Spatial autocorrelation and the selection of simultaneous autoregressive models

Aim Spatial autocorrelation is a frequent phenomenon in ecological data and can affect estimates of model coefficients and inference from statistical models. Here, we test the performance of three different simultaneous autoregressive (SAR) model types (spatial error = SAR_err, lagged = SAR_lag and mixed = SAR_mix) and common ordinary least squares (OLS) regression when accounting for spatial autocorrelation in species distribution data using four artificial data sets with known (but different) spatial autocorrelation structures. Methods We evaluate the performance of SAR models by examining spatial patterns in model residuals (with correlograms and residual maps), by comparing model parameter estimates with true values, and by assessing their type I error control with calibration curves. We calculate a total of 3240 SAR models and illustrate how the best models [in terms of minimum residual spatial autocorrelation (minRSA), maximum model fit (R²), or Akaike information criterion (AIC)] can be identified using model selection procedures. Results Our study shows that the performance of SAR models depends on model specification (i.e. model type, neighbourhood distance, coding styles of spatial weights matrices) and on the kind of spatial autocorrelation present. SAR model parameter estimates might not be more precise than those from OLS regressions in all cases. SAR_err models were the most reliable SAR models and performed well in all cases (independent of the kind of spatial autocorrelation induced and whether models were selected by minRSA, R² or AIC), whereas OLS, SAR_lag and SAR_mix models showed weak type I error control and/or unpredictable biases in parameter estimates. Main conclusions SAR_err models are recommended for use when dealing with spatially autocorrelated species distribution data. SAR_lag and SAR_mix might not always give better estimates of model coefficients than OLS, and can thus generate bias. Other spatial modelling techniques should be assessed comprehensively to test their predictive performance and accuracy for biogeographical and macroecological research.     

基于不同空间模型的马尾松林生态系统碳密度研究

以江西省马尾松林生态系统为研究对象,基于样地调查及样品碳含量测定结果计算其碳密度,并选取立地、植被及气象等方面的15个因子,采用多元线性逐步回归方法筛选出对生态系统碳密度影响显著的因子,然后分别利用最小二乘模型(OLS)、空间误差模型(SEM)、空间滞后模型(SLM)和地理加权回归模型(GWR)构建生态系统碳密度与其影响因子之间的关系模型,筛选出最优的拟合模型。结果表明:对马尾松林生态系统碳密度影响显著的因子分别为海拔、坡度、土层厚度、胸径、年均温度和年均降水量。4种模型拟合结果均显示碳密度与坡度呈负相关,与海拔、土层厚度、胸径呈正相关。模型的决定系数(R~2)由大到小分别为GWR(0.8043)SEM(0.6371)SLM(0.6364)OLS(0.6321),模型均方误差(MSE)与赤池信息准则(AIC)最大的均为OLS模型,最小的均为GWR模型;残差检验表明GWR模型能有效降低模型残差的空间自相关性。综合分析得出GWR模型的拟合效果最优,更适用于江西省马尾松林生态系统碳密度的估测。     

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、R~2和校正R~2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。     

基于GWR模型的东江水质空间分异与水生态功能分区验证

流域水生态功能分区研究是我国正在开展的一项重要工作,如何验证分区结果的合理性,是当前亟待解决的问题。采用地理加权回归(GWR)模型评估流域特征对东江水质的影响,验证水质及流域影响空间差异是否与一二级水生态功能分区结果吻合,并对比了GWR模型与普通最小二乘(OLS)模型性能,讨论了GWR在分区验证方面的应用价值及不足。结果显示:1)水质指标以及GWR模型局部解释率(Local R~2)均在一二级水生态功能分区间存在显著差异;2)相比OLS模型,GWR模型校正R~2更高,残差空间自相关指数Moran′s I更低。研究表明东江水生态功能分区结果能合理反映水陆耦合关系,有效解释水质空间差异。此外建议选择总氮(TN)而非溶解氧(DO)和总磷(TP)作为分区验证指标。GWR模型在分区结果验证中具有广泛应用前景。降低数据空间自相关影响及改善距离测度方法是未来GWR模型研究的难点问题。     

