辽中南城市群城市化及生态效应

20世纪90年代以来,我国进入快速城市化阶段,城市化引发的一系列环境问题不容忽视。本研究基于1995—2020年夜光遥感数据和土地利用数据,提取灯光指数测度辽中南城市群城市化水平,借助InVEST模型评价生境质量,并使用相关性分析方法和地理加权回归模型探讨辽中南城市群城市化水平与生境质量的关系。结果表明: 灯光指数在1995—2020年间增加了0.14,城市化水平不断提高,且呈现东低西高的格局;生境质量下降0.005,呈东高西低的格局,生态环境变差;辽中南城市群城市化水平与生境质量呈现显著的空间负相关关系,且城市化水平对生境质量的负面影响逐渐减少。为了缓解城市化带来的生境退化,实现区域社会经济协调可持续发展,迫切需要采取一系列措施,如:划定生态保护红线、提高土地集约利用度、划定城镇边界、促进区域一体化协调发展等。     

A comparison of approaches for modelling the occurrence of marine animals

Approaches for modelling the distribution of animals in relation to their environment can be divided into two basic types, those which use records of absence as well as records of presence and those which use only presence records. For terrestrial species, presence–absence approaches have been found to produce models with greater predictive ability than presence-only approaches. This study compared the predictive ability of both approaches for a marine animal, the harbour porpoise (Phoceoena phocoena). Using data on the occurrence of harbour porpoises in the Sea of Hebrides, Scotland, the predictive abilities of one presence–absence approach (generalised linear modelling—GLM) and three presence-only approaches (Principal component analysis—PCA, ecological niche factor analysis—ENFA and genetic algorithm for rule-set prediction—GARP) were compared. When the predictive ability of the models was assessed using receiver operating characteristic (ROC) plots, the presence–absence approach (GLM) was found to have the greatest predictive ability. However, all approaches were found to produce models that predicted occurrence significantly better than a random model and the GLM model did not perform significantly better than ENFA and GARP. The PCA had a significantly lower predictive ability than GLM but not the other approaches. In addition, all models predicted a similar spatial distribution. Therefore, while models constructed using presence–absence approaches are likely to provide the best understanding of species distribution within a surveyed area, presence-only models can perform almost as well. However, careful consideration of the potential limitations and biases in the data, especially with regards to representativeness, is needed if the results of presence-only models are to be used for conservation and/or management purposes. Guest editor: V. D. Valavanis Essential Habitat Mapping in the Mediterranean     

基于遥感生态指数的道路网络生态效应分析——以福州市为例

以2000年和2016年两期Landsat影像为基础数据源,借助遥感生态指数（RSEI）对福州市生态环境进行评价,在此基础上,从道路缓冲区、城乡梯度带、剖面线三种不同取样方法定量探讨RSEI对路网的响应机制;再以500 m×500 m、1000 m×1000 m、1500 m×1500 m、2000 m×2000 m、2500 m×2500 m、3000 m×3000 m不同尺度的网格划分空间单元,运用全局空间自相关、地理加权回归分析等方法分析了道路核密度（KDE）和RSEI及其之间关系的空间异质性。结果表明：从2000年到2016年,福州市生态环境好的区域面积增幅大于生态环境差的区域面积,生态环境质量向好的方向发展。各类型道路缓冲区的RSEI变化规律都是呈从0 m到3000 m逐渐上升的趋势,其中国道、省道、县道、乡镇道路影响的阈值分别在900、900、450、750 m左右。在城乡梯度分析中,RSEI曲线的变化规律都是随着与行政中心距离的增大而增大,到达一定阈值后趋于平缓,甚至还有小幅度的下降,区级的影响阈值在20 km左右,县级的影响阈值在12 km左右;而KDE曲线的变化规律与RSEI相反,其变化阈值与RSEI正好对应。剖面线所经过的行政中心处,其RSEI为低值,KDE为高值,西北方向的内陆地区RSEI高于东南方向的沿海地区。在多尺度的地理加权回归分析中,1500 m×1500 m和2000 m×2000 m这两个网格单元采样下的空间集聚性较强,空间异质性明显,总体上来看,RSEI与KDE呈现负相关关系,且相关关系存在空间分异,负回归系数主要分布在研究区的中心区域。研究结果可为福州市生态建设和路网规划提供参考依据。     

