稀疏群团状物种分布模型和抽样技术的研究
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摘要
物种分布与环境关系的研究、物种分布的模拟和生物多样性分布格局的模拟,在全球变化、生物多样性监测、保护和评价以及外来种入侵等方面都具有重要的理论和实践意义。然而,无论是植物种与环境的关系分析,还是其潜在分布区的模拟及未来分布趋势的预测,最基本的、也是最重要的工作应该是对物种分布现状的调查和记录。
森林资源的调查,特别是对于稀疏群团状分布的森林资源,如珍稀乔木树种、灌木、草本及濒临灭绝的动物等的调查,比较难执行,而且一般的调查方法效率不高。为了提高稀疏群团状分布资源调查的精度和效率,国内外应用自适应群团抽样调查技术和方法进行调查。对稀疏群团分布的物种进行调查,自适应群团抽样也存在一些问题,例如,在调查前人们事先无法知道稀疏群团物种的分布状况。因此,研究如何简便快捷的调查稀疏群团物种资源的分布,具有非常重要的现实意义。
物种分布模型可以利用物种已知的或是未知的分布点与其对应的环境因子的关系,预测物种的所有可能的分布状况。自适应群团抽样与物种分布模型相结合,应用物种分布模型事先对物种的分布状况进行预测,使自适应群团抽样调查技术更具有针对性和实用性。因此,研究物种分布模型和抽样技术的结合具有重要的意义。
本论文以内蒙古磴口县巴彦高勒镇乌兰布和沙漠边缘地区为研究区,选取沙枣(Elaeagnus angustifolia)、梭梭(Haloxylon ammodendron)、白刺(Nitraria tangtorum)和柽柳(Tamarix chinensis)研究对象,研究了四个物种的空间分布格局。使用研究区域的实际调查数据和遥感数据,选择物种分布模型MaxEnt模型和GARP模型进行了应用研究,并提出了物种分布模型和抽样技术相结合的应用研究方法。在物种分布模型的研究中,主要是研究了影响物种分布模型精度的几个因子。其中,在MaxEnt模型研究中,主要研究了训练数据和检验数据的比例、改变环境变量、阈值、样本量、样方大小以及物种等因子对MaxEnt模型预测精度的影响;在GARP模型研究中,研究了样本量、样方大小以及物种对其预测精度的影响。最后比较研究了MaxEnt模型和GARP模型。在研究物种分布模型和抽样技术时,研究了物种存在的概率,并提出了在物种分布模型的指导下,对于分布稀疏群团状分布的物种所应选择的抽样方法。主要研究结论和创新点如下:
主要创新点为:
1、模拟研究了多种因子对物种分布模型的预测精度的影响。
2、提出抽样技术和物种分布模型结合起来进行研究的方法,利用物种分布模型预测的结果指导抽样调查的实施。
主要结论为:
1、研究了样本量、样方大小、物种和环境变量对物种分布模型的精度影响,结果表明:样方为10×10m时,MaxEnt物种分布模型选择的样本量为100预测精度最好,GARP选择的样本量为150预测精度最高;这两个模型的预测精度都是在样方大小和分辨率比较近时比较大,样方大小和分辨率相差的比较大时,预测精度比较低;MaxEnt和GARP物种分布模型都是对物种沙枣和柽柳的预测结果比较好,对物种白刺和梭梭的预测结果稍差一些。证明了物种的聚集程度不同,得出的预测精度不同;环境变量的选择影响MaxEnt物种分布模型的预测稳定性和精度,选择的变量越多,可以得出的精度越高。
2、基于研究区域和样方为10×10m的设计,物种沙枣和柽柳的预测的样方物种存在概率为p≥0.55时,表示为物种存在;物种梭梭的预测的样方物种存在概率为p≥0.45时,表示为物种存在;物种白刺的预测的样方物种存在概率为p≥0.65时,表示为物种存在;物种花棒的样方物种存在概率为p≥0.7时,表示为物种存在;物种沙蒿的样方物种存在概率为p≥0.4时,表示为物种存在。物种实际存在的样方数越多,应该选择的概率越小;物种实际存在的样方数越少,应该选择的概率越大。
3、研究了物种分布模型和抽样技术结合的方法。集群分布的物种沙枣、梭梭、白刺和柽柳采用基于H-T估计量的适应性抽样得出的方差和存在物种的样方数的均值,估计效果都是最好的。
The study of relationship between species distribution and environmental, species distribution simulation and simulation of distribution patterns of biodiversity, which play an important role in theoretical and practical for global changing, biodiversity monitoring, conservation and evaluation, species invasive and so on. However, whether the analysis of relationship between species and environment, or the simulation of the potential distribution area and future distribution trends forecast, the most basic and important task is investigated and recorded the status of species distribution. Forest resource survey, especially the forest resource shows scare, cluster distribution. Such as the rare tree species, shrubs, herbs and other endangered animals, the investigation more difficult to perform, and generally not efficient. To improve the accuracy and efficiency of resource survey for sparse species. The adaptive sampling techniques and methods were used for investigating at home and abroad. But adaptive cluster sampling have some problems, e.g, survey people can not know the distribution of species ahead of time. So, There is very important significance for investigate species resources simply, especially the distribution of forest resource.
