湖北省日本落叶松栽培区划
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摘要
根据湖北省温、湿度气象资料及已知日本落叶松造林点的位置信息,利用最大熵(MaxEnt)生态位模型开展日本落叶松栽培气候区划研究,旨在阐明湖北省日本落叶松栽植的气候差异格局,为日本落叶松人工林发展的布局和规划提供支持。结果表明:湖北省日本落叶松气候区划可以分为3个等级:最适宜区、较适宜区和不适宜区,其中最适宜区面积为1.35×10~4km~2,较适宜区面积为1.29×10~4km~2。适宜区域主要为鄂西大巴山东段、巫山和武陵山的部分地区,以及鄂皖交界处的大别山山脉,海拔在800～2 000 m之间;模型受试者工作特征曲线(ROC)下的面积值(AUC)达到0.927,证明预测效果较好。刀切法分析对分布预测的贡献率排名前5位的因子分别为温度季节性变异、最热月最高温度、最热季平均温度、年平均温度和温度年较差,说明影响湖北省日本落叶松适生区划分的气候因子主要为温度因子。
To clarify the basic pattern of climate difference distribution and provide references for the layout and planning of Larix kaempferi in Hubei Province,the software MaxEnt was used to classify the cultivation regions of L. kaempferi in Hubei Province with environmental data for temperature and humidity and known distribution. The results showed that the suitability degrees were divided into three categories: the most suitable region( 1.35× 10~4 km~2),suitable region( 1.29×10~4 km~2),and not suitable region. The suitable region was mainly in western Hubei,including eastern Dabashan Mountain and parts of Wushan and Wulingshan Mountain and Dabieshan Mountain at the junction of Hubei and Anhui between 800 and 2 000 m altitude. The model constructed using MaxEnt was highly reliable and evaluated using the receiver operating characteristic( AUC = 0. 927). Response curves created using the jackknife method showed that the temperature factors had an obvious influence on the distribution of L. kaempferi. Temperature seasonality,maximum temperature in the warmest month,mean temperature in the warmest quarter,annual mean temperature,and annual temperature range,which were all related to high temperature and temperature range,were the top five factors that contributed to the prediction.
To clarify the basic pattern of climate difference distribution and provide references for the layout and planning of Larix kaempferi in Hubei Province,the software MaxEnt was used to classify the cultivation regions of L. kaempferi in Hubei Province with environmental data for temperature and humidity and known distribution. The results showed that the suitability degrees were divided into three categories: the most suitable region( 1.35× 10~4 km~2),suitable region( 1.29×10~4 km~2),and not suitable region. The suitable region was mainly in western Hubei,including eastern Dabashan Mountain and parts of Wushan and Wulingshan Mountain and Dabieshan Mountain at the junction of Hubei and Anhui between 800 and 2 000 m altitude. The model constructed using MaxEnt was highly reliable and evaluated using the receiver operating characteristic( AUC = 0. 927). Response curves created using the jackknife method showed that the temperature factors had an obvious influence on the distribution of L. kaempferi. Temperature seasonality,maximum temperature in the warmest month,mean temperature in the warmest quarter,annual mean temperature,and annual temperature range,which were all related to high temperature and temperature range,were the top five factors that contributed to the prediction.
引文
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[3]朱耿平,刘国卿,卜文俊,等.生态位模型的基本原理及其在生物多样性保护中的应用[J].生物多样性,2013,21(1):90-98.
[4]RAXWORTHY C J,MARTINEZ M E,HORNING N,et al.Predicting distributions of known and unknown reptile species in Madagascar[J].Nature,2003,426:837-841.
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[6]王雷宏,杨俊仙,徐小牛.基于MaxEnt分析金钱松适生的生物气候特征[J].林业科学,2015,51(1):127-131.
[7]江晓萍,徐基良,李建强,等.基于MaxEnt生态位模型分析江西省人与野猪冲突的空间分布[J].森林与环境学报,2018,38(3):334-340.
[8]PHILLIPS S J,ANDERSON R P,SCHAPIRE R E.Maximum entropy modeling of species geographic distributions[J].Ecological Modelling,2006,190(3/4):231-259.
[9]王运生,谢丙炎,万方浩,等.ROC曲线分析在评价入侵物种分布模型中的应用[J].生物多样性,2007,15(4):365-372.
[10]欧阳林男,陈少雄,何沙娥,等.基于MaxEnt模型对大花序桉在我国南方的适生区预测[J].桉树科技,2017,34(4):1-9.
[11]崔相艳,王文娟,杨小强,等.基于生态位模型预测野生油茶的潜在分布[J].生物多样性,2016,24(10):1 117-1 128.
[12]CHEN P F.WILEY E O,MCNYSET K M.Ecological niche modeling as a predictive tool:silver and bighead carps in North America[J].Biological Invasions,2006,9(1):43-51.
[13]国家林业局调查规划设计院.日本落叶松速生丰产林:LY/T 1058-2013[S].北京:中国标准出版社,2013.
[14]胡秀,吴福川,郭微,等.基于MaxEnt生态学模型的檀香在中国的潜在种植区预测[J].林业科学,2014,50(5):27-33.
[15]田志和.日本落叶松育种学[M].北京:北京农业大学出版社,1995.