This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vege...This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision.展开更多
There are typical ecosystems of littoral wetlands in the Yellow River Delta.In order to study the relationships between Tamarix chinensis and environmental variables and to predict T.chinensis potential distribution i...There are typical ecosystems of littoral wetlands in the Yellow River Delta.In order to study the relationships between Tamarix chinensis and environmental variables and to predict T.chinensis potential distribution in the Yellow River Delta,641 vegetation samples and 964 soil samples were collected in the area in October of 2004,2005,2006 and 2007.The contents of soil organic matter,total phosphorus,salt,and soluble potassium were determined.Then,the analyzed data were interpolated into spatial raster data by Kriging interpolation method.Meanwhile,the digital elevation model,soil type map and landform unit map of the Yellow River Delta were also collected.Generalized Additive Models(GAMs) were employed to build species-environment model and then simulate the potential distribution of T.chinensis.The results indicated that the distribution of T.chinensis was mainly limited by soil salt content,total soil phosphorus content,soluble potassium content,soil type,landform unit,and elevation.The distribution probability of T.chinensis was produced with a lookup table generated by Grasp Module(based on GAMs) in software ArcView GIS 3.2.The AUC(Area Under Curve) value of validation and cross-validation of ROC(Receive Operating Characteristic) were both higher than 0.8,which suggested that the established model had a high precision for predicting species distribution.展开更多
Count data that exhibit over dispersion (variance of counts is larger than its mean) are commonly analyzed using discrete distributions such as negative binomial, Poisson inverse Gaussian and other models. The Poisson...Count data that exhibit over dispersion (variance of counts is larger than its mean) are commonly analyzed using discrete distributions such as negative binomial, Poisson inverse Gaussian and other models. The Poisson is characterized by the equality of mean and variance whereas the Negative Binomial and the Poisson inverse Gaussian have variance larger than the mean and therefore are more appropriate to model over-dispersed count data. As an alternative to these two models, we shall use the generalized Poisson distribution for group comparisons in the presence of multiple covariates. This problem is known as the ANCOVA and is solved for continuous data. Our objectives were to develop ANCOVA using the generalized Poisson distribution, and compare its goodness of fit to that of the nonparametric Generalized Additive Models. We used real life data to show that the model performs quite satisfactorily when compared to the nonparametric Generalized Additive Models.展开更多
海山是海底重要的生物栖息地类型之一,是研究海洋生物多样性的热点区域。黄鳍金枪鱼(Thunnus albacares)广泛分布于中西太平洋,具有极高的生态和经济价值,然而,鲜有关于海山及其相关特征对黄鳍金枪鱼资源丰度和分布影响的研究。基于2010...海山是海底重要的生物栖息地类型之一,是研究海洋生物多样性的热点区域。黄鳍金枪鱼(Thunnus albacares)广泛分布于中西太平洋,具有极高的生态和经济价值,然而,鲜有关于海山及其相关特征对黄鳍金枪鱼资源丰度和分布影响的研究。基于2010—2021年中西太平洋渔业委员会(Western and Central Pacific Fisheries Commission,WCPFC)汇总的延绳钓和围网渔业数据结合海山特征数据,采用广义加性模型(Generalized additive model,GAM)分析两种不同捕捞方式的黄鳍金枪鱼单位捕捞努力量渔获量(Catch per unit effort,CPUE)与海山相关特征之间的关系。结果表明,中西太平洋两种渔业方式的黄鳍金枪鱼渔获量主要来源于海山区域,海山特征对两种渔业黄鳍金枪鱼的CPUE均产生了极显著性影响(P<0.001)。在延绳钓渔业中,较高的CPUE出现在山顶深度、粗糙度、底面积和海山密度较小、坡度较缓的区域;而在围网渔业中,较高的CPUE则出现在粗糙度较小、山顶深度较大、底面积较大、较陡峭且密集的海山区域。研究探讨了中西太平洋海山特征对黄鳍金枪鱼不同群体的影响机制,为今后进一步探索黄鳍金枪鱼种群分布和资源丰度变化与海洋环境的关系提供了参考与新思路。展开更多
