黄龙钙化滩流地物种-面积关系

黄龙沟钙化滩流地由于溪流的作用,在滩流地之间形成面积大小不一的植物群落斑块。这些小尺度斑块面积的大小对植物物种数量的影响尚不清楚。应用回归分析法和幂函数方程对黄龙沟钙化滩流地内的物种-面积关系进行了研究。结果表明黄龙沟钙化滩流地中斑块的大小对物种数(含兰科植物)具有强烈的影响,而调查的其他因子对总的植物物种数量的影响不显著。在所调查的环境因子中,斑块面积对物种数量的影响达到79.5%,即斑块越大,所包含的物种数量越多。兰科植物种类数量除了受斑块面积的影响外,还与距离林缘的距离有关(负相关)。物种-面积关系符合幂函数方程S=cA^Z的规律。不同的尺度下,z值略有差异,在中等尺度下 (1-10 m²)最大,为0.2616,较大尺度下(10-100 m²)的最小,z值为0.2050,小尺度下(<1 m²),z值为0.2382。表明中等尺度的斑块(1-10 m²)包含的物种数(含兰科植物)的增长速度最快,而在斑块面积大于10 m²时,物种数增长速度最小。     

坡向对四川冶勒红豆杉种群分布格局的影响——基于斑块信息的分析

在斑块信息的基础上,利用GIS技术分析了坡向对四川冶勒红豆杉种群分布格局的影响.结果表明,冶勒红豆杉种群主要分布在西北向、东北向、东向和北向斑块上,这些斑块在分布面积、平均斑块大小、平均斑块周长以及红豆杉个体的分布数量方面均占绝对优势.红豆杉种群的分布同坡向之间存在极显著的关联性,种群优先分布于北坡、东北坡、西北坡和东坡斑块,其次为西坡和西南坡斑块,最后为平地和东南坡斑块.冶勒红豆杉种群的分布格局绝大多数为聚集分布(除南向斑块外).其中,种群聚集强度最大的是北向斑块,其CE值高达0.906,其次为东北向、西北向斑块,其CE值分别为0.797和0.563,而其余坡向斑块上的CE值均低于0.5.聚集强度CE值与斑块数量、斑块总周长以及斑块上分布的红豆杉个体数呈显著正相关(相关系数分别为0.936、0.735和0.802),而与斑块面积、平均斑块大小、平均斑块周长、平均形状指数无显著相关.     

三峡库区低山丘陵区多尺度景观指数响应及适宜粒度

景观格局具有典型的空间异质性和尺度依赖性。在对景观格局进行分析研究时需跨越多个尺度,空间粒度大小在尺度聚合分析中至关重要。三峡库区低山丘陵区斑块破碎、景观格复杂性显著、景观格局特征及尺度变异规律仍待明确,本文以三峡库区秭归县为研究区,设定1—400m内23个粒度梯度水平,定量评估县域、乡镇以及小流域不同幅度上的景观格局指数以及拟合函数,探讨多空间幅度景观格局指数随粒度大小的变化特征。基于景观指数的粒度效应特征和拟合函数的曲线特征(最大曲率点、极值点),明确适宜不同幅度的山地景观格局指数研究的粒度阈值,以揭示库区低山丘陵区景观结构的复杂性和变异性。结果表明：不同景观指数对空间粒度变化和空间幅度变化的响应存在差异,不同景观指数的空间粒度响应主要呈现增加、降低、波动和无明显规律变化的趋势,其中斑块密度、最大斑块面积等指数对斑块形状和大小的变化敏感,而多样性指数的粒度变化敏感度较低;部分指数如斑块密度、边缘密度、周长面积分维数等对空间幅度的变化并不敏感,而最大斑块面积、景观形状指数、散布与并列指数、分离度等指数对空间幅度的变化敏感,适合进行不同幅度适宜粒度阈值的推定;边缘密度、平均斑块大小、景...     

