珠江三角洲土地利用变化空间自相关分析

通过对复杂土地利用地学现象的空间与时间上的综合表达和描述,可揭示土地利用时空结构演变的内在规律,可以为研究区土地资源的合理利用、优化配置和可持续发展提供科学依据。本文利用珠江三角洲1990-2005年的土地利用数据,采用空间自相关分析方法对珠江三角洲土地利用／覆盖变化的空间特征进行了实证研究。通过对研究区全域空间自相关、单变量区域空间自相关、双变量区域空间自相关的计算,表明研究区土地利用／覆盖的全域空间自相关较弱,土地利用和开发存在随机性。单变量区域空间自相关和双变量区域空间白相关在不同区域、不同时期显现不同形态,揭示了研究区内部发展的差异性。空间自相关分析方法可直观地显示分析结果,不失为一种深入理解区域土地利用格局和变化的重要和有益的方法。     

不同尺度下耕地土壤Cr含量的空间自相关性分析

以山东省禹城市耕地土壤重金属铬(Cr)元素为例,采用Moran’s I统计量对县级和镇级两个不同取样尺度下Cr含量的空间自相关关系、空间相关尺度和空间分布规律进行了对比研究.结果表明:1)随着研究尺度的增大,Cr含量的变异系数增加,块基比(C0/Sill)减小,空间结构性增强;2)两个尺度下Cr含量Moran’s I指数都大于0,表明研究区内两个尺度下的Cr含量都存在空间正自相关,且Moran’s I指数随着研究尺度的增加而增大;3)Lisa聚类图表明,两个尺度下Cr含量都存在空间聚集区和空间孤立区,其中"高—高"空间聚集和"低—高"空间孤立区域存在潜在Cr污染风险.研究表明小尺度下能够观察到大尺度下观察不到的细微差别,研究可为土壤重金属环境质量评价和重金属污染防治提供重要参考.图4表2参23     

中国电力行业氮氧化物排放的时空分布特征与类群分析

电力行业是中国氮氧化物排放最多的行业之一,其减排成效是完成氮氧化物减排任务的关键。文章基于各省(市、自治区)能源平衡表,采用"自下而上"排放因子法,对中国30个省(市、自治区)电力行业2006—2014年能源消费氮氧化物排放进行了核算。综合考虑经济发展水平、能源消费结构,以及产业结构等方面,从氮氧化物排放总量、人均氮氧化物排放量、氮氧化物排放强度、综合能源氮氧化物排放系数等方面,分析了中国电力行业能源消费氮氧化物排放的省域特征,对省域氮氧化物排放特征进行类群划分,以期为中国电力行业氮氧化物分区域减排政策、途径、措施的制定提供理论参考与科学依据。研究结果表明,从排放总量来看,2006—2014年各省(市、自治区)电力行业能源消费氮氧化物排放总体上呈增长趋势,年平均增长率为3.62%;从人均排放的绝对量来看,经济发展较快和经济发达省域电力行业的人均氮氧化物排放量较高;从排放强度绝对量来看,中西部省域电力行业氮氧化物排放强度较高,东部发达省域的人均氮氧化物排放强度较低,且中东部省域氮氧化物排放强度下降较快,西部省份下降较慢;从综合能源氮氧化物排放系数来看,大部分省域排放系数呈下降趋势,经济发达省域的排放系数较低,欠发达省域的排放系数较高。中国电力行业氮氧化物减排政策、途径和措施应充分考虑省域经济发展水平、产业结构以及能源利用结构等因素。     

基于LMDI-SMC中国氮氧化物减排绩效动态演化规律研究

氮氧化物减排作为经济转型发展中的重要环节,需要探索促进区域均衡发展的差异化协调减排政策。文章首先利用对数平均Divisia指数分解模型(LMDI法)将氮氧化物排放分解为排放因子、能源强度、经济结构、能源结构和经济发展5个因素得出2005-2015年中国30个省市氮氧化物排放与经济增长脱钩系数;然后,按照全国氮氧化物排放脱钩系数的平均水平,将其离散化为低、中低、中高和高4种类型构建其马尔可夫转移概率矩阵,考察其排放脱钩是否存在趋同现象;最后,引入空间滞后因素,构造其空间马尔可夫转移概率矩阵,分析在不同周围背景影响下氮氧化物排放脱钩的动态演化规律。结果表明,中国氮氧化物排放与经济增长脱钩系数呈现逐年上升趋势,且存在低脱钩、中低脱钩、中高脱钩及高脱钩4个趋同俱乐部;中国氮氧化物排放脱钩类型转移受周围背景的影响明显,在不同区域背景下同一类型的转移概率有所变化,同一区域背景下不同区域类型的转移概率不尽相同,且周围背景强化了区域氮氧化物排放脱钩的俱乐部趋同现象。通过对氮氧化物排放脱钩系数量化的减排绩效的时空动态变化进行分析可为氮氧化物区域协调减排提供参考建议。     