Spatial autocorrelation and red herrings in geographical ecology

Aim Spatial autocorrelation in ecological data can inflate Type I errors in statistical analyses. There has also been a recent claim that spatial autocorrelation generates ‘red herrings’, such that virtually all past analyses are flawed. We consider the origins of this phenomenon, the implications of spatial autocorrelation for macro‐scale patterns of species diversity and set out a clarification of the statistical problems generated by its presence. Location To illustrate the issues involved, we analyse the species richness of the birds of western/central Europe, north Africa and the Middle East. Methods Spatial correlograms for richness and five environmental variables were generated using Moran's I coefficients. Multiple regression, using both ordinary least‐squares (OLS) and generalized least squares (GLS) assuming a spatial structure in the residuals, were used to identify the strongest predictors of richness. Autocorrelation analyses of the residuals obtained after stepwise OLS regression were undertaken, and the ranks of variables in the full OLS and GLS models were compared. Results Bird richness is characterized by a quadratic north–south gradient. Spatial correlograms usually had positive autocorrelation up to c. 1600 km. Including the environmental variables successively in the OLS model reduced spatial autocorrelation in the residuals to non‐detectable levels, indicating that the variables explained all spatial structure in the data. In principle, if residuals are not autocorrelated then OLS is a special case of GLS. However, our comparison between OLS and GLS models including all environmental variables revealed that GLS de‐emphasized predictors with strong autocorrelation and long‐distance clinal structures, giving more importance to variables acting at smaller geographical scales. Conclusion Although spatial autocorrelation should always be investigated, it does not necessarily generate bias. Rather, it can be a useful tool to investigate mechanisms operating on richness at different spatial scales. Claims that analyses that do not take into account spatial autocorrelation are flawed are without foundation.     

基于带宽优选地理加权回归模型的深圳市植被碳储量反演

植被碳储量估测是自然资源监测的重要内容,遥感技术结合地面样地进行反演可以获得区域范围内植被碳储量的空间连续分布,弥补了传统人工抽样调查估测的不足。然而,现有的参数和非参数遥感估测模型大多忽略了样地数据的变异与空间自相关关系。研究以Landsat 8 OLI影像为数据源提取遥感变量,结合植被碳储量实测调查数据,利用最小信息准则(AICc)、最大空间自相关距离(MSAD)和交叉验证(CV)分别确定最优带宽,组合Gaussian、Bi-square和Exponential核函数构建地理加权回归(GWR)模型估算深圳市植被碳储量,并与多元线性回归(MLR)进行比较,选择最优模型绘制深圳市植被碳储量空间分布图。研究结果表明,GWR模型整体精度优于MLR模型,GWR模型的决定系数(R~2)均高于MLR模型,且均方根误差(RMSE)和平均绝对误差(MAE)显著降低。带宽和核函数的选择对GWR模型估测结果产生了显著影响。以CV确定带宽、Exponential为核函数组合构建的GWR模型效果最佳,其R~2为0.697,RMSE为10.437 Mg C/hm~2,相比其它模型精度上升了13.87%—32....     

福州市土壤铬含量高光谱预测的GWR模型研究

通过系统分析不同光谱分辨率和光谱变换对土壤铬高光谱预测模型的不确定性影响,筛选出最优的光谱分辨率及光谱变量进行土壤铬含量预测的地理权重回归(GWR)模型构建,利用该模型进行福州市土壤铬含量预测,并将预测结果与普通最小二乘法回归(OLS)结果进行比较分析,探讨GWR模型在土壤铬高光谱预测中的适用性及局限性。结果表明:(1)在10 nm分辨率尺度下,以土壤全铬含量为因变量,反射率的二阶微分和反射率倒数的二阶微分为自变量构建的GWR模型对土壤铬预测的效果最好。GWR模型的R~2和调节R~2分别为0.821和0.716,较OLS模型分别提高了0.529和0.450,而AIC值为720.703,较OLS模型减少了22个单位,残差平方和仅为OLS模型的1/4,说明GWR模型的预测效果较OLS模型有了显著提高。(2)土壤铬预测模型的精度受光谱分辨率影响。对于OLS预测模型来说,3 nm分辨率的模型预测效果最好,而对于GWR预测模型来说,10nm分辨率的模型不仅预测效果最好,其相较于OLS模型的改善作用显著,为土壤铬含量GWR预测的最佳光谱分辨率。(3)光谱的一阶微分变换可以有效增强土壤铬的光谱特征,而其余的光谱变换对土壤铬的光谱特征则未起到增强作用,但可以很好地提高模型的预测效果。(4)研究得出土壤铬GWR模型预测的最佳光谱分辨率为10 nm,为EO-1 Hyperion影像的光谱分辨率,而且随着采样点的增加,GWR模型的预测效果趋于稳定,适合空间异质性大的区域尺度土壤铬预测。故该模型与高光谱影像结合,实现模型从实验室尺度向区域尺度的推广,为格网尺度土壤铬的空间预测提供可能。     