探究城市化对绿地空间碳源/汇的空间分布格局的影响,对评估城市生态系统的碳足迹和制定相应的碳收支管理措施具有重要意义。以净生态系统生产力(NEP)做为碳源/汇的反映指标,基于净初级生产力和土壤呼吸估算杭州市主城区绿地碳源/汇的空...探究城市化对绿地空间碳源/汇的空间分布格局的影响,对评估城市生态系统的碳足迹和制定相应的碳收支管理措施具有重要意义。以净生态系统生产力(NEP)做为碳源/汇的反映指标,基于净初级生产力和土壤呼吸估算杭州市主城区绿地碳源/汇的空间分布格局,关注城乡梯度对不同绿地类型碳源/汇水平的作用。基于净初级生产力和土壤呼吸数据综合获得绿地空间NEP,通过土地利用数据和Fragstats软件进行景观格局分析,采用多元线性回归模型和逐步回归模型筛选影响NEP的景观、植被和气象因子,最后利用广义加性模型探讨NEP与各因子之间的关系。此外,分别比较了相同统计过程在不同城乡梯度和不同绿地类型之间的模型差异。结果表明:杭州市绿地空间NEP分布及其影响因子存在显著的城乡梯度与绿地类型差异。2019—2022年杭州市主城区绿地空间,整体表现为碳源,年均NEP为-0.277 kg C m^(-2) a^(-1);其中表现为碳汇的绿地主要分布在杭州市主城区的西部,而碳源绿地主要分布在中部和东部。整体绿地空间的NEP大小与绿地斑块面积、乔木盖度和灌木盖度呈正相关,与灌木物种丰富度和气温呈负相关。NEP随城区、城郊结合部、郊区的城乡梯度逐步增强;城区NEP与乔木盖度呈正相关,与景观多样性和气温呈负相关;城郊结合部NEP与乔木物种丰富度和灌木盖度呈正相关,与绿地斑块密度和气温呈负相关;郊区NEP则与聚集度指数、乔木盖度和灌木盖度呈正相关。公园、农田、自然植被的NEP依次增大并受到不同因素的调控。公园NEP与聚集度指数、乔木盖度和灌木盖度呈正相关,与景观分割指数、灌木物种丰富度和气温呈负相关;农田NEP与聚集度指数和灌木盖度呈正相关,与气温呈负相关;而自然植被NEP则与乔木盖度呈正相关,与景观多样性指数和气温呈负相关。研究进一步揭示了城市化对绿地空间碳源/汇的影响,为城乡碳收支的差异化管理提供了一定的理论和数据支持。展开更多
目的在上海市闵行区高血压人群中,探究年龄和体重指数(body mass index,BMI)对癌症发生的共同作用。方法研究对象为2007—2015年进入上海市闵行区电子健康信息系统的未患癌症的212394名高血压患者。将年龄、BMI以平滑函数形式纳入广义加...目的在上海市闵行区高血压人群中,探究年龄和体重指数(body mass index,BMI)对癌症发生的共同作用。方法研究对象为2007—2015年进入上海市闵行区电子健康信息系统的未患癌症的212394名高血压患者。将年龄、BMI以平滑函数形式纳入广义加性Cox比例风险模型,用双变量响应模型构建曲面图使结果可视化,全面分析两者对癌症发生的联合效应。结果截至2018年12月31日,累计22141名高血压患者新发癌症。年龄与癌症发生风险整体呈线性趋势,而BMI与癌症发生风险整体呈“U”型,BMI在26 kg/m^(2)左右时癌症发生风险最低。不同BMI下,随着年龄增大,癌症发生风险均增加;不同年龄下,BMI与癌症发生风险的关联不同:青年人群(20~44岁)中BMI与癌症发生风险无明显关联,中老年人群(≥45岁)中BMI与癌症发生风险呈“U”型关联,BMI在26 kg/m^(2)左右时癌症发生风险最低。结论建议高血压人群控制BMI在合理范围内,特别是中老年人群,以减少癌症发生。展开更多
基金Supported by the National Natural Science Foundation of China （61273131） 111 Project （B12018）+1 种基金 the Innovation Project of Graduate in Jiangsu Province （CXZZ12_0741） the Fundamental Research Funds for the Central Universities （JUDCF12034）
基金Under the auspices of National Natural Science Foundation of China(No.41001363)
文摘This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision.
基金Under the auspices of the Project of National Natural Science Foundation of China ( No. 41001363)Autonomous Project of State Key Laboratory of Resources and Environmental Information System,Geo-information Tupu Theory and Virtual Geoscience
文摘There are typical ecosystems of littoral wetlands in the Yellow River Delta.In order to study the relationships between Tamarix chinensis and environmental variables and to predict T.chinensis potential distribution in the Yellow River Delta,641 vegetation samples and 964 soil samples were collected in the area in October of 2004,2005,2006 and 2007.The contents of soil organic matter,total phosphorus,salt,and soluble potassium were determined.Then,the analyzed data were interpolated into spatial raster data by Kriging interpolation method.Meanwhile,the digital elevation model,soil type map and landform unit map of the Yellow River Delta were also collected.Generalized Additive Models(GAMs) were employed to build species-environment model and then simulate the potential distribution of T.chinensis.The results indicated that the distribution of T.chinensis was mainly limited by soil salt content,total soil phosphorus content,soluble potassium content,soil type,landform unit,and elevation.The distribution probability of T.chinensis was produced with a lookup table generated by Grasp Module(based on GAMs) in software ArcView GIS 3.2.The AUC(Area Under Curve) value of validation and cross-validation of ROC(Receive Operating Characteristic) were both higher than 0.8,which suggested that the established model had a high precision for predicting species distribution.
文摘Count data that exhibit over dispersion (variance of counts is larger than its mean) are commonly analyzed using discrete distributions such as negative binomial, Poisson inverse Gaussian and other models. The Poisson is characterized by the equality of mean and variance whereas the Negative Binomial and the Poisson inverse Gaussian have variance larger than the mean and therefore are more appropriate to model over-dispersed count data. As an alternative to these two models, we shall use the generalized Poisson distribution for group comparisons in the presence of multiple covariates. This problem is known as the ANCOVA and is solved for continuous data. Our objectives were to develop ANCOVA using the generalized Poisson distribution, and compare its goodness of fit to that of the nonparametric Generalized Additive Models. We used real life data to show that the model performs quite satisfactorily when compared to the nonparametric Generalized Additive Models.