干旱区绿洲城市生态系统服务价值空间自相关格局分析与模拟

基于1995—2015年乌鲁木齐土地利用遥感解译数据,运用CA-Markov模型模拟预测2025年土地利用变化并进行生态系统服务价值(ESV)评估,应用格网分析、空间自相关、Getis-Ord热点区分析等空间统计方法,进一步分析生态系统服务价值与土地利用空间格局的分布特征,讨论两者空间自相关关系及年际变化的冷热点空间分布变化原因。结果表明,近30 a研究区各土地利用类型的生态系统服务价值呈波动变化态势,但总体上表现为减少趋势,主要原因在于耕地、林草地提供的生态系统服务价值减少。生态系统服务价值空间分布格局差异明显,高值区域多年来主要分布在乌鲁木齐县林地面积较多的地区,低值区域集中分布于乌鲁木齐中心四区(天山区、沙依巴克区、水磨沟区、头屯河区)的周边区域。研究区生态系统服务价值具有显著的空间正自相关性与空间聚集特征,高-高、低-低聚集分布分别与生态系统服务价值高值、低值区域高度重合。生态系统服务价值热冷点区呈逐年增加趋势,热点区增加与近年来乌鲁木齐大力推行生态保护及修复等措施有极大关联,冷点区增加与城市建成区无序扩张及土地开发等一系列人类活动有密切关系。     

京津冀区域工业水污染排放空间密度特征研究

京津冀地区是海河流域水资源开发程度最高的流域,水资源严重短缺,废水排污量大,水资源、水环境已成为制约京津冀地区发展的全局性限制因素。考虑到京津冀严峻的水环境形势和流域上下游污染影响的现状,明确水污染物空间排放特征,对于划分水污染空间管理分区、设计分区污染物减排方案,推动京津冀水污染协同治理都有着积极的作用。文章基于京津冀企业污染源环境统计数据,应用核密度空间分析工具模拟了京津冀工业源水污染排放空间集聚特征。结果显示,(1)京津冀工业源COD和NH_3-N排放最多的行业集中在造纸和纸制品业、农副食品加工业、化学原料和化学制品制造业、纺织业等十大行业。(2)京津冀工业源COD排放主要集中在邯郸、天津、张家口和秦皇岛,合计占京津冀区域COD总排放的56%;工业源NH_3-N排放主要集中在邯郸、天津,合计占京津冀区域NH_3-N总排放的52%。(3)COD排放密度大于1 t?km~(-2)的区域面积占京津冀区域面积的20.4%,在石家庄、保定、秦皇岛、天津、邢台、唐山等市形成显著的COD污染集聚区位;氮排放密度高于0.2 t?km~(-2)的区域面积占京津冀区域面积的15.9%,污染集聚区主要位于天津、承德、石家庄等地。根据水污染空间密度分析结果,针对京津冀不同区域不同行业制定有针对性的水污染防治措施,根据上述重点污染行业和污染排放密集区域,加强水污染综合管控力度,开展区域协同综合治理,提升区域水环境质量。     

辽河流域工业废水污染物排放的时空变化规律研究

采用等标污染负荷法,针对改革开放和产业结构大调整前后的5个典型年份,从工业污染源空间特征出发分析了辽宁省辽河流域工业废水污染物排放的变化趋势,并讨论了重点污染物排放的时空变化规律。结果表明：辽河流域工业污染源主要分布在辽宁中部城市群及沿海地区,且有不断集中的趋势;工业废水排放总量以及污染物排放总量都呈不断减少的趋势;辽河流域主要污染物为COD、氨氮、挥发酚和石油类,有机污染物排放量高于无机污染物;重金属类污染物变化较大,1985—1999年重金属类以汞为主,2002—2005年期间重金属类以镉、铅和六价铬为主,2008年重金属类以铅、砷和六价铬为主;氨氮、COD的主要排放区域变化不大,挥发酚、石油类、铅、砷的主要排放区域变化很大,污染物排放区域转移趋势明显。     

