分形理论对水生态系统空间格局研究初探

首先对分形理论在生态学中的应用情况作了简要介绍,然后应用驼对梅子垭水库的藻类叶绿素ａ浓度场水平２维空间格局进行了分析,通过对不同时间各浓度轮廓线的空间占据能力进行比较分析,确定了各浓度的稳定程度。可由此说明浮游植物的变化情况和生长潜力。     

2006年夏季南黄海浮游植物叶绿素a分布特征及其环境调控机制

根据2006年夏季南黄海生态调查资料,分析了浮游植物现存量(叶绿素a)的空间分布特征及其主要环境调控机制.结果表明:南黄海浮游植物叶绿素a浓度范围为0.07～12.17 mg/m3,平均值为1.42 mg/m3;浮游植物叶绿素a平面分布呈近岸混合区高于外海层化区的特征,总体上随水体盐度和营养盐梯度的变化而变化,其中浅海陆架锋区对浮游植物有明显的聚集作用,垂直结构中层化区叶绿素次表层最大值现象明显,且最大值的深度和量值具有明显的区域差异;叶绿素a浓度与主要环境因子的相关性分析表明,在较大空间尺度上营养盐是浮游植物的主要调控因子,温度、光照和浮游动物摄食等也在一定空间和程度上对浮游植物的生长产生影响.     

基于MODIS数据的云南九大高原湖泊叶绿素a浓度反演

叶绿素a浓度是反映水体富营养化程度的重要参数。本研究以MODIS卫星影像为数据源,结合地面实测数据,实现了对云南省九大高原湖泊的叶绿素a浓度反演。首先,以2010—2012年的叶绿素a浓度实测数据为基础,对MODIS不同波段的地表辐射率进行了组合试验,优选出九湖叶绿素a浓度的统计模型。其次,利用2013—2014年的叶绿素a浓度实测数据对反演模型进行了验证。最后,利用优选模型对九湖叶绿素a的空间分布特征和变化动态进行分析。结果表明:获取的九湖叶绿素a浓度的空间分布比较符合专业人员的判断,结果较为合理;滇池、洱海、程海、杞麓湖、星云湖、异龙湖和阳宗海的叶绿素a浓度周围高,中部低,抚仙湖和泸沽湖则为周围低,中部高;从年际变化看,2010—2014年期间,滇池、洱海、星云湖和异龙湖叶绿素a浓度年际波动幅度较大,抚仙湖、程海、杞麓湖和泸沽湖变化较为稳定,阳宗海呈减少趋势。     

三峡库区古夫河着生藻类叶绿素a的时空分布特征及其影响因素

古夫河系长江三峡水库湖北省境内香溪河的一条支流,发源于神农架林区并最终汇入三峡水库香溪河库湾,是流域生态学研究的热点水体。本文对古夫河2010年9月至2011年8月干流及主要支流21个样点的着生藻类叶绿素a和14项环境因子进行了调查,采用多元统计方法对调查数据进行了方差分析、聚类分析、偏相关分析和逐步回归分析,研究了着生藻类叶绿素a的时空分布特征及其主要环境影响因子。结果表明：叶绿素a含量范围为0.07—145.96 mg/m2,平均值为11.63 mg/m2。不同样点的叶绿素a含量差异显著,其中古夫河干流上游低于下游,支流竹园河上游高于下游,表现为人为影响大的区域高于人为影响小的区域;不同季节着生藻类叶绿素a含量差异显著,表现出冬春季高、夏秋季低的趋势。古夫河着生藻类叶绿素a与总磷和硬度呈极显著正相关,与电导率、氨氮和总氮呈显著正相关,而与流速呈极显著负相关;水体总磷是古夫河流域着生藻类生长的第一限制因子,流速对着生藻类的生长具有显著抑制作用;古夫河着生藻类生物量空间格局可能由其生境尺度（营养盐）和流域尺度（硬度和电导率）的特征决定,而古夫河着生藻类生物量时间变化主要受水动力（流速）的影响。     

风场对太湖叶绿素a空间分布的影响

叶绿素a的浓度是水环境评价的重要参数。根据2005—2009年太湖全湖32个采样点的20次太湖采样数据,结合气象要素资料模拟的太湖风场,探求了风场对太湖叶绿素a空间分布的影响。结果表明:叶绿素a浓度的高值中心位于太湖西北侧、竺山湾以及梅梁湾流域,而太湖东南部的叶绿素a浓度较低;全太湖全年以东南风为主,南部风速较大,北部风速较弱;风场对叶绿素a的输移作用明显,在风场作用下,叶绿素向太湖西北部和北部输移,造成了该地区太湖流域叶绿素浓度普遍偏高。     

