Maize Pollen Dispersal Model: Using Nonlinear, Quasi-mechanistic Models and Neural Networks to Evaluate the Recommended Isolation Distance for Coexistence between GM and Non-GM Crops in Taiwan

Abstract

在過去的二十年，許多基因改造 (genetically modified, GM) 作物已被成功開發上市，並進入商業化的田間生產。種植在開放田間的基因改造作物，可能與野生近緣種或傳統非基改品種的作物發生雜交，導致基因流和基因污染。由於花粉傳播是影響玉米基因流的主要因素，瞭解在田野中發生異交 (cross-pollination,CP) 的程度，可以幫助建立基因改造與非基因改造作物間，共存的隔離距離和田區種植的配置。本研究於 2009 至 2011 年間，在台灣本土進行實際玉米田間試驗，考慮七種不同的場景佈局，調查田間種植時玉米花粉流的程度。試驗田區設置位於台中霧豐的農業試驗所 (Taiwan Agricultural Research Institute, TARI) (24°1'N,120°41'E)，及位於嘉義朴子的台南區農業改良場 (Tainan District Agricultural Research and Extension Station , TNDAIS) 朴子分場 (23°47'N, 120°26'E)。利用此本土田間試驗的異交率資料，以及數種非線性和擬機械模式，建立花粉接受親與花粉貢獻親間之距離與異交程度的關係。此外，在模式建立同時，也採用重複抽樣的技術，進行模式的評估及驗證。在第三章中，NIG (normal inverse Gaussian)模式在所使用的二維與一維模式中，擁有最好的建模表現。根據保守的結果，田區模擬的異交汙染率在 95%信心水準，小於 0.9%的情況下 (kernel-number-based量測法)，其所需的距離分別為 NIG 模式的 25 公尺。此外，在 95%信心水準小於 3%與 5%的情況下 (kernel-number-based 量測方法)，其所需的距離為 NIG 模式的 15 與 10 公尺。在第四章中，我們使用三種具有不同的輸入的類神經網絡 (artificial neural network, ANN）模型建立花粉飄散模式：原始的輸入神經網絡 (neural network,NN)、主成分神經網絡 (principal components neural network, PCNN)、淨最小平方 神經網絡 （partial least squared neural network, PLSNN）模型。根據 ANN 模型田區 模 擬 結 果 ， 異 交 汙 染 率 在 95% 信 心 水 準 小 於 0.9% 的 情 況 下(kernel-number-based 量測法)，其所需的距離分別為 NN 模型的 40 公尺、PCNN模型與 PLSNN 模型的 35 公尺。在小於 3%的情況下 (kernel-number-based 量測 法)，其所需的距離分別為在 NN 模式與 PCNN 模式的 30 公尺、以及 PLSNN 模式的 35 公尺。在 95%信心水準小於 5%的情況下 (kernel-number-based 量測法)，其所需的距離分別為在 NN 模式與 PLSNN 模式的 30 公尺、以及 PCNN 模式的25 公尺。此外，我們亦發展了一套方法，可應用於解釋 ANN 模型的黑盒子，根據本研究的所建立的 3 種 ANN 模型，其模型訓練後的記憶特徵可藉由因素分析(factor analysis) 的結果進行說明與解釋。綜合各項結果，同時考慮模型效率和保守的預測隔離距離，本研究建議在大多數的情況下，在 95%信心水準下，30 公尺、35 公尺、40 公尺的隔離距離即分別足以維持非基因改造玉米田區收穫物，基改基因污染率小於 5%、3%與 0.9%的門檻。In the past two decades, numerous genetically modified (GM) crops have been successfully developed and released into commercial production. GM crops planted in an open field can outcross with feral or conventional crops, leading to gene flow and gene contamination. Because pollen dispersal is the major factor that affects gene flow in maize, the exploration of cross-pollination (CP) in the field may facilitate the establishment of the isolation distance and field arrangements to allow the coexistence of GM and non-GM crops. In this study, we conducted local field experiments to measure pollen flow in maize by using seven scenarios of diverse field-layout patterns during 2009 and 2011. The experimental fields were located at Wufeng (24°1'N, 120°41'E) managed by the Taiwan Agricultural Research Institute (TARI) and at Puzih (23°47'N, 120°26'E) belonging to the Potzu Branch Station of the Tainan District Agricultural Research and Extension Station (TNDAIS). The CP rates collected from local maize field trials were used to establish the relationship between the extent of CP in recipient and distances from the pollen source by using several nonlinear and quasi-mechanistic models. In addition, the resampling method was used for both evaluating and verifying the models. The normal inverse Gaussian (NIG) model had a better performance among the 2D and 1D model. According to the conservative result, the 95% upper confidence limits of simulated CP rates were below 0.9% (kernel-number-based approach) at the isolation distance of 25 m on the basis of the NIG model. Additionally, the simulated CP rates were <3% and <5% beyond 15 m and 10 m for NIG model at a 95% confidence level, respectively. In chapter 4, we used three artificial neural network (ANN) models featuring distinct inputs to establish the pollen dispersal models: the neural network (NN), principal components neural network (PCNN), and partial least squared neural network (PLSNN) models. Our analyses revealed that the 95% upper confidence limits of the CP rate (as per the kernel-number-based approach) in field simulations were <0.9% at the isolation distances of 40 m, 35 m, and 35 m predicted using the NN, PCNN, and PLSNN models, respectively. The simulated CP rates were <3% at the isolation distances of 30 m, 30 m, and 35 m predicted by NN, PCNN, and PLSNN, respectively. The simulated CP rates predicted using the NN, PCNN, and PLSNN models were <5% at a 95% confidence level at distances greater than 30 m, 25 m, and 30 m, respectively. Furthermore, we developed a technique to interpret the black box of ANN model, and the result showed that various characteristics of the data can be explained by using factor analysis in 3 ANN models. In conclusion, to consider both model performance and conservative