Spatial patterns of species richness in New World coral snakes and the metabolic theory of ecology

The metabolic theory of ecology (MTE) has attracted great interest because it proposes an explanation for species diversity gradients based on temperature-metabolism relationships of organisms. Here we analyse the spatial richness pattern of 73 coral snake species from the New World in the context of MTE. We first analysed the association between ln-transformed richness and environmental variables, including the inverse transformation of annual temperature (1/kT). We used eigenvector-based spatial filtering to remove the residual spatial autocorrelation in the data and geographically weighted regression to account for non-stationarity in data. In a model I regression (OLS), the observed slope between ln-richness and 1/kT was ?0.626 (r² = 0.413), but a model II regression generated a much steeper slope (?0.975). When we added additional environmental correlates and the spatial filters in the OLS model, the R² increased to 0.863 and the partial regression coefficient of 1/kT was ?0.676. The GWR detected highly significant non-stationarity, in data, and the median of local slopes of ln-richness against 1/kT was ?0.38. Our results expose several problems regarding the assumptions needed to test MTE: although the slope of OLS fell within that predicted by the theory and the dataset complied with the assumption of temperature-independence of average body size, the fact that coral snakes consist of a restricted taxonomic group and the non-stationarity of slopes across geographical space makes MTE invalid to explain richness in this case. Also, it is clear that other ecological and historical factors are important drivers of species richness patterns and must be taken into account both in theoretical modeling and data analysis.     

Geographic Disparity in Chronic Obstructive Pulmonary Disease (COPD) Mortality Rates among the Taiwan Population

Chronic obstructive pulmonary disease (COPD) causes a high disease burden among the elderly worldwide. In Taiwan, the long-term temporal trend of COPD mortality is declining, but the geographical disparity of the disease is not yet known. Nationwide COPD age-adjusted mortality at the township level during 1999–2007 is used for elucidating the geographical distribution of the disease. With an ordinary least squares (OLS) model and geographically weighted regression (GWR), the ecologic risk factors such as smoking rate, area deprivation index, tuberculosis exposure, percentage of aborigines, density of health care facilities, air pollution and altitude are all considered in both models to evaluate their effects on mortality. Global and local Moran’s I are used for examining their spatial autocorrelation and identifying clusters. During the study period, the COPD age-adjusted mortality rates in males declined from 26.83 to 19.67 per 100,000 population, and those in females declined from 8.98 to 5.70 per 100,000 population. Overall, males’ COPD mortality rate was around three times higher than females’. In the results of GWR, the median coefficients of smoking rate, the percentage of aborigines, PM₁₀ and the altitude are positively correlated with COPD mortality in males and females. The median value of density of health care facilities is negatively correlated with COPD mortality. The overall adjusted R-squares are about 20% higher in the GWR model than in the OLS model. The local Moran’s I of the GWR’s residuals reflected the consistent high-high cluster in southern Taiwan. The findings indicate that geographical disparities in COPD mortality exist. Future epidemiological investigation is required to understand the specific risk factors within the clustering areas.     