太浦河水体叶绿素a纵向演变特征及主要影响因子

叶绿素a作为重要的水质指标,可直观的描述水体的营养状态。在随水流输送过程中,受沿程环境条件的影响,水体叶绿素a浓度会发生相应改变。2017年11月-2018年10月,在东太湖及太浦河沿线设置了固定采样站点,开展了水体叶绿素a的月际取样调研与分析。结果表明：从东太湖至太浦河沿线叶绿素a浓度发生了明显的纵向演变,在东太湖叶绿素a浓度年平均为（15.341±2.329）μg/L,经过多个站点,到达金泽水文站时,浓度为（11.002±1.004）μg/L,整体上呈显著下降态势（P<0.05）,但不同站点两两之间的对比特征存在差异,而且在不同月份其纵向演变特征也有所不同。聚类及非度量多维尺度分析也表明其在空间和时间上分布的差异性。非线性回归分析表明,在时间和空间上与叶绿素a浓度相关性最高的环境因子为水下0.5 m处的光照强度（P<0.01）;在2018年10月,水体叶绿素a浓度与溶解氧也呈显著相关（P<0.05）。综合现有研究,水体叶绿素a的纵向演变受水质等多种因素的影响。未来需采用系统分析手段,探讨水质、降水、水力停留时间、流速和流量等多种因子对叶绿素a时空分布特征的综合作用,以期为水源地水质安全保障提供科学依据。     

三峡水库香溪河库湾春季水华期间悬浮物动态

在三峡水库香溪河库湾春季藻类水华期间开展了水体悬浮物动态研究.调查结果表明：总悬浮物浓度的中位数是6.80 mg·L^-1, 波动范围是0.66～134.92 mg·L^-1,从河口到库尾入库点呈现逐渐递增的趋势;无机悬浮物空间格局与总悬浮物基本相似,而有机悬浮物空间格局与总悬浮物差异较大,与叶绿素a较为相似.回归分析表明：库湾中部水域叶绿素a与总悬浮物、有机悬浮物均有显著的线性关系,分别解释了总悬浮物、有机悬浮物总变异的66.7%～96.7% 和 58.9%～85.5%;在库湾两端（河口与库尾）叶绿素a与悬浮物参数均没有显著的线性关系.在库湾中部水域,有机悬浮物比无机悬浮物更能解释总悬浮物的变异;在库湾两端,无机悬浮物比有机悬浮物更能解释总悬浮物的变异.这意味着库湾中间水域总悬浮物的动态主要受有机悬浮物内源性生产的影响,而河口或库尾总悬浮物的动态主要受外源性的无机悬浮颗粒物输入的影响.     

滴水湖叶绿素a时空分布及其与水质因子的关系

根据2011年9月-2012年8月监测数据,分析上海市人工湖泊——滴水湖叶绿素a的时空分布及其与主要环境因子的相互关系.结果表明:滴水湖叶绿素a含量随时间的变化幅度较大,表现为3、7月出现高峰而其他月份较低;叶绿素a含量在空间分布上具有一定的分异性,湖心普遍偏高,其他各点相对较低;对滴水湖叶绿素a采用主成分多元线性回归分析(APCS-MLR),得到描述水质因子的潜变量F1、F2与其相关性较大,建立了潜变量与叶绿素a含量的相关性模型;滴水湖叶绿素a含量与溶解氧(DO)、水温、总溶解磷(TDP)、活性磷(AP)有着密切的相关性;磷为滴水湖水体初级生产力的限制因子;因此,磷是有效控制滴水湖水体富营养化的关键因子.     

不同降水条件下沙鞭种群非生殖株丛空间格局分析

植物空间分布格局是物种自身生物学特性与环境因素共同作用的结果,非生殖株丛空间分布格局能够揭示物种无性繁殖与种群扩张过程对异质生境的生态学适应机制。利用基于完全随机、泊松聚块和嵌套双聚块模型的点格局方法和群落学调查,分析了沙鞭(Psammochloa villosa)种群非生殖株丛空间分布格局,探讨了空间格局对降水梯度的响应和适应过程。结果表明：(1)聚集分布是沙鞭种群非生殖株丛的主要类型。在89 mm/a、107.8 mm/a、117.4 mm/a、186 mm/a、191.1 mm/a、363 mm/a降水梯度上聚集尺度分别为3—126 cm、9—200 cm、9—129 cm、6—77 cm、2—95 cm、2—96 cm;(2)基于完全随机模型的空间分布格局对降水的响应规律显著,整体表现为随干旱程度加剧,沙鞭种群非生殖株丛聚集尺度从2—96 cm下降至6—77 cm;(3)在107.8 mm/a、117.4 mm/a、191.1 mm/a、363 mm/a降水梯度上,基于泊松聚块模型的空间分布格局分别在15—19 cm、2—6 cm、2—4 cm、9—25 cm尺度正向偏离泊松聚...     