不同尺度下红树景观格局与叶面积指数的空间分异及表征关系

以2010 年SPOT5(空间分辨率为2.5 m)遥感图像为信息源, 提取红树林群落的空间分布范围及叶面积指数(LAI), 在ArcGIS10.0 软件及fragstats4.0 软件平台的支持下, 分别以100 m、300 m、500 m、700 m、900 m 为网格单元大小计算景观格局指数及LAI 指数, 分析不同尺度下红树林景观格局与叶面积指数的空间分异及表征关系。结果表明: (1)红树林群落叶面积指数(LAI)均呈现较为明显的空间分异特征。随着尺度的增大, 各网格单元的LAI 平均值先减小后增大; (2)斑块数量(NP)及平均斑块面积(MPS)呈现出东南地区高于西北地区的特点, 随着尺度的增大而增大。平均形状指数(MSI)随着尺度的增大逐渐增大。面积周长比均值(MPAR)的空间分异特征不明显; (3)红树林群落叶面积指数与群落景观指数在部分尺度下(500 m、700 m)存在明显的表征关系。(4)700 m 尺度是本研究分析的最佳尺度。     

基于分形理论的云南红河县景观斑块尺度效应

运用分形理论,采用网格法作为基本尺度测度方法,选择云南红河县规普村作为研究区,对不同尺度下的景观斑块数量、相对频率、平均分维数、稳定性指数进行了分析。结果表明:从斑块面积上,研究区域景观斑块以灌木林地、其他草地、水田为主,裸地最小。从斑块个数上,水田最多,裸地最少。随着研究区域观测尺度(粒度)从4000、2000、1333、1000、800、667 m递减变化,每种景观斑块的数量和稳定性指数呈递增趋势,相对频率和平均分维数呈递减趋势。各景观斑块平均分维数介于1.029~1.959,其中河流水面斑块形状最复杂,农村居民点斑块最简单。景观斑块分维数受尺度影响呈中小程度变异;景观斑块个数受尺度影响呈中度变异,斑块个数比斑块分维数受尺度影响更显著。景观斑块分维数对尺度响应过程揭示了研究区域景观斑块空间分布尺度变化的规律性。     

林地面积比例梯度下景观组成和构型对生物多样性的影响

以黄河中下游郑州地区为研究区域,根据林地面积比例高低,把确定的260块景观区域划分为林地面积比例高(50%)、中等(50%—30%)、低(30%—10%)和非常低(10%)共4个不同类型;采用概率连接度指数(PC)表征林地栖息地可用性程度。利用R软件里的广义线性模型(GLM),分析了10、50、100、250、500、1000、2000 m和3000 m物种不同扩散能力下,反映林地景观组成和构型特征的林地面积比例(PA),以及林地面积比例(PA)与林地斑块数量(NP)、林地平均斑块大小(PS)、林地斑块隔离度(PI)组合作用对栖息地可用性的影响。结果表明:在林地面积比例高的区域,林地面积比例是决定栖息地可用性程度的重要影响因素;在林地面积比例中等的区域,除林地面积比例外,林地斑块隔离度和平均斑块大小是主要影响因素;在林地面积比例低和较低时,依据物种扩散能力的不同,需要考虑平均斑块大小和林地斑块数量的影响。景观中不同林地面积比例情况下,生物保护措施和管理要依据林地面积比例、林地斑块数量、林地平均斑块大小、以及林地斑块隔离度。     

吉林蛟河针阔混交林多年生幼苗物种组成及其生境解释

为了解生物因素与非生物因素对吉林蛟河针阔混交林多年生幼苗物种组成、数量特征及其空间分布的影响,以吉林蛟河21.12 hm2针阔混交林监测样地为基础,在样地内按照机械布点的方式设置了128个5 m×5 m多年生幼苗监测样方,以乔木树种多年生幼苗(高度H≥30 cm,胸径DBH1 cm)为对象,分析多年生幼苗的物种组成与数量特征。以重要值排名前三的主要树种幼苗为对象,分析生物因素与非生物因素对其物种组成及多度的影响。结果表明:(1)本次共调查到871株实生幼苗,分属13科16属25种,均为样地内主要树种的幼苗,Simpson多样性指数为0.91,重要值排名前三的幼苗为色木槭(Acer mono)、白牛槭(Acer mandshuricum)和红松(Pinus koaiensis)。(2)生物成体因素对幼苗数量影响的尺度效应(10和20 m)在物种间存在差异。白牛槭幼苗数量在两个尺度都与其成体数量呈显著正相关。红松幼苗数量在两个尺度都与其成体胸高断面积之和呈显著正相关,与相对胸高断面积在20 m尺度呈显著负相关。色木槭幼苗数量在20 m尺度上与成体胸高断面积之和呈显著正相关,与相对胸高断面积则呈显著负相关,在10 m尺度上与成体数量呈显著正相关。同种成体对3种幼苗的数量没有表现出显著的密度制约效应。(3)回归结果表明:全部物种幼苗数量与胸高断面积之和、叶面积指数及土壤酸碱度呈显著正相关,与土壤湿度呈显著负相关。白牛槭、红松及色木槭幼苗数量与10个因子的相关性有较大差异,表明不同树种对不同的生境选择性存在较大差异。     