辽河流域工业废水主要污染物排放强度单元差异分析

污染物排放强度反映了单位新创造经济价值的环境负荷大小,运用均方差赋权法对辽宁省辽河流域6个控制单元2006年度废水量、COD和氨氮等3种主要污染物排放强度的区域差异进行了评价,结果表明,当年3种主要污染物排放强度综合评价的平均水平为0.4411,低于和高于平均水平的单元各有3个,其中浑河中游单元的评价值最低,只有平均水平的11.74%;辽河上游单元的污染物排放强度综合评价值最高,是平均水平的1.8倍,更是浑河中游的15.6倍。然后,以流域污染物排放强度最低值作为流域污染物排放强度目标值,计算各单元污染物排放强度减排潜力,结果表明,各控制单元主要污染物排放强度减排潜力具有显著的区域间差异。最后,应用＂污染贡献率＂这一指标,分析了辽河流域COD和氨氮排放的重污染行业以及单元分布,指出了各单元控制的重点。     

长三角区域地方生态环境标准发展的时空格局演变及其影响机制研究

地方生态环境标准在地方环境管理、产业升级与绿色发展中起着重要作用,推动长三角地方生态环境标准的发展有助于推动区域生态环境一体化保护和高质量发展。通过采用主成分分析、空间自相关检验等分析法,探索长三角区域地方生态环境标准发展时空格局演变特征及其影响因素。研究表明：自“十二五”时期以来,长三角省级层面年均发布的生态环境标准数量由6.4上升至33,市级层面由0.2上升至13.5,增长趋势显著。现阶段约60%的水、气、土及生态领域的地方标准在省级层面制定发布,40%的地方标准由市级部门制定发布,研究领域不同,地方标准的发展速度与发展水平差异显著。Moran’s I指数分析显示地方市级生态环境标准在空间上呈现自相关较弱、相邻市域发展不平衡的特征,长三角南部地区地方标准发展增幅相对较快;区域绿色发展全局空间集聚效应显著,发展低值区主要聚集在长三角北部,高值区域同样聚集在南部地区,但城市绿色发展与地方生态环境标准之间的空间相关性显著性较弱。地方标准与经济发展显著相关,地方标准总量增加1%,上海、江苏、浙江与安徽的经济分别增长0.952%、0.766%、0.883%与0.915%,长三角区域经济增长为...     

1975年以来黑河中游地区土地利用／覆被变化时空演变

以西北干旱区重要的内陆河——黑河中游地区为研究区域,采用1975、1987、1992、2001和2010年遥感影像为基础数据,选取土地利用变化幅度、动态度和土地利用强度指数3个指标,并结合转移矩阵,采用相关性分析和主成分分析方法,对该地区近35a来的土地利用／覆被变化及其驱动力进行综合分析。结果表明,1975--2010年,研究区耕地和城乡居民工矿用地所占比例持续上升,分别由14．14％和1．52％增加到19．74％和1．98％;草地和水域面积则分别减少3．87×10^4和0．41×10^4hm2。各土地利用类型中以耕地的土地利用变化强度指数最高,达0．1604％,草地次之。研究区土地利用类型间主要转移方向为：草地转化为耕地和未利用地,未利用地转化为耕地和草地,林地与草地问相互转化。导致黑河中游地区土地利用发生上述变化的主要驱动力为社会经济因素。     

南疆西部不同植被类型NDVI对风沙灾害的响应

不同植被类型对风沙灾害的响应研究有助于风沙灾害的遥感监测评估和防灾减灾工作。采用传统统计学和空间自相关方法,利用MODIS的每日反射率和土地类型产品,分析了风沙灾害后南疆西部不同植被类型NDVI的变化规律及其空间分布特征。结果表明：风沙灾害后,研究区不同植被类型NDVI的差异在增大,NDVI变化值在一0．3865~0．4148之间,NDVI减小的面积占整个研究区面积的54．98％,变异系数增大值在2．44％～36．75％之间;受到风沙灾害的影响,研究区植被NDVI全局Moran’SI系数从0．7982减小到0．6786,但在空间上仍存在显著的正相关性,具有显著的空间集聚特征;由NDVI差值的局部空间自相关指标集聚图以及不同植被类型NDVI差值的空间关联区域面积百分比,可以发现落叶针叶林和裸地或低植被覆盖地受风沙灾害的影响较小,郁闭灌丛和作物受风沙灾害的影响较大。上述关于风沙灾害后南疆西部不同植被类型的变化及其空间格局的研究结果,不仅证明了防护林的风沙阻挡作用,也可以为风沙灾害的防治工作提供参考依据。     

