area of pollen source, we found a significantly higher saturation point for the amount of pollen deposition. Once the source area exceeded 1000 × 1000 m2, the pollen deposition no longer increased, even if the source area continued to increase, indicating the "critical source area" of rice pollen dispersal. However, a 100 × 100 m2 critical source area for conventional rice and hybrid rice was sufficient, while the critical source area for the sterile line was about 230 × 230 m2.

Model-based calculating tool for pollen-mediated gene flow frequencies in plants

The potential social-economic and environmental impacts caused by transgene flow from genetically engineered (GE) crops have stimulated worldwide biosafety concerns. To determine transgene flow frequencies resulted from pollination is the first critical step for assessing such impacts, in addition to the determination of transgene expression and fitness in crop-wild hybrid descendants. Two methods are commonly used to estimate pollen-mediated gene flow (PMGF) frequencies: field experimenting and mathematical modeling. Field experiments can provide relatively accurate results but are time/resource consuming. Modeling offers an effective complement for PMGF experimental assessment. However, many published models describe PMGF by mathematical equations and are practically not easy to use. To increase the application of PMGF modeling for the estimation of transgene flow, we established a tool to calculate PMGF frequencies based on a quasi-mechanistic PMGF model for wind-pollination species. This tool includes a calculating program displayed by an easy-operating interface. PMGF frequencies of different plant species can be quickly calculated under different environmental conditions by including a number of biological and wind speed parameters that can be measured in the fields/laboratories or obtained from published data. The tool is freely available in the public domain (http://ecology.fudan.edu.cn/userfiles/cn/files/Tool_Manual.zip). Case studies including rice, wheat, and maize demonstrated similar results between the calculated frequencies based on this tool and those from published PMGF data. This PMGF calculating tool will provide useful information for assessing and monitoring social-economic and environmental impacts caused by transgene flow from GE crops. This tool can also be applied to determine the isolation distances between GE and non-GE crops in a coexistence agro-ecosystem, and to ensure the purity of certified seeds by setting proper isolation distances among field production plots.