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基于MAXENT和ZONATION的加拿大一枝黄花入侵重点监控区确定
引用本文:李丽鹤,刘会玉,林振山,贾俊鹤,刘翔.基于MAXENT和ZONATION的加拿大一枝黄花入侵重点监控区确定[J].生态学报,2017,37(9):3124-3132.
作者姓名:李丽鹤  刘会玉  林振山  贾俊鹤  刘翔
作者单位:南京师范大学地理科学学院, 南京 210023;江苏省地理环境演化国家重点实验室培育建设点, 南京 210023;虚拟地理环境教育部重点实验室(南京师范大学), 南京 210023;江苏省地理信息资源开发与利用协同创新中心, 南京 210023,南京师范大学地理科学学院, 南京 210023;江苏省地理环境演化国家重点实验室培育建设点, 南京 210023;虚拟地理环境教育部重点实验室(南京师范大学), 南京 210023;江苏省地理信息资源开发与利用协同创新中心, 南京 210023,南京师范大学地理科学学院, 南京 210023;江苏省地理环境演化国家重点实验室培育建设点, 南京 210023;虚拟地理环境教育部重点实验室(南京师范大学), 南京 210023;江苏省地理信息资源开发与利用协同创新中心, 南京 210023,南京师范大学地理科学学院, 南京 210023;江苏省地理环境演化国家重点实验室培育建设点, 南京 210023;虚拟地理环境教育部重点实验室(南京师范大学), 南京 210023;江苏省地理信息资源开发与利用协同创新中心, 南京 210023,南京师范大学地理科学学院, 南京 210023;江苏省地理环境演化国家重点实验室培育建设点, 南京 210023;虚拟地理环境教育部重点实验室(南京师范大学), 南京 210023;江苏省地理信息资源开发与利用协同创新中心, 南京 210023
基金项目:国家自然科学基金项目(31470519,31370484);江苏省自然科学基金项目(BK20131399);江苏省高校优势学科建设工程资助项目
摘    要:外来入侵植物对本地生态系统及其生物多样性构成严重的威胁,要有效地控制外来植物入侵,首先应该明确植物入侵的高度风险区.以加拿大一枝黄花(Solidago canadensis)为对象,以其广泛发生的安徽、江苏、浙江和上海华东3省1市为研究区域,综合考虑了土地利用变化、人类活动干扰、土壤性质、气候和地形等影响因子,采用MAXENT模型预测其潜在分布及其对主要影响因子的响应,并结合空间优化软件ZONATION识别出需要重点布控的入侵风险区。结果表明:1)影响加拿大一枝黄花分布的主要环境因子及其百分比贡献率分别为:距主要道路距离(29.4%)、土地利用变化(16.9%)、降水的季节性变异(15.9%)、人口密度(9.5%)与最干季均温(6.2%)。2)从影响因子的响应曲线分析得出,加拿大一枝黄花的发生概率随着距主要道路距离的增大而迅速减小;在耕地转化成的城乡居民点及工矿用地、水域转化成的草地、城乡居民点及工矿用地转化成的林地、草地与城乡居民点及工矿用地相互转换频繁的区域和城乡居民点及工矿用地保持不变的区域,其发生概率明显较高;其发生概率随着降水季节性变异的增大而快速减小至0.4,之后缓慢减小;随着人口密度的增大,其发生概率起初急剧升高,人口密度超过4千人/km~2后又缓慢地小幅下降;随着最干季均温的增大,其发生概率逐渐减小,在2.4℃附近达最小,之后逐渐增大。3)加拿大一枝黄花的入侵风险区面积为130433 km~2。其中,一级风险区主要分布在太湖流域、沿杭州湾地区、浙江沿海以及内陆地势较低的耕地及居民点区域;二级风险区主要分布在一级风险区的外缘,尤其是江苏南部的长江沿岸地区。三级风险区则广泛分布在江苏的南部和东部,安徽的中东部,浙江的北部和东部。

关 键 词:加拿大一枝黄花  MAXENT  风险预测  响应  ZONATION
收稿时间:2016/1/26 0:00:00
修稿时间:2016/10/17 0:00:00

Identifying priority areas for monitoring the invasion of Solidago canadensis based on MAXENT and ZONATION
LI Lihe,LIU Huiyu,LIN Zhenshan,JIA Junhe and LIU Xiang.Identifying priority areas for monitoring the invasion of Solidago canadensis based on MAXENT and ZONATION[J].Acta Ecologica Sinica,2017,37(9):3124-3132.
Authors:LI Lihe  LIU Huiyu  LIN Zhenshan  JIA Junhe and LIU Xiang
Institution:College of Geography Science, Nanjing Normal University, Nanjing 210023, China;State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China;Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China;Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China,College of Geography Science, Nanjing Normal University, Nanjing 210023, China;State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China;Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China;Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China,College of Geography Science, Nanjing Normal University, Nanjing 210023, China;State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China;Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China;Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China,College of Geography Science, Nanjing Normal University, Nanjing 210023, China;State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China;Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China;Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China and College of Geography Science, Nanjing Normal University, Nanjing 210023, China;State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China;Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China;Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Abstract:The invasion of alien plants poses serious threats to local ecosystems and biodiversity. To control plants invasion effectively, the relative importance of influencing factors and the areas with high invasion risk must be identified. In the present study, MAXENT was applied to simulate the potential distribution of Solidago canadensis and explore its response to major impact factors in east China including Anhui, Jiangsu, Zhejiang and Shanghai. This was based on occurrence records and environmental factors including land use change, human disturbance, soil characteristics, climate, and topography. ZONATION was combined with the results from MAXENT to identify priority areas with high invasion risks for monitoring. The results showed that (1) the five most important factors influencing the distribution of S. canadensis were the distance to major roads (29.4%), land use change (16.9%), precipitation seasonality (15.9%), population density (9.5%), and mean temperature of the driest quarter (6.2%), respectively; (2) the occurrence probability of S. canadensis decreased rapidly with increased distance from major roads. The occurrence probability was dramatically higher in areas where cropland was transformed to construction land, aquatic areas to grassland, construction land to woodland; mutual conversion between grassland and construction land occurred; and construction land remained unchanged. As precipitation seasonality increased, the probability initially decreased quickly and then slowly. With an increase of population density, the occurrence probability initially increased rapidly and then decreased very slowly. As the mean temperature of the driest quarter increased, the probability decreased initially and then increased gradually; (3) the area of invasion risk of S. canadensis was 130,433 km2. The primary risk area was mainly distributed in the Taihu basin, the area surrounding Hangzhou Bay, and the Zhejiang coast and its inland crop-and construction land. The secondary risk area was mainly distributed outside the primary risk area, particularly along the Yangtze River in southern Jiangsu. The third-level risk area was widely distributed in the south and east of Jiangsu, the middle and east of Anhui, and the north and east of Zhejiang.
Keywords:Solidago canadensis  MAXENT  invasion risk prediction  response  ZONATION
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