太湖水色因子空间分布特征及其对水生植物光合作用的影响

利用太湖全湖64个采样点的数据,分析了各水色因子及真光层深度的空间分布和变化特征,并探讨了其对水生植物光合作用的影响.结果表明:叶绿素a浓度在全湖间的差异最大,其变化范围为1.67～159.94μg.L-1,均方差为41.03μg.L-1,在梅梁湾、竺山湖、夹浦港和小梅口附近湖区,叶绿素a浓度较高且空间变化明显,等值线在这些湖区密集分布;悬浮物浓度变化次之,其含量在6.47～143.47mg.L-1之间变化,均方差为31.63mg.L-1,其在大浦港和小梅口入湖口附近湖区的空间变化明显,等值线分布密集;有色可溶性有机物(CDOM)吸收系数在全湖变化较小,没有明显的空间变化特征;真光层深度受悬浮物和叶绿素的共同影响,其空间分布特征与悬浮物相反.     

Geostatistics and spatial analysis in biological anthropology

A variety of methods have been used to make evolutionary inferences based on the spatial distribution of biological data, including reconstructing population history and detection of the geographic pattern of natural selection. This article provides an examination of geostatistical analysis, a method used widely in geology but which has not often been applied in biological anthropology. Geostatistical analysis begins with the examination of a variogram, a plot showing the relationship between a biological distance measure and the geographic distance between data points and which provides information on the extent and pattern of spatial correlation. The results of variogram analysis are used for interpolating values of unknown data points in order to construct a contour map, a process known as kriging. The methods of geostatistical analysis and discussion of potential problems are applied to a large data set of anthropometric measures for 197 populations in Ireland. The geostatistical analysis reveals two major sources of spatial variation. One pattern, seen for overall body and craniofacial size, shows an east-west cline most likely reflecting the combined effects of past population dispersal and settlement. The second pattern is seen for craniofacial height and shows an isolation by distance pattern reflecting rapid spatial changes in the midlands region of Ireland, perhaps attributable to the genetic impact of the Vikings. The correspondence of these results with other analyses of these data and the additional insights generated from variogram analysis and kriging illustrate the potential utility of geostatistical analysis in biological anthropology.     

Stomatal density distribution patterns in leaves of the Jatobá (Hymenaea courbaril L.)

Stomata are leaf structures that are essential for regulating gas exchange and water balance in terrestrial plants. Accurately quantifying stomatal characteristics is consequently of great importance for understanding the physiological processes of plants under different environmental conditions. The objective of this study was to investigate the spatial distribution pattern of stomata on leaflet surfaces, and the possible mechanisms that influence this pattern, particularly leaf expansion. To achieve this, we used geostatistical tools combined with an analysis of biometric relationships of leaves from Hymenaea courbaril L. Our analysis indicates that stomata show a clear spatial structure in this species: average values of foliar expansion rates (ERs) were different on right and left-hand sides of the primary venation of each leaflet and there was a close relationship between the spatial pattern of stomatal density and leaf expansion rate. Such differences in lateral expansion may therefore be partially responsible for the heterogeneous distribution of stomata documented here and in other studies.     

红松老龄林主要树种的空间异质性特征与比较的定量研究

 空间异质性研究对认识不同尺度上的生态系统结构、功能和过程具有重要的理论意义。采用地统计学理论和方法,通过空间异质性特征和比较研究了我国东北红松老龄林中主要树种,即红松(Pinus koraiensis)、云杉(Picea koraiensis)、冷杉(Abies nephrolepis)、枫桦(Betula costata)和紫椴(Tilia amurensis)的空间异质性程度、空间异质性组成、尺度和格局问题。结果表明：红松老龄林中主要树种虽然生长在同一林分中,但是它们的空间异质性与格局具有明显的差异。红松的空间异质性程度最高,分数维较低,其空间异质性主要由空间自相关部分组成,在450m的尺度内,具有明显的空间格局。枫桦的空间异质性程度最低,分数维最大,空间异质性主要由随机部分组成,在大于100m的尺度上不存在明显的空间格局。云杉和冷杉由于生长的立地相似,因此具有相似的空间异质性特征和格局。紫椴是红松最重要的伴生树种,其空间异质性程度、空间异质性组成、尺度和格局等方面与红松接近。红松老龄林中主要树种空间异质性与格局在一定尺度上的差异,反映这些树种在生态系统中具有不同的生态学功能和过程。     