Predicting species distributions: a critical comparison of the most common statistical models using artificial species

Aim To test statistical models used to predict species distributions under different shapes of occurrence–environment relationship. We addressed three questions: (1) Is there a statistical technique that has a consistently higher predictive ability than others for all kinds of relationships? (2) How does species prevalence influence the relative performance of models? (3) When an automated stepwise selection procedure is used, does it improve predictive modelling, and are the relevant variables being selected? Location We used environmental data from a real landscape, the state of California, and simulated species distributions within this landscape. Methods Eighteen artificial species were generated, which varied in their occurrence response to the environmental gradients considered (random, linear, Gaussian, threshold or mixed), in the interaction of those factors (no interaction vs. multiplicative), and on their prevalence (50% vs. 5%). The landscape was then randomly sampled with a large (n = 2000) or small (n = 150) sample size, and the predictive ability of each statistical approach was assessed by comparing the true and predicted distributions using five different indexes of performance (area under the receiver‐operator characteristic curve, Kappa, correlation between true and predictive probability of occurrence, sensitivity and specificity). We compared generalized additive models (GAM) with and without flexible degrees of freedom, logistic regressions (general linear models, GLM) with and without variable selection, classification trees, and the genetic algorithm for rule‐set production (GARP). Results Species with threshold and mixed responses, additive environmental effects, and high prevalence generated better predictions than did other species for all statistical models. In general, GAM outperforms all other strategies, although differences with GLM are usually not significant. The two variable‐selection strategies presented here did not discriminate successfully between truly causal factors and correlated environmental variables. Main conclusions Based on our analyses, we recommend the use of GAM or GLM over classification trees or GARP, and the specification of any suspected interaction terms between predictors. An expert‐based variable selection procedure was preferable to the automated procedures used here. Finally, for low‐prevalence species, variability in model performance is both very high and sample‐dependent. This suggests that distribution models for species with low prevalence can be improved through targeted sampling.     

Penalized local polynomial regression for spatial data

When performing spatial regression analysis in environmental data applications, spatial heterogeneity in the regression coefficients is often observed. Spatially varying coefficient models, including geographically weighted regression and spline models, are standard tools for quantifying such heterogeneity. In this paper, we propose a spatially varying coefficient model that represents the spatially varying parameters as a mixture of local polynomials at selected locations. The local polynomial parameters have attractive interpretations, indicating various types of spatial heterogeneity. Instead of estimating the spatially varying regression coefficients directly, we develop a penalized least squares regression procedure for the local polynomial parameter estimation, which both shrinks the parameter estimation and penalizes the differences among parameters that are associated with neighboring locations. We develop confidence intervals for the varying regression coefficients and prediction intervals for the response. We apply the proposed method to characterize the spatially varying association between particulate matter concentrations ( ${\text{PM}}_{2.5}$) and pollutant gases related to the secondary aerosol formulation in China. The identified regression coefficients show distinct spatial patterns for nitrogen dioxide, sulfur dioxide, and carbon monoxide during different seasons.     

基于回归和地理加权回归Kriging的土壤有机质空间插值

基于地形因子与土壤有机质的相关分析,选取相对高程和汇流动力指数作为辅助变量.以普通克里格(OK)作对照,比较地理加权回归克里格(GWRK)与回归克里格(RK)在土壤有机质空间插值及制图上的精度与效果.结果表明:土壤有机质含量与相对高程呈显著正相关,与汇流动力指数呈显著负相关;经半方差分析,土壤有机质及其插值残差具有强烈的空间自相关;对验证集中98个样点的精度加以分析,RK法插值结果的平均误差(ME)、平均绝对误差(MAE)、均方根误差(RMSE)较OK法分别降低39.2％、17.7％和20.6％,相对提高度(RI)为20.63,GWRK法插值结果的ME、MAE、RMSE较OK法分别降低60.6％、23.7％、27.6％,RI为59.79.与OK相比,考虑了辅助变量的RK和GWRK明显提高了插值精度;GWRK考虑了样点位置,成图效果更加精细,对土壤有机质的局部模拟效果优于RK.     

