基于ESDA的省域空气污染空间特征研究

运用探索性空间数据分析(ESDA)方法,对中国31个省份的空气质量指数和PM2.5进行了空间分布和时空演化分析,结果显示,中国省域空气综合污染存在较强的空间自相关性,可对相邻区域空气质量造成影响.PM2.5污染与空气综合污染的空间相关趋势保持了高度的一致性,体现主导污染物的地位.辽宁与华北省份北京、天津、河北、山东,以及部分华中省份河南、湖北及江苏形成了较为稳定的高污染集聚区,较轻污染的集聚大多分布在西部、西南和东南沿海区域.基于空间特征的污染治理措施,可实现功效发挥的最大化.     

Natural forest regrowth as a proxy variable for agricultural land abandonment in the Swiss mountains: a spatial statistical model based on geophysical and socio-economic variables

In many European mountain regions, natural forest regrowth on abandoned agricultural land and the related consequences for the environment are issues of increasing concern. We developed a spatial statistical model based on multiple geophysical and socio-economic variables to investigate the pattern of natural forest regrowth in the Swiss mountain area between the 1980s and 1990s. Results show that forest regrowth occurred primarily in areas with low temperature sum, intermediate steepness and soil stoniness as well as close to forest edges and relatively close to roads. Model results suggest that regions with weak labor markets are favored in terms of land abandonment and forest regrowth. We could not find an effect of population change on land abandonment and forest regrowth. Therefore, we conclude that decision makers should consider non-linearities in the pattern of forest regrowth and the fact that labor markets have an effect on land abandonment and forest regrowth when designing measures to prevent agricultural land abandonment and natural forest regrowth in the Swiss mountains.     

基于微空间单元的岩溶峡谷区土地利用结构演变

采用2004年、2010年和2015年分辨率均为2.5 m的SPOT等遥感影像和社会经济数据,创建200 m×200 m网格的微空间单元,计算单元网格内地类面积占比、土地利用程度综合指数,并结合双变量局部空间自相关和热点分析法,深入研究岩溶峡谷区土地利用结构演变特征。结果表明：（1）2004年林地、耕地、聚落和裸岩裸土网格内平均占比分别为6.46%、11.76%、0.87%、28.86%,2010年分别为10.36%、8.77%、1.27%、27.13%,2015年分别为9.13%、10.30%、26.18%、1.29%;（2）各种土地利用类型在空间分布上具有明显的空间差异特征;（3）耕地热点区呈减少趋势,且后期主要集中在研究区西南部,林地热点区总体增加,裸岩裸土极热点区逐渐向研究区东北部收缩。而近几年土地利用程度热点区逐渐向研究区西北部延伸,花椒林则主要分布于北部干热河谷地带。简而言之,花江峡谷区林地面积增加,且逐渐向集中连片演变,而裸岩地逐渐向峡谷区北部以及东北部收缩,研究区农户生计多样化发展及交通条件的改善,使得土地压力减小,生态环境逐渐转好。本文以典型岩溶地貌单元为代表,揭示中国西南岩溶山地的土地利用演变规律及内部差异性,对研究更大尺度土地利用演变特征与规律具有重要意义。     

中国臭氧时空分布特征及与社会经济因素影响分析

本文根据2015～2017年中国大陆338个城市空气质量监测站臭氧(O_3)浓度数据,综合利用空间插值法、全局自相关法和地理加权回归模型(GWR),探讨了O_3浓度的时空变化特征及其与社会经济因素的关系。结果表明,2015～2017年中国大陆338个城市的O_3日最大8小时浓度为2～300μg/m~3,其中超标天数比例为5. 9%,323个城市达标率在85%以上; O_3月均值变化曲线基本呈"单峰状",5月达到峰值,12月最低; O_3浓度季节变化为夏季春季秋季冬季; O_3日变化特征为夜间到清晨O_3浓度很低,上午8∶00左右开始升高,下午16∶00达到峰值;中国华北地区、华东地区和华中地区O_3污染严重,华南地区、西南地区、西北地区和东北地区整体污染较低。O_3浓度在全国尺度上的集聚性呈上升趋势,GWR表明,人口密度、人均私家车保有量与O_3浓度显著正相关,第一产业占比与O_3浓度显著负相关。     

广州市空气可吸入颗粒物污染现状研究

根据近年来广州市区空气质量监测资料,对广州市空气可吸入颗粒物（PM10）的污染水平、时空变化特征进行了分析、评价。结果表明,2007年广州市PM10平均浓度为0．077mg／m^3,符合国家空气质量二级标准。得益于空气污染控制取得的成效,近5年来PM10年均浓度呈下降趋势。受气候因素影响,PM10污染季节变化明显,冬季节较高,夏季节浓度较低;在空间分布上PM10污染呈现区域性发展趋势。     

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.