Geostatistical estimation of surface soil carbon stock in Mt. Wakakusa grassland of Japan

Although nation-wide assessments on the grassland soil carbon storage have been conducted in Japan, the uncertainty of the estimation accuracy remains with its local variability. In the present study, using geostatistical approach, we assessed the spatial variability and distribution pattern of organic carbon (OC) and estimated its total stock in the surface soils (0–5 cm) over Mt. Wakakusa grassland (30.2 ha) of Central Japan. The exploratory statistics indicated that the surface soils had 1.88?±?0.28 kg C m^?2 of soil OC density on average (n?=?147) with a moderate variability (CV?=?18.8%), while the geostatistical analysis unveiled that its semivariogram was well fitted by a spherical model (R²?=?0.93, RSS?=?1.15E^?05) and had a strong spatial dependency (nugget–sill ratio?=?0.31). Based on these results, we constructed an interpolated map and estimated total OC stock to 552 Mg C in the surface soil of Mt. Wakakusa grassland.

     

Geostatistical modelling on stream networks: developing valid covariance matrices based on hydrologic distance and stream flow

1. Geostatistical models based on Euclidean distance fail to represent the spatial configuration, connectivity, and directionality of sites in a stream network and may not be ecologically relevant for many chemical, physical and biological studies of freshwater streams. Functional distance measures, such as symmetric and asymmetric hydrologic distance, more accurately represent the transfer of organisms, material and energy through stream networks. However, calculating the hydrologic distances for a large study area remains challenging and substituting hydrologic distance for Euclidean distance may violate geostatistical modelling assumptions. 2. We provide a review of geostatistical modelling assumptions and discuss the statistical and ecological consequences of substituting hydrologic distance measures for Euclidean distance. We also describe a new family of autocovariance models that we developed for stream networks, which are based on hydrologic distance measures. 3. We describe the geographical information system (GIS) methodology used to generate spatial data necessary for geostatistical modelling in stream networks. We also provide an example that illustrates the methodology used to create a valid covariance matrix based on asymmetric hydrologic distance and weighted by discharge volume, which can be incorporated into common geostatistical models. 4. The methodology and tools described supply ecologically meaningful and statistically valid geostatistical models for stream networks. They also provide stream ecologists with the opportunity to develop their own functional measures of distance and connectivity, which will improve geostatistical models developed for stream networks in the future. 5. The GIS tools presented here are being made available in order to facilitate the application of valid geostatistical modelling in freshwater ecology.     

塔里木盆地北缘盐土有机碳含量的贝叶斯地统计预测

准确预测土壤有机碳的空间分布,对于土壤资源开发和保护、应对气候变化和生态系统健康都具有重要意义.本文以塔里木盆地北缘盐土1300 m×1700 m样地为试验区,采集5～10 cm深度土壤样品144个,构建土壤有机碳含量的贝叶斯地统计空间预测模型,并以普通克里格、序惯高斯模拟和逆距离加权方法为对照,评价贝叶斯地统计对土壤有机碳含量的预测性能.结果表明: 研究区土壤有机碳含量处于1.59～9.30 g·kg^-1,平均值为4.36 g·kg^-1,标准偏差为1.62 g·kg^-1;半方差函数符合指数模型,空间结构比参数值为0.57;利用贝叶斯地统计方法,获得了土壤有机碳含量的空间分布图以及评价预测不确定性的预测方差、上95%分位数、下95%分位数分布图;与普通克里格、序惯高斯模拟和逆距离加权方法相比,贝叶斯地统计方法具有更高的土壤有机碳含量空间预测精度,显示出该方法对土壤有机碳含量预测的优越性.     