Species distribution models use present species points and absent species points and their corresponding environmental factors to predict all possible species distribution. Adaptive cluster sample combine with species distribution model predict species distribution in advance. The investigation has a target, and greatly improves the efficiency of the investigation. Therefore, the study of species distribution model and its guidance to sampling techniques are extremely important.
Based on the population data of four species, Elaeagnus angustifolia, Haloxylon ammodendron, Nitraria tangtorum, Tamarix chinensis, which are rare and clustering. Research area lie at the edge of Wulanbuhe desert at Bayangaolei Town in Dengkuo County in west Inner Mongolia. The spatial pattern of four species were studied first. Actual data and remote sensing data were combined. First the application of MaxEnt and GARP species distribution models were studied. Then the research of species distribution model to guide sampling technique was presented. In the study of MaxEnt species distribution model, the radio of training data and test data, change of environment variable, threshold, sample size, quadrat size and species, which influence on predictive accuracy were main studied. However, about GARP species distribution model, only sample size, quadrat size and species were studied. At last, MaxEnt and GARP models were compared. When study on the guidance of species distribution models to sampling technique, first the probability of present species was studied, then the sample method was presented about rare, cluster species under the guidance of species distribution models. Research conclusions and innovations are mainly as follows:
The main innovations are as follows:
1. The influence of various factors on predictive accuracy of species distribution was studied.
2. The method of sampling techniques and species distribution model to study together was presented, and use the results of species distribution model to guide the sampling.
The main research conclutions are as follows:
1. Through the study of influence of sample size, quadrat size, species and environment variables,on predictive accuracy, we received that : when the quadrat size is 10×10m, MaxEnt model chooses 100 sample size the accuracy is the best and GARP model choose 150 the accuracy is the best. The predictive accuracy is much large when quadrat size close to resolution of these two models. On the contrary, the predictive accuracy is low.MaxEnt and GARP species distribution models are both better to Elaeagnus angustifolia and Tamarix chinensis, but worse to Haloxylon ammodendron and Nitraria tangtorum. It is proved that different degree of aggregation, received the different predictive accuracy. Different environment variables, different predictive accuracy. And the more variables received higher accuracy.
2. Based on the design of study area and 10×10m quadrat size. The probability of present species was presented. When the probability more than 0.55, Species Elaeagnus angustifolia and Tamarix chinensis are shown presence. When the probability more than 0.45, Species Haloxylon ammodendron are shown presence. When the probability more than 0.65, Species Nitraria tangtorum are shown presence. When the probability more than 0.45, Species Haloxylon ammodendron are shown presence. When the probability more than 0.65, Species Nitraria tangtorum are shown presence. When the probability more than 0.7, Species Hedysarum scoparium are shown presence. When the probability more than 0.4, Species Artemisia ordosica are shown presence. Overall, When the species distribution overview was known, if the more point of actual distribution, the smaller probability value was choice; if the smaller point of actual distribution, the larger probability value was choice.