Thresholds in Songbird Occurrence in Relation to Landscape Structure

Abstract:  Theory predicts the occurrence of threshold levels of habitat in landscapes, below which ecological processes change abruptly. Simulation models indicate that below critical thresholds, fragmentation of habitat influences patch occupancy by decreasing colonization rates and increasing rates of local extinction. Uncovering such putative relationships is important for understanding the demography of species and in developing sound conservation strategies. Using segmented logistic regression, we tested for thresholds in occurrence of 15 bird species as a function of the amount of suitable habitat at multiple scales (150–2000-m radii). Suitable habitat was defined quantitatively based on previously derived, spatially explicit distribution models for each species. The occurrence of 10 out of 15 species was influenced by the amount of habitat at a landscape scale (≥500-m radius). Of these species all but one were best predicted by threshold models. Six out of nine species exhibited asymptotic thresholds; the effects of habitat loss intensified at low amounts of habitat in a landscape. Landscape thresholds ranged from 8.6% habitat to 28.7% (     = 18.5 ± 2.6%[95% CI]). For two species landscape thresholds coincided with sensitivity to fragmentation; both species were more likely to occur in large patches, but only when the amount of habitat in a landscape was low. This supports the fragmentation threshold hypothesis. Nevertheless, the occurrence of most species appeared to be unaffected by fragmentation, regardless of the amount of habitat present at landscape extents. The thresholds we identified may be useful to managers in establishing conservation targets. Our results indicate that findings of landscape-scale studies conducted in regions with relatively high proportions of habitat and low fragmentation may not be applicable in regions with low habitat proportions and high fragmentation.     

Nonparametric spatial covariance functions: Estimation and testing

Spatial autocorrelation techniques are commonly used to describe genetic and ecological patterns. To improve statistical inference about spatial covariance, we propose a continuous nonparametric estimator of the covariance function in place of the spatial correlogram. The spline correlogram is an adaptation of a recent development in spatial statistics and is a generalization of the commonly used correlogram. We propose a bootstrap algorithm to erect a confidence envelope around the entire covariance function. The meaning of this envelope is discussed. Not all functions that can be drawn inside the envelope are candidate covariance functions, as they may not be positive semidefinite. However, covariance functions that do not fit, are not supported by the data. A direct estimate of the L₀ spatial correlation length with associated confidence interval is offered and its interpretation is discussed. The spline correlogram is found to have high precision when applied to synthetic data. For illustration, the method is applied to electrophoretic data of an alpine grass (Poa alpina).     

Spatial structure effects on the detection of patches boundaries using local operators

Landscapes exhibit various degrees of spatial heterogeneity according to the differential intensity and interactions among processes and disturbances that they are subjected to. The management of these spatially dynamical landscapes requires that we can accurately map them and monitor the evolution of their spatial arrangement through time. Such a mapping requires first the delineation of various spatial features present in the landscape such as patches and their boundaries. However, there are several environmental (spatial variability) as well as technical (spatial resolution) factors that impair our ability to accurately delineate patches and their boundaries as polygons. Here, we investigate how the spatial structure and spatial resolution of the data affect the accuracy of detecting patches and their boundaries over simulated landscapes and real data. Simulated landscapes consisted of two patches with parameterized spatial properties (patches’ level of spatial autocorrelation, mean value and variance) separated by a boundary of known location. Real data allowed the investigation of a more complex landscape where there is a known transition between two forest domains with unknown spatial properties. Boundary locations are defined using the lattice-wombling edge detector at various aggregation levels and the degree of patch homogeneity is determined using Getis-Ord’s G*. Results show that boundary detection using a local edge detector is greatly affected by the spatial conditions of the data, namely variance, abruptness of the spatial gradient between two patches and patches’ level of spatial autocorrelation. They also suggest that data aggregation is not a panacea for bringing out the ecological process creating the patches and that indicators derived from local measures of spatial association can be complementary tools for analysing spatial structures affecting boundary delineation.

Effects of Land Use on Threatened Species

Abstract:  There is widespread agreement that biodiversity loss must be reduced, yet to alleviate threats to plant and animal species, the forces driving these losses need to be better understood. We searched for explanatory variables for threatened-species data at the country level through land-use information instead of previously used socioeconomic and demographic variables. To explain the number of threatened species in one country, we used information on land-use patterns in all neighboring countries and on the extent of the country's sea border. We carried out multiple regressions of the numbers of threatened species as a function of land-use patterns, and we tested various specifications of this function, including spatial autocorrelation. Most cross-border land-use patterns had a significant influence on the number of threatened species, and land-use patterns explained the number of threatened species better than less proximate socioeconomic variables. More specifically, our overall results showed a highly adverse influence of plantations and permanent cropland, a weaker negative influence of permanent pasture, and, for the most part, a beneficial influence of nonarable lands and natural forest. Surprisingly, built-up land also showed a conserving influence on threatened species. The adverse influences extended to distances between about 250 km (plants) and 2000 km (birds and mammals) away from where the species threat was recorded, depending on the species. Our results highlight that legislation affecting biodiversity should look beyond national boundaries.     