Periodicity in spatial data and geostatistical models: autocorrelation between patches

Several recent studies in landscape ecology have found periodicity in correlograms or semi-variograms calculated, for instance, from spatial data of soils, forests, or animal populations. Some of the studies interpreted this as an indication of regular or periodic landscape patterns. This interpretation is in disagreement with other studies that doubt whether such analysis is valid. The objective of our study was to explore the relationship between periodicity in landscape patterns and geostatistical models. We were especially interested in the validity of the assumption that periodicity in geostatistical models indicates periodicity in landscape pattern, and whether the former can characterize frequency and magnitude of the latter. We created maps containing various periodic spatial patterns, derived correlograms from these, and examined periodicity in the correlograms. We also created non-regular maps that we suspected would cause periodicity in correlograms. Our results demonstrate that a) various periodic spatial patterns produce periodicity in correlograms derived from them, b) the distance-lags at which correlograms peak correspond to the average distances between patch centers, c) periodicity is strongest when the diameter of patches is equal to the distance between patch edges, d) periodicity in omni-directional correlograms of complex spatial patterns (such as checkerboards) are combinations of several waves because inter-patch distances differ with direction; multiple directional correlograms can decompose such complexity, and e) periodicity in correlograms can also be caused when the number of patches in a study site is small. These results highlight that correlograms can be used to detect and describe regular spatial patterns. However, it is crucial to ensure that the assumption of stationarity is not violated, i.e., that the study area contains a sufficiently large number of patches to avoid incorrect conclusions.     

Bayesian design for minimizing prediction uncertainty in bivariate spatial responses with applications to air quality monitoring

Model-based geostatistical design involves the selection of locations to collect data to minimize an expected loss function over a set of all possible locations. The loss function is specified to reflect the aim of data collection, which, for geostatistical studies, could be to minimize the prediction uncertainty at unobserved locations. In this paper, we propose a new approach to design such studies via a loss function derived through considering the entropy about the model predictions and the parameters of the model. The approach includes a multivariate extension to generalized linear spatial models, and thus can be used to design experiments with more than one response. Unfortunately, evaluating our proposed loss function is computationally expensive so we provide an approximation such that our approach can be adopted to design realistically sized geostatistical studies. This is demonstrated through a simulated study and through designing an air quality monitoring program in Queensland, Australia. The results show that our designs remain highly efficient in achieving each experimental objective individually, providing an ideal compromise between the two objectives. Accordingly, we advocate that our approach could be adopted more generally in model-based geostatistical design.     

北方农牧交错带沽源农田-草地界面土壤水热空间特征

农田-草地景观界面是我国北方农牧交错带景观组成部分,研究该界面生态特征的格局和过程是完善北方农牧交错带研究的热点领域.首次采用地统计学方法中的半变异函数、克里格插值估计、空间分布图等方法研究北方农牧交错带河北沽源地区的农田-草地界面(CGB)土壤水热的空间异质性和界面空间效应.结果表明:根据不同尺度下的土壤水热空间异质性各参数的变化趋势确定较为合理的采样粒度为 0.5m×0.5m;农田-草地界面0～20cm土层土壤含水量为中等变异,土壤温度为弱变异,不同采样粒度空间结构特征定量研究得到土壤水分变程范围(A_0)为2.93～15.4m,且为中到强度的异质性程度(MSH),土壤温度变程为3.75～20.99m,表现为强空间异质性;验证在农田-草地边界向两侧的一定范围内土壤水热存在过渡型界面效应,且具不同的生态梯度.该研究结果是认识农田-草地界面和进一步开展该界面功能研究的基础,对深入研究北方农牧交错带农田-草地界面非生物水热界面具有重要意义.     

Soil Moisture Estimation by Assimilating L-Band Microwave Brightness Temperature with Geostatistics and Observation Localization

The observation could be used to reduce the model uncertainties with data assimilation. If the observation cannot cover the whole model area due to spatial availability or instrument ability, how to do data assimilation at locations not covered by observation? Two commonly used strategies were firstly described: One is covariance localization (CL); the other is observation localization (OL). Compared with CL, OL is easy to parallelize and more efficient for large-scale analysis. This paper evaluated OL in soil moisture profile characterizations, in which the geostatistical semivariogram was used to fit the spatial correlated characteristics of synthetic L-Band microwave brightness temperature measurement. The fitted semivariogram model and the local ensemble transform Kalman filter algorithm are combined together to weight and assimilate the observations within a local region surrounding the grid cell of land surface model to be analyzed. Six scenarios were compared: 1_Obs with one nearest observation assimilated, 5_Obs with no more than five nearest local observations assimilated, and 9_Obs with no more than nine nearest local observations assimilated. The scenarios with no more than 16, 25, and 36 local observations were also compared. From the results we can conclude that more local observations involved in assimilation will improve estimations with an upper bound of 9 observations in this case. This study demonstrates the potentials of geostatistical correlation representation in OL to improve data assimilation of catchment scale soil moisture using synthetic L-band microwave brightness temperature, which cannot cover the study area fully in space due to vegetation effects.