基于土地利用及植被覆盖变化的黄河源区生境质量时空变化特征

位于青藏高原腹地的黄河源地区生态环境脆弱,面临生物多样性锐减、生态系统退化等问题,黄河源区生态系统保护及其高质量发展已成为国家的重点战略之一。土地利用与植被覆盖是影响生境质量的重要因素,定量化土地利用方式、强度及格局和植被覆盖格局对生态质量影响的研究越来越受到关注,但其对黄河源区生态质量的耦合效应尚不明确。基于2000年和2015年黄河源区土地利用类型及生长季归一化植被指数（NDVI）,采用InVEST模型探究了不同时期黄河源区生境质量时空变化,并采用地理加权回归（GWR）模型揭示了生境质量对土地利用和植被覆盖变化的空间响应特征。结果表明,2000年与2015年土地利用类型变化主要为未利用土地向草地的转移。植被覆盖变化方面,源区生长季NDVI整体上升。从生境质量的空间分布来看,黄河源区生境质量总体呈现南高北低的空间格局,高值分布在南部及中部地区,低值分布在北部布青山、东北部高海拔区及黄河乡的黄河沿岸。相较于2000年,2015年黄河源区生境质量平均提高11.47%。草地面积和NDVI与生境质量均呈显著正相关关系,其中NDVI是提高黄河源区生境质量的重要驱动因子。研究结果突出了NDVI对提高黄河源区生境质量的主导作用,可为未来源区生态保护提供借鉴。     

基于地理加权回归模型探究环境异质性对秦岭大熊猫空间利用的影响

环境异质性对野生动物分布的影响具有明显的空间不均匀性。传统分析中多采用经典线性回归模型来量化野生动物分布与环境变量之间的关系,难以准确反映物种-环境关系的空间异质特征。地理加权回归(GWR)是近年来提出的一种新的空间分析方法,通过将空间结构嵌入线性回归模型中,以此来探测空间关系的非均匀性。以秦岭大熊猫为例,应用GWR模型分析大熊猫空间分布与环境异质性特征之间的潜在关系,并同经典的全局最小二乘回归法(OLS)进行比较。结果表明,GWR模型的AIC、R²和校正R²均显著优于OLS模型,GWR模型的局部回归系数估计能够更加深刻地揭示大熊猫空间分布与环境变量间的复杂空间关系,且GWR模型能够为物种的科学保护提供更加有效的理论支撑。因此,GWR模型可为探究物种-环境关系的空间异质特征提供一种新的方法,在物种栖息地选择与利用研究中具有一定的应用前景。     

神农架林区人类活动与生境质量的空间关系

近几十年来,全球范围内人类活动的加剧对于区域生态系统产生了深远影响,对区域可持续发展造成了严重威胁。科学测度人类活动与生境质量的关系,可为制定有效的生态系统保护政策提供科学依据。基于1995年、2000年、2005年、2010年和2015年土地利用变化数据,借助InVEST模型测度神农架林区生境质量的时空分布特征,并且结合多源数据测度其人类足迹指数的时空分布,综合运用双变量空间自相关和地理加权回归,对神农架林区人类活动与生境质量的空间关系以及人类活动对生境质量的影响进行分析。研究结果显示：（1）1995-2015年间,神农架林区生境质量水平基本保持稳定,无剧烈变化,超过60%的区域处于较高生境质量和高生境质量类;（2）1995-2015年间,神农架林区人类足迹指数呈现"西部低,东北高"的空间分布特征和"两极化"的发展趋势;（3）1995-2015年间,神农架林区人类活动与生境质量之间存在显著的空间依赖性,二者呈现显著的空间负相关,林区中部人类活动对生境质量的影响以负面效应为主且愈加显著,人类活动会导致生境质量的恶化。研究结果表明未来的人类活动管理以及生态系统保护政策制定需要充分考虑二者之间的空间依赖效应,科学合理划定保护范围,提高生态保护规划实施效果。     

Spatial heterogeneity in habitat selection: Nest site selection by greater prairie-chickens

Ecological relationships of animals and their environments are known to vary spatially and temporally across scales. However, common approaches for evaluating resource selection by animals assume that the processes of habitat selection are stationary across space. The assumption that habitat selection is spatially homogeneous may lead to biased inference and ineffective management. We present the first application of geographically weighted logistic regression to habitat selection by a wildlife species. As a case study, we examined nest site selection by greater prairie-chickens at 3 sites with different ecological conditions in Kansas to assess whether the relative importance of habitat features varied across space. We found that 1) nest sites were associated with habitat conditions at multiple spatial scales, 2) habitat associations across spatial scales were correlated, and 3) the influence of habitat conditions on nest site selection was spatially explicit. Post hoc analyses revealed that much of the spatial variability in habitat selection processes was explained at a regional scale. Moreover, habitat features at local spatial scales were more strongly associated with nest site selection in unfragmented grasslands managed intensively for cattle production than they were in fragmented grasslands within a matrix of farmland. Female prairie-chickens exhibited spatial variability in nest site selection at multiple spatial scales, suggesting plasticity in habitat selection behavior. Our results highlight the importance of accounting for spatial heterogeneity when evaluating the ecological effects of habitat components. © 2013 The Wildlife Society.     