Spatial smoothing techniques for the assessment of habitat suitability

Precise knowledge about factors influencing the habitat suitability of a certain species forms the basis for the implementation of effective programs to conserve biological diversity. Such knowledge is frequently gathered from studies relating abundance data to a set of influential variables in a regression setup. In particular, generalised linear models are used to analyse binary presence/absence data or counts of a certain species at locations within an observation area. However, one of the key assumptions of generalised linear models, the independence of observations is often violated in practice since the points at which the observations are collected are spatially aligned. In this paper, we describe a general framework for semiparametric spatial generalised linear models that allows for the routine analysis of non-normal spatially aligned regression data. The approach is utilised for the analysis of a data set of synthetic bird species in beech forests, revealing that ignorance of spatial dependence actually may lead to false conclusions in a number of situations.

Modelling directional spatial processes in ecological data

Distributions of species, animals or plants, terrestrial or aquatic, are influenced by numerous factors such as physical and biogeographical gradients. Dominant wind and current directions cause the appearance of gradients in physical conditions whereas biogeographical gradients can be the result of historical events (e.g. glaciations). No spatial modelling technique has been developed to this day that considers the direction of an asymmetric process controlling species distributions along a gradient or network. This paper presents a new method that can model species spatial distributions generated by a hypothesized asymmetric, directional physical process. This method is an eigenfunction-based spatial filtering technique that offers as much flexibility as the Moran's eigenvector maps (MEM) framework; it is called asymmetric eigenvector maps (AEM) modelling. Information needed to construct eigenfunctions through the AEM framework are the spatial coordinates of the sampling or experimental sites, a connexion diagram linking the sites to one another, prior information about the direction of the hypothesized asymmetric process influencing the response variable(s), and optionally, weights attached to the edges (links). To illustrate how this new method works, AEM is compared to MEM analysis through simulations and in the analysis of an ecological example where a known asymmetric forcing is present. The ecological example reanalyses the dietary habits of brook trout (Salvelinus fontinalis) sampled in 42 lakes of the Mastigouche Reserve, Québec.     

Effects of the spatial pattern of disturbance on the patch-occupancy dynamics of juniper–pine open woodland

Typically, studies of the disturbance effect on metapopulation dynamics are limited to understanding the effect of habitat loss although, recently, the spatial pattern of the disturbance has been shown to influence dynamics. In this study, we used a stochastic patch-dynamic model to investigate the effects of spatial disturbance patterns on the persistence of an open woodland community of Juniperus spp. and Pinus spp. First, we estimated patch-occupancy dynamics by using the coefficients that best predicted the occupancy observed in 1998 based on occupancy data from 1957. Next, we evaluated the effects of the rate and pattern of the disturbance on the extinction probability. In modeling the disturbance, we considered (1) the degree of disturbance produced by scenarios of complete destruction or degradation (with the potential for recolonization), (2) the overall rate of disturbance, and (3) the spatial autocorrelation of habitat destruction. Twenty 40-year simulations predicted a 25% increase in the number of patches, and when 50% of the habitat was removed, the impact was more pronounced after complete destruction than it was after degradation of the area. Predictions based on scenarios of complete destruction, including random, contiguous, Brownian, and autoregressive noise, demonstrated that the impact of disturbance depends upon the spatial structure of the disturbance regimen. The autocorrelated structure of the disturbance regimen had the greatest impact on patch persistence. Patch-occupancy was higher after 20 40-year simulations when habitat loss was randomly distributed than when it followed an autocorrelated patch destruction, which was simulated using autoregressive noise to produce 50% habitat destruction. In addition, while habitat loss was negatively linearly correlated with patch persistence when habitat destruction was randomly distributed, a dramatic transition shift occurred when habitat destruction was simulated following an autoregressive spatial distribution after a certain threshold of habitat destruction (40% of the actual open woodland habitat). Our study suggests that the spatial patterns of the disturbance should be considered when predicting the consequences of fragmentation and improving management strategies.     

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.