Interannual NPP variation and trend of Picea schrenkiana forests under changing climate conditions in the Tianshan Mountains, Xinjiang, China

The interannual net primary production variation and trends of a Picea schrenkiana forest were investigated in the context of historical changes in climate and increased atmospheric CO₂ concentration at four sites in the Tianshan Mountain range, China. Historical changes in climate and atmospheric CO₂ concentration were used as Biome–BGC model drivers to evaluate the spatial patterns and temporal trends of net primary production (NPP). The temporal dynamics of NPP of P. schrenkiana forests were different in the western, middle and eastern sites of Tianshan, which showed substantial interannual variation. Climate changes would result in increased NPP at all study sites, but only the change in NPP in the western forest (3.186 gC m⁻² year⁻¹, P < 0.05) was statistically significant. Our study also showed a higher increase in the air temperature, precipitation and NPP during 1987–2000 than 1961–1986. Statistical analysis indicates that changes in NPP are positively correlated with annual precipitation (R = 0.77–0.92) but that NPP was less sensitive to changes in air temperature. According to the simulation, increases in atmospheric CO₂ increased NPP by improving the water use efficiency. The results of this study show that the Tianshan Mount boreal forest ecosystem is sensitive to historical changes in climate and increasing atmospheric CO₂. The relative impacts of these variations on NPP interact in complex ways and are spatially variable, depending on local conditions and climate gradients. W. Sang and H. Su contributed equally to this paper, arranged in alphabetical order by surnames.     

Red herrings revisited: spatial autocorrelation and parameter estimation in geographical ecology

There have been numerous claims in the ecological literature that spatial autocorrelation in the residuals of ordinary least squares (OLS) regression models results in shifts in the partial coefficients, which bias the interpretation of factors influencing geographical patterns. We evaluate the validity of these claims using gridded species richness data for the birds of North America, South America, Europe, Africa, the ex‐USSR, and Australia. We used richness in 110×110 km cells and environmental predictor variables to generate OLS and simultaneous autoregressive (SAR) multiple regression models for each region. Spatial correlograms of the residuals from each OLS model were then used to identify the minimum distance between cells necessary to avoid short‐distance residual spatial autocorrelation in each data set. This distance was used to subsample cells to generate spatially independent data. The partial OLS coefficients estimated with the full dataset were then compared to the distributions of coefficients created with the subsamples. We found that OLS coefficients generated from data containing residual spatial autocorrelation were statistically indistinguishable from coefficients generated from the same data sets in which short‐distance spatial autocorrelation was not present in all 22 coefficients tested. Consistent with the statistical literature on this subject, we conclude that coefficients estimated from OLS regression are not seriously affected by the presence of spatial autocorrelation in gridded geographical data. Further, shifts in coefficients that occurred when using SAR tended to be correlated with levels of uncertainty in the OLS coefficients. Thus, shifts in the relative importance of the predictors between OLS and SAR models are expected when small‐scale patterns for these predictors create weaker and more unstable broad‐scale coefficients. Our results indicate both that OLS regression is unbiased and that differences between spatial and nonspatial regression models should be interpreted with an explicit awareness of spatial scale.     

Information-Theoretic Model Selection and Model Averaging for Closed-Population Capture-Recapture Studies

Specification of an appropriate model is critical to valid statistical inference. Given the “true model” for the data is unknown, the goal of model selection is to select a plausible approximating model that balances model bias and sampling variance. Model selection based on information criteria such as AIC or its variant AIC_c, or criteria like CAIC, has proven useful in a variety of contexts including the analysis of open-population capture-recapture data. These criteria have not been intensively evaluated for closed-population capture-recapture models, which are integer parameter models used to estimate population size (N), and there is concern that they will not perform well. To address this concern, we evaluated AIC, AIC_c, and CAIC model selection for closed-population capture-recapture models by empirically assessing the quality of inference for the population size parameter N. We found that AIC-, AIC_c-, and CAIC-selected models had smaller relative mean squared errors than randomly selected models, but that confidence interval coverage on N was poor unless unconditional variance estimates (which incorporate model uncertainty) were used to compute confidence intervals. Overall, AIC and AIC_c outperformed CAIC, and are preferred to CAIC for selection among the closed-population capture-recapture models we investigated. A model averaging approach to estimation, using AIC, AIC_c, or CAIC to estimate weights, was also investigated and proved superior to estimation using AIC-, AIC_c-, or CAIC-selected models. Our results suggested that, for model averaging, AIC or AIC_c should be favored over CAIC for estimating weights.     