文摘海山是海底重要的生物栖息地类型之一,是研究海洋生物多样性的热点区域。黄鳍金枪鱼(Thunnus albacares)广泛分布于中西太平洋,具有极高的生态和经济价值,然而,鲜有关于海山及其相关特征对黄鳍金枪鱼资源丰度和分布影响的研究。基于2010—2021年中西太平洋渔业委员会(Western and Central Pacific Fisheries Commission,WCPFC)汇总的延绳钓和围网渔业数据结合海山特征数据,采用广义加性模型(Generalized additive model,GAM)分析两种不同捕捞方式的黄鳍金枪鱼单位捕捞努力量渔获量(Catch per unit effort,CPUE)与海山相关特征之间的关系。结果表明,中西太平洋两种渔业方式的黄鳍金枪鱼渔获量主要来源于海山区域,海山特征对两种渔业黄鳍金枪鱼的CPUE均产生了极显著性影响(P<0.001)。在延绳钓渔业中,较高的CPUE出现在山顶深度、粗糙度、底面积和海山密度较小、坡度较缓的区域;而在围网渔业中,较高的CPUE则出现在粗糙度较小、山顶深度较大、底面积较大、较陡峭且密集的海山区域。研究探讨了中西太平洋海山特征对黄鳍金枪鱼不同群体的影响机制,为今后进一步探索黄鳍金枪鱼种群分布和资源丰度变化与海洋环境的关系提供了参考与新思路。
文摘探究城市化对绿地空间碳源/汇的空间分布格局的影响,对评估城市生态系统的碳足迹和制定相应的碳收支管理措施具有重要意义。以净生态系统生产力(NEP)做为碳源/汇的反映指标,基于净初级生产力和土壤呼吸估算杭州市主城区绿地碳源/汇的空间分布格局,关注城乡梯度对不同绿地类型碳源/汇水平的作用。基于净初级生产力和土壤呼吸数据综合获得绿地空间NEP,通过土地利用数据和Fragstats软件进行景观格局分析,采用多元线性回归模型和逐步回归模型筛选影响NEP的景观、植被和气象因子,最后利用广义加性模型探讨NEP与各因子之间的关系。此外,分别比较了相同统计过程在不同城乡梯度和不同绿地类型之间的模型差异。结果表明:杭州市绿地空间NEP分布及其影响因子存在显著的城乡梯度与绿地类型差异。2019—2022年杭州市主城区绿地空间,整体表现为碳源,年均NEP为-0.277 kg C m^(-2) a^(-1);其中表现为碳汇的绿地主要分布在杭州市主城区的西部,而碳源绿地主要分布在中部和东部。整体绿地空间的NEP大小与绿地斑块面积、乔木盖度和灌木盖度呈正相关,与灌木物种丰富度和气温呈负相关。NEP随城区、城郊结合部、郊区的城乡梯度逐步增强;城区NEP与乔木盖度呈正相关,与景观多样性和气温呈负相关;城郊结合部NEP与乔木物种丰富度和灌木盖度呈正相关,与绿地斑块密度和气温呈负相关;郊区NEP则与聚集度指数、乔木盖度和灌木盖度呈正相关。公园、农田、自然植被的NEP依次增大并受到不同因素的调控。公园NEP与聚集度指数、乔木盖度和灌木盖度呈正相关,与景观分割指数、灌木物种丰富度和气温呈负相关;农田NEP与聚集度指数和灌木盖度呈正相关,与气温呈负相关;而自然植被NEP则与乔木盖度呈正相关,与景观多样性指数和气温呈负相关。研究进一步揭示了城市化对绿地空间碳源/汇的影响,为城乡碳收支的差异化管理提供了一定的理论和数据支持。
文摘目的在上海市闵行区高血压人群中,探究年龄和体重指数(body mass index,BMI)对癌症发生的共同作用。方法研究对象为2007—2015年进入上海市闵行区电子健康信息系统的未患癌症的212394名高血压患者。将年龄、BMI以平滑函数形式纳入广义加性Cox比例风险模型,用双变量响应模型构建曲面图使结果可视化,全面分析两者对癌症发生的联合效应。结果截至2018年12月31日,累计22141名高血压患者新发癌症。年龄与癌症发生风险整体呈线性趋势,而BMI与癌症发生风险整体呈“U”型,BMI在26 kg/m^(2)左右时癌症发生风险最低。不同BMI下,随着年龄增大,癌症发生风险均增加;不同年龄下,BMI与癌症发生风险的关联不同:青年人群(20~44岁)中BMI与癌症发生风险无明显关联,中老年人群(≥45岁)中BMI与癌症发生风险呈“U”型关联,BMI在26 kg/m^(2)左右时癌症发生风险最低。结论建议高血压人群控制BMI在合理范围内,特别是中老年人群,以减少癌症发生。