3. Though the study of species distribution models together with sampling technique, it is proved that the species of aggregated distribution use based on HT estimator adaptive sampling is the best.
森林资源的调查,特别是对于稀疏群团状分布的森林资源,如珍稀乔木树种、灌木、草本及濒临灭绝的动物等的调查,比较难执行,而且一般的调查方法效率不高。为了提高稀疏群团状分布资源调查的精度和效率,国内外应用自适应群团抽样调查技术和方法进行调查。对稀疏群团分布的物种进行调查,自适应群团抽样也存在一些问题,例如,在调查前人们事先无法知道稀疏群团物种的分布状况。因此,研究如何简便快捷的调查稀疏群团物种资源的分布,具有非常重要的现实意义。
物种分布模型可以利用物种已知的或是未知的分布点与其对应的环境因子的关系,预测物种的所有可能的分布状况。自适应群团抽样与物种分布模型相结合,应用物种分布模型事先对物种的分布状况进行预测,使自适应群团抽样调查技术更具有针对性和实用性。因此,研究物种分布模型和抽样技术的结合具有重要的意义。
本论文以内蒙古磴口县巴彦高勒镇乌兰布和沙漠边缘地区为研究区,选取沙枣(Elaeagnus angustifolia)、梭梭(Haloxylon ammodendron)、白刺(Nitraria tangtorum)和柽柳(Tamarix chinensis)研究对象,研究了四个物种的空间分布格局。使用研究区域的实际调查数据和遥感数据,选择物种分布模型MaxEnt模型和GARP模型进行了应用研究,并提出了物种分布模型和抽样技术相结合的应用研究方法。在物种分布模型的研究中,主要是研究了影响物种分布模型精度的几个因子。其中,在MaxEnt模型研究中,主要研究了训练数据和检验数据的比例、改变环境变量、阈值、样本量、样方大小以及物种等因子对MaxEnt模型预测精度的影响;在GARP模型研究中,研究了样本量、样方大小以及物种对其预测精度的影响。最后比较研究了MaxEnt模型和GARP模型。在研究物种分布模型和抽样技术时,研究了物种存在的概率,并提出了在物种分布模型的指导下,对于分布稀疏群团状分布的物种所应选择的抽样方法。主要研究结论和创新点如下:
主要创新点为:
1、模拟研究了多种因子对物种分布模型的预测精度的影响。
2、提出抽样技术和物种分布模型结合起来进行研究的方法,利用物种分布模型预测的结果指导抽样调查的实施。
主要结论为:
1、研究了样本量、样方大小、物种和环境变量对物种分布模型的精度影响,结果表明:样方为10×10m时,MaxEnt物种分布模型选择的样本量为100预测精度最好,GARP选择的样本量为150预测精度最高;这两个模型的预测精度都是在样方大小和分辨率比较近时比较大,样方大小和分辨率相差的比较大时,预测精度比较低;MaxEnt和GARP物种分布模型都是对物种沙枣和柽柳的预测结果比较好,对物种白刺和梭梭的预测结果稍差一些。证明了物种的聚集程度不同,得出的预测精度不同;环境变量的选择影响MaxEnt物种分布模型的预测稳定性和精度,选择的变量越多,可以得出的精度越高。
2、基于研究区域和样方为10×10m的设计,物种沙枣和柽柳的预测的样方物种存在概率为p≥0.55时,表示为物种存在;物种梭梭的预测的样方物种存在概率为p≥0.45时,表示为物种存在;物种白刺的预测的样方物种存在概率为p≥0.65时,表示为物种存在;物种花棒的样方物种存在概率为p≥0.7时,表示为物种存在;物种沙蒿的样方物种存在概率为p≥0.4时,表示为物种存在。物种实际存在的样方数越多,应该选择的概率越小;物种实际存在的样方数越少,应该选择的概率越大。
3、研究了物种分布模型和抽样技术结合的方法。集群分布的物种沙枣、梭梭、白刺和柽柳采用基于H-T估计量的适应性抽样得出的方差和存在物种的样方数的均值,估计效果都是最好的。
The study of relationship between species distribution and environmental, species distribution simulation and simulation of distribution patterns of biodiversity, which play an important role in theoretical and practical for global changing, biodiversity monitoring, conservation and evaluation, species invasive and so on. However, whether the analysis of relationship between species and environment, or the simulation of the potential distribution area and future distribution trends forecast, the most basic and important task is investigated and recorded the status of species distribution. Forest resource survey, especially the forest resource shows scare, cluster distribution. Such as the rare tree species, shrubs, herbs and other endangered animals, the investigation more difficult to perform, and generally not efficient. To improve the accuracy and efficiency of resource survey for sparse species. The adaptive sampling techniques and methods were used for investigating at home and abroad. But adaptive cluster sampling have some problems, e.g, survey people can not know the distribution of species ahead of time. So, There is very important significance for investigate species resources simply, especially the distribution of forest resource.