The influence of environmental spatial structure on the life-history traits and diversity of species in a metacommunity

Several models have been proposed to understand how so many species can coexist in ecosystems. Despite evidence showing that natural habitats are often patchy and fragmented, these models rarely take into account environmental spatial structure. In this study we investigated the influence of spatial structure in habitat and disturbance regime upon species’ traits and species’ coexistence in a metacommunity. We used a population-based model to simulate competing species in spatially explicit landscapes. The species traits we focused on were dispersal ability, competitiveness, reproductive investment and survival rate. Communities were characterized by their species richness and by the four life-history traits averaged over all the surviving species. Our results show that spatial structure and disturbance have a strong influence on the equilibrium life-history traits within a metacommunity. In the absence of disturbance, spatially structured landscapes favour species investing more in reproduction, but less in dispersal and survival. However, this influence is strongly dependent on the disturbance rate, pointing to an important interaction between spatial structure and disturbance. This interaction also plays a role in species coexistence. While spatial structure tends to reduce diversity in the absence of disturbance, the tendency is reversed when disturbance occurs. In conclusion, the spatial structure of communities is an important determinant of their diversity and characteristic traits. These traits are likely to influence important ecological properties such as resistance to invasion or response to climate change, which in turn will determine the fate of ecosystems facing the current global ecological crisis.     

The performance of state-of-the-art modelling techniques depends on geographical distribution of species

We explored the effects of prevalence, latitudinal range and clumping (spatial autocorrelation) of species distribution patterns on the predictive accuracy of eight state-of-the-art modelling techniques: Generalized Linear Models (GLMs), Generalized Boosting Method (GBM), Generalized Additive Models (GAMs), Classification Tree Analysis (CTA), Artificial Neural Network (ANN), Multivariate Adaptive Regression Splines (MARS), Mixture Discriminant Analysis (MDA) and Random Forest (RF). One hundred species of Lepidoptera, selected from the Distribution Atlas of European Butterflies, and three climate variables were used to determine the bioclimatic envelope for each butterfly species. The data set consisting of 2620 grid squares 30′ × 60′ in size all over Europe was randomly split into the calibration and the evaluation data sets. The performance of different models was assessed using the area under the curve (AUC) of a receiver operating characteristic (ROC) plot. Observed differences in modelling accuracy among species were then related to the geographical attributes of the species using GAM. The modelling performance was negatively related to the latitudinal range and prevalence, whereas the effect of spatial autocorrelation on prediction accuracy depended on the modelling technique. These three geographical attributes accounted for 19–61% of the variation in the modelling accuracy. Predictive accuracy of GAM, GLM and MDA was highly influenced by the three geographical attributes, whereas RF, ANN and GBM were moderately, and MARS and CTA only slightly affected. The contrasting effects of geographical distribution of species on predictive performance of different modelling techniques represent one source of uncertainty in species spatial distribution models. This should be taken into account in biogeographical modelling studies and assessments of climate change impacts.     

Pseudoreplication in Tropical Forests and the Resulting Effects on Biodiversity Conservation

Tropical forest ecosystems are threatened by habitat conversion and other anthropogenic actions. Timber production forests can augment the conservation value of primary forest reserves, but studies of logging effects often yield contradictory findings and thus inhibit efforts to develop clear conservation strategies. We hypothesized that much of this variability reflects a common methodological flaw, simple pseudoreplication, that confounds logging effects with preexisting spatial variation. We reviewed recent studies of the effects of logging on biodiversity in tropical forests (n = 77) and found that 68% were definitively pseudoreplicated while only 7% were definitively free of pseudoreplication. The remaining proportion could not be clearly categorized. In addition, we collected compositional data on 7 taxa in 24 primary forest research plots and systematically analyzed subsets of these plots to calculate the probability that a pseudoreplicated comparison would incorrectly identify a treatment effect. Rates of false inference (i.e., the spurious detection of a treatment effect) were >0.5 for 2 taxa, 0.3–0.5 for 2 taxa, and <0.3 for 3 taxa. Our findings demonstrate that tropical conservation strategies are being informed by a body of literature that is rife with unwarranted inferences. Addressing pseudoreplication is essential for accurately assessing biodiversity in logged forests, identifying the relative merits of specific management practices and landscape configurations, and effectively balancing conservation with timber production in tropical forests. Pseudoreplicación en Bosques Tropicales y Efectos Resultantes Sobre la Conservación de Biodiversidad