【作者】 王运生；

【导师】 肖启明； 戴良英； 谢丙炎； 万方浩；

【作者基本信息】 湖南农业大学 ， 植物病理学， 2007， 博士

【摘要】 随着全球经济一体化进程的不断加快,世界贸易自由化越来越成为一种趋势。以此为背景的国际大环境为外来物种的入侵、传播和扩散创造了条件。外来物种的入侵在给世界很多国家造成不可逆转的生态灾难的同时也造成了巨大的经济损失。由于我国幅员广阔,地理气候多样,其生态环境极为复杂,几乎所有的生物在我国境内均可找到适合生境,加强对外来入侵物种的防御对于保障我国动植物及人类健康来说尤为重要。为保障国际贸易的正常进行,避免将检疫措施作为变相的贸易保护主义采用的手段,世界贸易组织(WTO)就实施检疫措施作出规定,SPS协议便是这些规定中最集中的代表。我国作为WTO成员国之一,在积极采取检疫措施的同时必须注意保持与SPS协议一致,SPS协议规定允许各国采取一定的检疫措施来防止和减少随国际贸易传入生物体为害本国动植物及人类健康,但这些检疫措施必须符合国际有关标准,否则必须提供科学依据,而有害生物的风险分析是最核心的科学依据。对外来入侵物种的定量化风险评估是发展的总趋势,而目前风险的定量化主要体现在外来入侵物种的定殖风险、扩散风险及社会影响等方面,对外来入侵物种在目标地区的适生性分析是定殖风险评估的基础。由于外来入侵物种一旦定殖后将其铲除非常困难,所以不可能在目标地区进行野外试验来验证其适生性。目前一般采用相关数学模型进行适生区预测,在假设物种的生态位需求保守的前提下,可应用基于生态位理论的模型预测外来入侵物种在目标地区的适生区,从而为决策者制定相应管理措施提供技术支撑。生态位模型的基本原理是根据每种生物均有其特殊的小生境,即生态位要求,从该物种的已知分布区出发,利用数学模型归纳或模拟其生态位需求,然后将其投射到目标地区即可得到该物种的适生区分布。应用生态位模型一般需要两种数据,一个为物种现有分布信息,另一个为环境数据。物种现有分布数据可通过野外调查、查阅文献和查询相关物种分布数据库得到。各类数字化的环境数据越来越多,环境变量选择的好坏直接影响预测结果,如何选择环境变量是应用生态位模型首先要解决的一个问题。本文以生态位因子分析(ENFA)结果为基础,分析影响物种分布的主要环境因子,并以此为指导选择环境因子。模型评价是选择模型的依据,本文应用应试者工作特征曲线(ROC曲线)分析法对不同模型的预测效果进行评价,选择效果最好的模型进行最后预测,得到其适生区分布图,以Boyes指数曲线为指导并结合真阳性率进行适生图的分级显示,从而建立了基于生态位模型进行外来入侵物种风险评估的技术框架。本文应用生态化模型对相似穿孔线虫、大豆疫霉、马铃薯癌肿病菌的中国适生区进行了预测。结果表明,所选6种生态位模型:BIOCLIM、DOMAIN、ENFA、GARP、Mahalanobis和Maxent均可用来预测外来入侵物种的适生区,预测结果均能较好地与实际分布相吻合,但以Maxent模型预测效果最好,且使用方便、运行速度快。相似穿孔线虫在我国的适生区主要集中我国南部,如海南、福建、广东、广西、云南、台湾等省。大豆疫霉在我国的适生区较广,主要高风险区有黑龙江、吉林、辽宁、北京、河北、山东、汀苏、浙江、安徽、湖北、江西等省市,另外在湖南、福建、广东、广西、云南、四川、重庆、山西、内蒙等省市部分地区也适合大豆疫霉发生。在我国,马铃薯癌肿病主要分布在广西、贵州、四川、甘肃、陕西、湖北以及江苏等省区。应用生态位模型进行外来入侵物种适生区预测的一个前提条件是假设物种的生态位需求是保守的,即在原产地的生态位要求与入侵地相同或相近。但物种的生态位有时会发生漂移(nicheshift),外来入侵物种的生态位漂移现象更为突出,这主要有两个原因,一是外来入侵物种本身的快速进化作用产生了新的生态位适应机制从而影响其“基本生态位”(fundamental niche),另一方面是入侵物种在入侵地缺少天敌和竞争者和空生态位(empty niche)的存在会导致入侵物种的“实际生态位”(realized niche)发生漂移。获取外来入侵物种生态位漂移的直接证据往往很困难,本文基于生态位模型,以原产地和入侵地分布互为训练数据,建立了获取入侵物种生态位漂移的间接证据的方法。以我国区域性恶性外来入侵杂草—胜红蓟为例分析其生态位漂移现象,结果表明,胜红蓟在入侵我国后其生态位已发生部分漂移。本文所建立的“基于生态位模型进行外来入侵物种风险评估技术框架”为我国农业外来入侵生物的风险评估提供了必要的技术基础,并探讨了生态位模型在研究外来入侵物种生态位漂移现象中的应用。更多还原