Species distribution models use present species points and absent species points and their corresponding environmental factors to predict all possible species distribution. Adaptive cluster sample combine with species distribution model predict species distribution in advance. The investigation has a target, and greatly improves the efficiency of the investigation. Therefore, the study of species distribution model and its guidance to sampling techniques are extremely important.
Based on the population data of four species, Elaeagnus angustifolia, Haloxylon ammodendron, Nitraria tangtorum, Tamarix chinensis, which are rare and clustering. Research area lie at the edge of Wulanbuhe desert at Bayangaolei Town in Dengkuo County in west Inner Mongolia. The spatial pattern of four species were studied first. Actual data and remote sensing data were combined. First the application of MaxEnt and GARP species distribution models were studied. Then the research of species distribution model to guide sampling technique was presented. In the study of MaxEnt species distribution model, the radio of training data and test data, change of environment variable, threshold, sample size, quadrat size and species, which influence on predictive accuracy were main studied. However, about GARP species distribution model, only sample size, quadrat size and species were studied. At last, MaxEnt and GARP models were compared. When study on the guidance of species distribution models to sampling technique, first the probability of present species was studied, then the sample method was presented about rare, cluster species under the guidance of species distribution models. Research conclusions and innovations are mainly as follows:
The main innovations are as follows:
1. The influence of various factors on predictive accuracy of species distribution was studied.
2. The method of sampling techniques and species distribution model to study together was presented, and use the results of species distribution model to guide the sampling.
The main research conclutions are as follows:
1. Through the study of influence of sample size, quadrat size, species and environment variables,on predictive accuracy, we received that : when the quadrat size is 10×10m, MaxEnt model chooses 100 sample size the accuracy is the best and GARP model choose 150 the accuracy is the best. The predictive accuracy is much large when quadrat size close to resolution of these two models. On the contrary, the predictive accuracy is low.MaxEnt and GARP species distribution models are both better to Elaeagnus angustifolia and Tamarix chinensis, but worse to Haloxylon ammodendron and Nitraria tangtorum. It is proved that different degree of aggregation, received the different predictive accuracy. Different environment variables, different predictive accuracy. And the more variables received higher accuracy.
2. Based on the design of study area and 10×10m quadrat size. The probability of present species was presented. When the probability more than 0.55, Species Elaeagnus angustifolia and Tamarix chinensis are shown presence. When the probability more than 0.45, Species Haloxylon ammodendron are shown presence. When the probability more than 0.65, Species Nitraria tangtorum are shown presence. When the probability more than 0.45, Species Haloxylon ammodendron are shown presence. When the probability more than 0.65, Species Nitraria tangtorum are shown presence. When the probability more than 0.7, Species Hedysarum scoparium are shown presence. When the probability more than 0.4, Species Artemisia ordosica are shown presence. Overall, When the species distribution overview was known, if the more point of actual distribution, the smaller probability value was choice; if the smaller point of actual distribution, the larger probability value was choice.
3. Though the study of species distribution models together with sampling technique, it is proved that the species of aggregated distribution use based on HT estimator adaptive sampling is the best.
引文
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