Spatial heterogeneity of climate and land-cover constraints on distributions of tick-borne pathogens

Aim To compare the geographical distributions of two tick‐borne pathogens vectored by different tick species, to examine the relative importance of climate, land cover and host density in structuring these distributions, and to assess the spatial variability of these environmental constraints across the species ranges. Location South‐central and south‐eastern North America. Methods Presence/absence data for two tick‐borne pathogens, Ehrlichia chaffeensis and Anaplasma phagocytophilum, were obtained for 567 counties from a regional data base based on white‐tailed deer (Odocoileus virginianus) serology. Environmental variables describing climate, land cover and deer density were calculated for these counties. Global logistic regression analysis was used to screen the environmental variables and select a parsimonious subset of predictors. Local analysis was carried out using geographically weighted regression (GWR) to explore spatial variability in the parameters of the regression models. Cluster analysis was applied to the GWR output to identify zones with distinctive species–habitat relationships. Results Global habitat models for E. chaffeensis and A. phagocytophilum included temperature, humidity, precipitation and forest cover as explanatory variables. The E. chaffeensis model also included forest fragmentation, whereas the A. phagocytophilum model included deer density. Local analyses revealed that climate was the primary correlate of pathogen presence in the eastern portion of the study area, whereas forest cover and fragmentation constrained the western range boundaries. Habitat relationships for all variables were weak in and around the Mississippi Delta. Main conclusions Efforts to model pathogen and disease ranges, and to predict shifts in response to global change should consider future scenarios of land‐cover change as well as climate change, and should address the possibility of spatial heterogeneity in species–habitat relationships. The methods presented here outline an approach for objectively delineating geographical zones with similar species–environment relationships, which can then be used to stratify landscapes for the purposes of further explanatory and predictive modelling.     

基于地理加权回归的渤海沙氏下鱵鱼仔稚鱼栖息地指数

为研究渤海鱼类资源早期补充过程,本文将地理加权回归法(GWR)引入栖息地指数(HSI)模型,选取海表温度、海表盐度、水深和叶绿素a浓度4个环境因子建立基于GWR的渤海沙氏下鱵鱼仔稚鱼的HSI_GWR模型.模拟发现:在2015年8月渤海的HSI_GWR模型中,海表温度和叶绿素a浓度为全局变量,两者的回归系数分别为-0.027和0.006,对HSI影响较小.海表盐度和水深为局地变量,两者回归系数绝对值的平均值分别为0.075和0.129,对HSI的影响较大.其中,海表盐度在渤海中部与HSI呈负相关,负相关系数最大,为-0.3,在三湾呈微弱正相关,相关系数最大值为0.1;水深在整个渤海均与HSI呈负相关,且在三湾的负相关程度明显大于渤海中部,三湾的负相关系数最大,为-0.16.该HSI_GWR模型的泊松相关系数为0.705,拟合效果较好,可为今后的鱼类栖息地环境研究提供一种新的方法.     