丘塘景观土壤养分的空间变异

理解土壤养分空间分布的异质性对于评价和管理土地资源具有重要意义。利用地理信息系统和地统计学方法定量研究了丘塘景观土壤养分的空间异质性特征。结果表明 ,土壤全氮的有效变程最大 (为 16 5 m) ,有机碳次之 (10 2 m) ,而全磷的变程最小(90 m)。土壤有机碳含量由高到低 5个不同级别的土壤面积与丘塘景观整个面积的比值的变化范围较小 (10 .5 4 %～ 2 3.15 % ) ,土壤全氮含量比值变化范围较大 (5 .79%～ 32 .73% ) ,土壤全磷的比值的变化范围最大 (1.80 %～ 4 2 .0 6 % )。土壤有机碳和土壤全氮的分布情况较为一致 ,不同级别斑块分布也很相似 ,土壤有机碳含量高的地方土壤全氮也高。表层土壤有机碳和全磷的空间异质分布用球状模型拟合最佳 ,而全氮的空间分布规律更宜用指数模型来拟合。景观尺度的半方差拟合总体上优于斑块尺度。相对有机碳和全氮 ,全磷的空间异质性更多由随机因素 (如人类施肥活动 )引起和决定。土壤全氮的取样尺度应大于 16 5 m,而有机碳、全磷的取样距离则分别可大于 10 3m和 90 m。     

基于RS和GIS的开封市土地覆盖分形

以RS和GIS为技术手段,利用开封市0.61m空间分辨率的QuickBird卫星遥感数据,采用分形几何方法研究了斑块的面积效应和覆盖类型分形的关系,并对覆盖类型的分形特征差异等进行了分析。结果表明,开封市防护林地和农田的平均斑块面积较大,分维数也较大,而水体的分维数较小,说明防护林和农田斑块的边界结构特征比水体更为复杂。斑块的分维值具有尺度依赖性,同一类型中大的斑块往往具有较大的分维值,其原因是大斑块经常出现不同类型的斑块相互嵌套,小斑块则很少出现甚至不出现这种现象。     

农业主产区湖泊水质对湖滨带多尺度景观格局的空间响应

为分析农业主产区湖泊水环境质量对不同空间尺度上景观格局的响应关系,以洪湖为研究对象,以5种湖泊功能区为基础,利用RS和GIS软件生成7种空间尺度的湖滨带缓冲区,采用Fragstats 软件分析景观格局指数,结合冗余分析等数理统计方法与模型,研究景观格局对洪湖水质变化的空间尺度效应。结果表明: 1)景观格局在不同宽度缓冲区内对湖泊水质的影响具有空间尺度性,在200 m宽度其解释能力最大,达到86.1%,是影响水质的最有效空间尺度。2)景观配置变量(如最大斑块指数、斑块密度等)对水质的影响程度大于景观组成变量(如面积比例和均匀度指数等)。3)景观类型对水质的影响机制具有较大的差异性,农业用地受流域的地形地貌和耕作方式等因素的影响,在100～500 m小尺度的湖滨带缓冲区内对水质退化起主导作用;在远离水体的相对较大尺度(1000～5000 m),林地分布越密集、面积越大,对污染物进入水体的净化作用越明显;草地对水质的影响与林地一致;密集分布的城市用地对水质的影响与林地相反。研究结果可以从宏观尺度上为农业主产区湖泊流域水质管理和景观合理配置提供科学参考。     

