林业科学2011，Vol.47Issue(7)：20-26,7.
基于DOMAIN和NeuralEnsembles模型预测中国毛竹潜在分布
Predicting the Potential Distribution of Phyllostachys edulis with DOMAIN and NeuralEnsembles Models
摘要
Abstract
In this paper a profile technique- DOMAIN was used to map potential habitat suitable for moso bamboo ( Phyllostachys edulis). And to select the areas with low suitable habitat as pseudo-absences. Then a group discrimination technique-NeuralEnsembles was employed to predict the potential distribution of moso bamboo ( hereafter termed hybrid model) based on pseudo-absences and true presences data. Sensitivity, Kappa and the area under the curve ( AUC) values of receiver operator characteristic ( ROC ) curve were employed to assess model predictive accuracy. Meanwhile, we investigated the sample size effects of pseudo-absences generated by DOMAIN on model performance. We also compared model performance of hybrid model with single model-NeurnalEnsembles. Results indicated that the hybrid model could achieve a higher accuracy in simulating current distribution of moso bamboo in comparison to single model. Sensitivity and AUC were relatively independent from pseudo-absence sample size, but Kappa declined with the increasing pseudo-absence sample size. Climate change is likely to have dramatic effects on the potential distribution of moso bamboo, with the northward migration ranging from 33 to 266 km, and the area expansion by 7. 4% to 13. 9% .关键词
DOMAIN/NeuralEnsembles/模型耦合/潜在分布模拟/气候变化/毛竹Key words
DOMAIN/ NeuralEnsembles/ hybrid model/ potential distribution modeling/ climate change/ Phyllostachys edulis分类
农业科技引用本文复制引用
张雷,刘世荣,孙鹏森,王同立..基于DOMAIN和NeuralEnsembles模型预测中国毛竹潜在分布[J].林业科学,2011,47(7):20-26,7.基金项目
国家自然科学基金重大项目课题(30590383),林业公益性行业重大科研专项(200804001.201104006),中国林业科学研究院院所基金海外人才专项(CAFYBB2008007),“十一五”科技支撑项目(2006BAD03A04),国家科技部国际科技合作项目(2008 DFA32070)资助. （30590383）