isolation distance in most cases, a recommended isolation distances of 30 m, 35 m, and 40 m are adequate for maintaining the GM contamination rate of the total harvest of a field at <5%, <3%, and <0.9% at a 95% confidence level, respectively.CHAPTER 1 COEXISTENCE BETWEEN GM AND NON-GM MAIZE ......1 1.1 INTRODUCTION........................1 1.2 FIELD EXPERIMENTAL STUDIES .....................2 1.3 MODELING APPROACH ..............................4 1.4 MEASUREMENT UNITS FOR GENETICALLY MODIFIED ORGANISMS.............................................5 1.5 COEXISTENCE MEASURES AND MANAGEMENT...................5 1.6 MAIZE IN TAIWAN .........................8 CHAPTER 2 FIELD EXPERIMENT ON CROSS-POLLINATION OF MAIZE IN TAIWAN ......................... 17 2.1 INTRODUCTION..................17 2.2 MATERIALS AND METHODS ...........................17 2.2.1 Experimental fields .............................17 2.2.2 Meteorological data.............................18 2.2.3 Sampling method........................... 19 2.2.3.1 Scenario W1................................19 2.2.3.2 Scenario W2.................................19 2.2.3.3 Scenarios W3 and W4 .............................20 2.2.3.4 Scenarios P1, P2, and P3.........................20 2.2.4 CP rate and distance definition..............20 2.3 RESULTS AND DISCUSSION ..............................21 2.3.1 Experimental scenarios.....................21 2.3.2 Extent of observed CP rate.........................26 2.4 CONCLUSION ...............................28 CHAPTER 3 USING EMPIRICAL AND QUASI-MECHANISTIC MODELS TO ASSESS THE CROSS-POLLINATION OF MAIZE BY POLLEN-MEDIATED GENE FLOW AT A FIELD SCALE...................................60 3.1 INTRODUCTION.........................60 3.2 MATERIALS AND METHODS ...........................60 3.2.1 Fit models ..................................60 3.2.2 Model evaluation..................................65 3.2.3 Evaluation criteria .........................66 3.3 RESULTS AND DISCUSSION .............................68 3.3.1 Meteorological data and experimental sites .....................................68 3.3.2 Parameter estimation ...........................69 3.3.3 Model selection...............................72 3.3.4 Field simulation of the NIG models ................74 3.4 CONCLUSION ....................................76 CHAPTER 4 DETERMINING AND INTERPRETING THE EXTENT OF MAIZE CROSS-POLLINATION AT A FIELD SCALE BY USING ARTIFICIAL NEURAL NETWORKS ................................... 97 4.1 INTRODUCTION.....................97 4.2 MATERIALS AND METHODS ...........................98 4.2.1 Artificial neural network models and input variables.........................................98 4.2.2 Resampling simulation..........................101 4.2.2.1 Model evaluation ........................... 101 4.2.2.2 Model interpretation ...........................101 4.2.3 Interpretation of input variables in artificial neural network models ............................ 101 4.2.3.1 Sensitivity analysis of input variables..........102 4.2.3.2 The black box of weight and bias ...............102 4.2.4 Simulation analysis ........................... 104 4.2.4.1 Single recipient point ..........................104 4.2.4.2 Total harvest of recipient field ...............105 4.3 RESULTS AND DISCUSSION ..............................105 4.3.1 Model selection....................106 4.3.2 Factor analysis and interpretation of artificial neural network models......................... 108 4.3.2.1 Neural network model ...........................110 4.3.2.2 Principal components neural network model .......112 4.3.2.3 Partial least squares neural network model ......113 4.3.3 Field simulation ..............................116 4.3.3.1 Single recipient point.................... 116 4.3.3.2 Whole recipient field ...........................119 4.4 CONCLUSION .......................................122 CHAPTER 5 CONCLUDING REMARKS AND FUTURE WORKS ....................161 5.1 CONCLUDING REMARKS.......................... 161 5.1.1 Filed experiment .................................161 5.1.2 Nonlinear and quasi-mechanistic models ............162 5.1.3 Artificial neural network models ..............162 5.2 COEXISTENCE BETWEEN GM AND NON-GM MAIZE IN TAIWAN............................................. 164 5.3 FUTURE WORKS.....................166 REFERENCES ................................168 APPENDIX A.............................180 APPENDIX B........................... 189 APPENDIX C .............................21