Partial least squares for functional joint models with applications to the Alzheimer's disease neuroimaging initiative study

Many biomedical studies have identified important imaging biomarkers that are associated with both repeated clinical measures and a survival outcome. The functional joint model (FJM) framework, proposed by Li and Luo in 2017, investigates the association between repeated clinical measures and survival data, while adjusting for both high-dimensional images and low-dimensional covariates based on the functional principal component analysis (FPCA). In this paper, we propose a novel algorithm for the estimation of FJM based on the functional partial least squares (FPLS). Our numerical studies demonstrate that, compared to FPCA, the proposed FPLS algorithm can yield more accurate and robust estimation and prediction performance in many important scenarios. We apply the proposed FPLS algorithm to a neuroimaging study. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.     

Managing data locally to answer questions globally: The role of collaborative science in ecology

Ecologists are increasingly asking large‐scale and/or broad‐scope questions that require vast datasets. In response, various top‐down efforts and incentives have been implemented to encourage data sharing and integration. However, despite general consensus on the critical need for more open ecological data, several roadblocks still discourage compliance and participation in these projects; as a result, ecological data remain largely unavailable. Grassroots initiatives (i.e. efforts initiated and led by cohesive groups of scientists focused on specific goals) have thus far been overlooked as a powerful means to meet these challenges. These bottom‐up collaborative data integration projects can play a crucial role in making high quality datasets available because they tackle the heterogeneity of ecological data at a scale where it is still manageable, all the while offering the support and structure to do so. These initiatives foster best practices in data management and provide tangible rewards to researchers who choose to invest time in sound data stewardship. By maintaining proximity between data generators and data users, grassroots initiatives improve data interpretation and ensure high‐quality data integration while providing fair acknowledgement to data generators. We encourage researchers to formalize existing collaborations and to engage in local activities that improve the availability and distribution of ecological data. By fostering communication and interaction among scientists, we are convinced that grassroots initiatives can significantly support the development of global‐scale data repositories. In doing so, these projects help address important ecological questions and support policy decisions.     

Is DIA proteomics data FAIR? Current data sharing practices,available bioinformatics infrastructure and recommendations for the future

Data independent acquisition (DIA) proteomics techniques have matured enormously in recent years, thanks to multiple technical developments in, for example, instrumentation and data analysis approaches. However, there are many improvements that are still possible for DIA data in the area of the FAIR (Findability, Accessibility, Interoperability and Reusability) data principles. These include more tailored data sharing practices and open data standards since public databases and data standards for proteomics were mostly designed with DDA data in mind. Here we first describe the current state of the art in the context of FAIR data for proteomics in general, and for DIA approaches in particular. For improving the current situation for DIA data, we make the following recommendations for the future: (i) development of an open data standard for spectral libraries; (ii) make mandatory the availability of the spectral libraries used in DIA experiments in ProteomeXchange resources; (iii) improve the support for DIA data in the data standards developed by the Proteomics Standards Initiative; and (iv) improve the support for DIA datasets in ProteomeXchange resources, including more tailored metadata requirements.     

Integrating vegetation field surveys with remotely sensed data

Summary   This paper explores data compatibility issues arising from the assessment of remnant native vegetation condition using satellite remote sensing and field-based data. Space-borne passive remote sensing is increasingly used as a way of providing a total sample and synoptic overview of the spectral and spatial characteristics of native vegetation canopies at a regional scale. However, integrating field-collected data often not designed for integration with remotely sensed data can lead to data compatibility issues. Subsequent problems associated with the integration of unsuited datasets can contribute to data uncertainty and result in inconclusive findings. It is these types of problems (and potential solutions) that form the basis of this paper. In other words, how can field surveys be designed to support and improve compatibility with remotely sensed total surveys? Key criteria were identified for consideration when designing field-based surveys of native vegetation condition (and other similar applications) with the intent to incorporate remotely sensed data. The criteria include recommendations for the siting of plots, the need for reference location plots, the number of sample sites and plot size and distribution, within a study area. The difficulties associated with successfully integrating these data are illustrated using real examples taken from a study of the vegetation in the Little River Catchment, New South Wales, Australia.     