【Abstract】 The world trade globalization has greatly promoted with the accelerating of the global economy integration, which provide a pathway for the alien species to invade, disseminate and spread around the world. The biological invasion has caused irreversible ecological disaster and enormous economic losses in many countries in the world. The geography and climate of China are tremendously diverse, and almost all kind of species can found suitable places to live in our country. Due to the vast, diverse ecological environment, it is particularly important to strengthen the defense against the biological invasion in our country. In order to promote the international trade and avoid using quarantine measures as a disguised mean by the trade protectionism, the World Trade Organization (WTO) has made some provisions to guarantee the measures being used appropriately, the Sanitary and Phytosanitary measures agreement (SPS) is one of the most important representatives. China, as one of WTO member, we must obey the agreement as we take active quarantine measures to protect the health of plant, animal and human in our country against the invasion of the alien species. A scientific reason must be provided to support decision when the measures did not agree with the international standards. The risk analysis is the centre of the scientific basis.The quantitative risk assessment of invasive alien species (IAS) is the developing trend all over the world, and currently this quantification is mainly embodied in the establish risk, spread risk and social impact of IAS. The suitability analysis lays the foundation of establish risk of IAS. It is very difficult to eradicate the IAS once it had successfully invaded, so we could not to test the suitability of the IAS in the novel region by introducing the species into this area. Fortunately we can predict the potential geographic distribution of IAS in the target region by using mathematical models. The niche based models were commonly employed to predict the habitat suitable maps of species, which can provide essential technical support for the policy-maker when they formulate the corresponding measures.The principle of ecological niche modeling was based on that there is a special niche - the set of environmental factors that determine where a species can or cannot maintain populations - for each specific species. We can deduce the ecological niche requirement of a species from the species’ distribution information, and then project the niche requirement onto the novel area the species never been found before to predict the suitable maps of the species. Typically, applying niche models needs two types of data, one is the distribution of the species, the other is the environmental data. The locations of species distribution can be obtained from field survey, consulting the literature and searching the occurrence database of species, such as GBIF (Global Biodiversity Information Facility). The choice of environment variables has a direct effect on the final result. We select the predict variables which determine the species geographical distribution mostly based on the ecological niche factors analysis (ENFA) results. Receive operating characteristic curve (ROC) analysis was employed to evaluate the performance of different models, and based on the assessment results we chose the best model to give the final prediction suitable maps of the IAS. The habitat suitable maps were scaled by combining the Boyes index curve and the true positive rate. Thus we proposed a framework of the risk assessment of IAS based on ecological niche modeling.Using this risk assessment framework, we analyzed the potential geographic distribution of Radopholus similis (R.similis), Phatophthora sojae (P.sojae) and Synchytrium endobioticum (S.endobioticum) in China with 6 common used niche models: BIOCLIM, DOMAIN, ENFA, GARP, Mahalanobis and Maxent. The results showed that all of the 6 models can be used to predict the distribution of species with acceptable results, but the performance of different models varied dramatically. From the cases studied in this paper, the performance of Maxent is on top of the range with easy use and fast speed. From the results predicted by the models, the suitable areas of R.similis concentrates in the south of China, such as Hainan, Fujian, Guangdong, Guangxi, Yunnan, Taiwan provinces and areas, the high risk regions of P.sojae including the Heilongjiang, Jilin, Liaolning, Beijing, Hebei, Shandong, Jiangsu, Zhejiang, Anhui, Hubei, Jiangxi provinces, the potential distribution of S.endobioticum would lie in Guangxi, Guizhou, Sichuan, Gansu, Shanxi, Hubei and Jiangsu provinces.Using the ecological niche models to predict the distribution of species, we assumed that the niche requirement of species is conserved, which means that the niche of the species in the original region is the same or very similar to that of in the invaded area. But this presumption was not always correct in all conditions, the niche of species may shift some time. The niche of IAS tends to shift due to following two reasons: one is the fundamental niche of IAS can be changed as a result of the fast evolution of invasive species, the other is the realized niche of IAS can be alternated by lacking of natural enemies and competitors, and existing lots of empty niches. It is difficult to obtain the direct evidence of niche shift of IAS by traditional experiment. We proposed a method to gain an indirect evidence of niche shift of IAS by applying the niche models. Using Ageratum conyzoides (A.conyzoides) - one of regional distress invasive weed in China - as a case study, we analyzed the niche shift phenomenal of A.conyzoides. The results showed that the niche of tropical ageratum has shifted after invaded in China.The framework of risk assessment of IAS based on niche modeling has provided an essential technical support and useful tool for us to struggle with invasive species. We also investigated the possibility of applying the niche models in studying the niche shift of IAS during the invading process.更多还原