比较逻辑斯蒂与地理加权逻辑斯蒂回归模型在福建林火发生的适用性

林火预测预报是科学有效进行林火管理的前提,是林业管理部门和科研工作者的广泛关注的领域。逻辑斯蒂回归(Logistic Regression,LR)是目前国内外广泛应用于森林火灾预测的模型方法,然而近年来有学者发现该方法没有充分考虑林火影响因子的空间相关性和异质性,从而导致模型拟合结果偏差。地理加权逻辑斯蒂回归(Geographically weighted logistic regression,GWR)模型考虑到了模型变量之间的空间相关性,有效提高的模型的拟合能力。为探讨GWLR模型在福建林火预测上的适用性,本研究应用LR和GWLR两种方法分别建立福建省森林火灾与气象因子的预测模型,通过模型拟合能力对比,判断在GWLR的适用性。研究以2000—2005年福建地区森林火灾卫星火点数据和每日气象因子为基础,将全样本分为60%的建模数据和40%的校验数据,并重复5次,建立5个样本组。选择在5个样本组中3个及以上表现显著的变量进入最终模型。研究结果表明GWLR在模型拟合度、模型残差、空间自相关性以及预测准确率等方面均优于LR模型,说明充分考虑模型变量的空间异质性有助于提高模型的预测精度,同时也验证了GWLR在福建地区林火预测上的适应性。此外,模型参数结果显示,"日最高地表气温"、"日最低地表气温"、"日平均风速"、"24小时降水量"、"日最高本站气压"、"日照时数"、"日最高气温"和"日最小相对湿度"8个因子对福建省林火发生有显著影响,研究结论为福建地区林火预测预报提供了新的方法。     

Population modelling of the spatial interactions between Maculinea rebeli their initial foodplant Gentiana cruciata and Myrmica ants within a site

A spatial computer simulation model has been developed to assist our understanding of the ways in which Maculinea butterflies depend upon the spatial distribution and abundance of their initial foodplant and their Myrmica host ant. It was initially derived for the Maculinea rebeli-Myrmica schencki-Gentiana cruciata system. It relates the population processes of the competing host and other ant species to an underlying gradient of habitat quality and incorporates the impact of adopted Maculinea caterpillars on the growth and survival of individual ant nests. The model was initially calibrated for a large site in the Spanish Pyrenees, but has since been successfully tested on 12 French sites and another in Spain. On such sites, with M. rebeli present, there is a close relationship between Maculinea population density and the density of the early larval foodplant G. cruciata. Optimum gentian density is estimated to be about 1500 plants ha-1 on sites with the natural clumping of gentians found. However, any site management which added extra gentians, especially if filling the gaps, is predicted to reduce the Maculinea population. Meta-population studies of single species have shown that the size and spatial arrangement of patches of assumed uniformly suitable habitat can influence their population dynamics and persistence. Our modelling suggests that the spatial pattern of suitable habitat of varied quality within a single site can influence the local butterfly population size and perhaps also persistence. Despite being free-ranging over the whole area, the butterfly's dynamics may depend on the arrangement of habitat quality at a finer spatial scale, due to its interactions with ant species possessing narrower habitat niches and more localized dispersal. © Rapid Science Ltd. 1998     

A comparison of statistical approaches for modelling fish species distributions

SUMMARY 1. The prediction of species distributions is of primary importance in ecology and conservation biology. Statistical models play an important role in this regard; however, researchers have little guidance when choosing between competing methodologies because few comparative studies have been conducted. 2. We provide a comprehensive comparison of traditional and alternative techniques for predicting species distributions using logistic regression analysis, linear discriminant analysis, classification trees and artificial neural networks to model: (1) the presence/absence of 27 fish species as a function of habitat conditions in 286 temperate lakes located in south‐central Ontario, Canada and (2) simulated data sets exhibiting deterministic, linear and non‐linear species response curves. 3. Detailed evaluation of model predictive power showed that approaches produced species models that differed in overall correct classification, specificity (i.e. ability to correctly predict species absence) and sensitivity (i.e. ability to correctly predict speciespresence) and in terms of which of the study lakes they correctly classified. Onaverage, neural networks outperformed the other modelling approaches, although all approaches predicted species presence/absence with moderate to excellent success. 4. Based on simulated non‐linear data, classification trees and neural networks greatly outperformed traditional approaches, whereas all approaches exhibited similar correct classification rates when modelling simulated linear data. 5. Detailed evaluation of model explanatory insight showed that the relative importance of the habitat variables in the species models varied among the approaches, where habitat variable importance was similar among approaches for some species and very different for others. 6. In general, differences in predictive power (both correct classification rate and identity of the lakes correctly classified) among the approaches corresponded with differences in habitat variable importance, suggesting that non‐linear modelling approaches (i.e. classification trees and neural networks) are better able to capture and model complex, non‐linear patterns found in ecological data. The results from the comparisons using simulated data further support this notion. 7. By employing parallel modelling approaches with the same set of data and focusing on comparing multiple metrics of predictive performance, researchers can begin to choose predictive models that not only provide the greatest predictive power, but also best fit the proposed application.