The Wilcoxon signed rank test for paired comparisons of clustered data

The Wilcoxon signed rank test is a frequently used nonparametric test for paired data (e.g., consisting of pre- and posttreatment measurements) based on independent units of analysis. This test cannot be used for paired comparisons arising from clustered data (e.g., if paired comparisons are available for each of two eyes of an individual). To incorporate clustering, a generalization of the randomization test formulation for the signed rank test is proposed, where the unit of randomization is at the cluster level (e.g., person), while the individual paired units of analysis are at the subunit within cluster level (e.g., eye within person). An adjusted variance estimate of the signed rank test statistic is then derived, which can be used for either balanced (same number of subunits per cluster) or unbalanced (different number of subunits per cluster) data, with an exchangeable correlation structure, with or without tied values. The resulting test statistic is shown to be asymptotically normal as the number of clusters becomes large, if the cluster size is bounded. Simulation studies are performed based on simulating correlated ranked data from a signed log-normal distribution. These studies indicate appropriate type I error for data sets with > or =20 clusters and a superior power profile compared with either the ordinary signed rank test based on the average cluster difference score or the multivariate signed rank test of Puri and Sen. Finally, the methods are illustrated with two data sets, (i) an ophthalmologic data set involving a comparison of electroretinogram (ERG) data in retinitis pigmentosa (RP) patients before and after undergoing an experimental surgical procedure, and (ii) a nutritional data set based on a randomized prospective study of nutritional supplements in RP patients where vitamin E intake outside of study capsules is compared before and after randomization to monitor compliance with nutritional protocols.     

Approaches for Addressing Life Cycle Assessment Data Gaps for Bio‐based Products

There is an increasing need for life cycle data for bio‐based products, which becomes particularly evident with the recent drive for greenhouse gas reporting and carbon footprinting studies. Meeting this need is challenging given that many bio‐products have not yet been studied by life cycle assessment (LCA), and those that have are specific and limited to certain geographic regions. In an attempt to bridge data gaps for bio‐based products, LCA practitioners can use either proxy data sets (e.g., use existing environmental data for apples to represent pears) or extrapolated data (e.g., derive new data for pears by modifying data for apples considering pear‐specific production characteristics). This article explores the challenges and consequences of using these two approaches. Several case studies are used to illustrate the trade‐offs between uncertainty and the ease of application, with carbon footprinting as an example. As shown, the use of proxy data sets is the quickest and easiest solution for bridging data gaps but also has the highest uncertainty. In contrast, data extrapolation methods may require extensive expert knowledge and are thus harder to use but give more robust results in bridging data gaps. They can also provide a sound basis for understanding variability in bio‐based product data. If resources (time, budget, and expertise) are limited, the use of averaged proxy data may be an acceptable compromise for initial or screening assessments. Overall, the article highlights the need for further research on the development and validation of different approaches to bridging data gaps for bio‐based products.     

Data Fusion Methods for Human Health Risk Assessment: Review and Application

The improved accessibility to data that can be used in human health risk assessment (HHRA) necessitates advanced methods to optimally incorporate them in HHRA analyses. This article investigates the application of data fusion methods to handling multiple sources of data in HHRA and its components. This application can be performed at two levels, first, as an integrative framework that incorporates various pieces of information with knowledge bases to build an improved knowledge about an entity and its behavior, and second, in a more specific manner, to combine multiple values for a state of a certain feature or variable (e.g., toxicity) into a single estimation. This work first reviews data fusion formalisms in terms of architectures and techniques that correspond to each of the two mentioned levels. Then, by handling several data fusion problems related to HHRA components, it illustrates the benefits and challenges in their application.     

一种新的生态监测数据质量评估方法——以CERN乔木生长数据为例

将数据可靠性作为有序变量进行分级,在理论上使数据可靠性与主要生态过程、次级生态过程、外部过程等数据源建立关联,构建了一种生态监测数据质量评估方法,提供了一个新的数据质量指数.它通过观察记录的合格率来估计数据集的质量,其检测结果包括了每一条数据的可靠性级别、标记为离群或错误数据的原因,以及完整数据集的质量指数值.将该方法应用于CERN的两个乔木生长数据集,发现该数据质量指数可以定量评估乔木生长数据集的质量.该方法为相关软件的开发提供了基础.