基于地理加权回归克里格模型的帽儿山地区森林碳储量空间分布

森林碳储量对于全球气候变化具有重要影响,以往的模型估算未考虑到模型残差的空间相关性和碳储量数据的非平稳性,影响模型的预测精度.本研究基于东北林业大学帽儿山实验林场的ETM+遥感影像数据和193块固定样地,利用地理加权克里格回归(GWRK)建立森林碳储量与遥感和地形因子的回归模型,同时对比最小二乘模型(OLS)、地理加权回归模型(GWR)的预测精度.结果表明: 对于帽儿山地区的森林碳储量估算,GWRK的平均绝对误差(MAE)、均方根误差(RMSE)低于OLS模型和GWR模型,GWRK模型的平均误差(ME)低于GWR模型,与OLS模型相近.GWRK模型的预测精度为83.2%,较OLS模型(73.7%)和GWR模型(77.3%)分别提高6%和10%,拟合精度明显提高,说明GWRK模型是森林碳储量估算的有效方法.利用GWRK模型预测的研究区森林碳储量平均值为70.31 t·hm^-2,在海拔较高的地区,森林碳储量值相对较高,说明海拔对其有较大影响.     

基于集成生物圈模型的南岭不同植被类型碳收支研究

广东南岭保存着世界上同纬度带上最完整的亚热带植被,森林资源丰富,具有巨大的固碳潜力。然而,目前该地区不同森林植被类型的碳收支年积累量特征及月动态规律尚不明确。选择广东南岭国家级自然保护区内沟谷常绿阔叶林、山地常绿阔叶林、针阔叶混交林和山顶常绿阔叶矮林4种典型森林植被为研究对象,运用集成生物圈模型(IBIS)对其2020年总初级生产力(GPP)、净初级生产力(NPP)、净生态系统生产力(NEP)和土壤异养呼吸(R_h)进行模拟,利用样地调查数据对NPP模拟结果进行验证,分析该地区不同植被类型的碳收支年积累量特征及月变化特征。研究结果表明,2020年南岭不同植被类型GPP、NPP、NEP和R_h的平均值分别为1.709、0.718、0.596和0.123 kg C m^-2 a^-1,4种植被类型中GPP最高的是沟谷常绿阔叶林,NPP、NEP最高的是山地常绿阔叶林,山顶常绿阔叶矮林的GPP、NPP和NEP均相对较低。南岭不同植被类型全年各月均表现出碳汇(NEP>0),逐月NPP和NEP均表现为双峰变化规律...     

Net primary production and light use efficiency in a mixed coniferous forest in Sweden

Simple light use efficiency (ɛ) models of net primary production (NPP) have recently been given great attention (NPP = ɛ × absorbed photosynthetically active radiation). The underlying relationships have, however, not been much studied on a time step less than a month. In this study daily NPP was estimated as the sum of net ecosystem exchange (NEE) and heterotrophic respiration (R_h) of a mixed pine and spruce forest in Sweden. NEE was measured by eddy correlation technique and R_h was estimated from measurements of forest floor respiration (R_f) and the root share of R_f. The total yearly NPP was on average 810 g C m⁻² year⁻¹ for 3 years and yearly ɛ was between 0.58 and 0.71 g C MJ⁻¹, which is high in comparison with other studies. There was a seasonal trend in ɛ with a relatively constant level of approximately 0.90 g C MJ⁻¹ from April to September Daily NPP did not increase for daily intercepted radiation above 6 MJ m⁻² d⁻¹, indicating that between-years variation in NPP is not directly dependent on total Q_i. The light was most efficiently used at an average daytime temperature of around 15 °C. At daytime vapour pressure deficit above 1400 Pa ɛ was reduced by approximately 50%.     

宁夏中宁枸杞林地粉壤土阳离子交换量空间分布预测方法对比

土壤阳离子交换量(CEC)是土壤施肥、改良的主要依据和土壤质量的评价指标,研究土壤CEC的空间分布及模型预测可为土壤养分监测、管理及精准农业实施提供科学依据。本研究以中宁枸杞林地粉壤土为对象,在自相关、交互相关等分析基础上,采用协同克里格(CoKriging)、普通最小二乘法(OLS)、地理加权回归(GWR)和随机森林(RF)模型对土壤CEC进行回归分析,比较了制图效果及模型预测精度。结果表明:中宁枸杞林地粉壤土CEC平均值为13.12 cmol·kg^-1,属中等肥力;土壤CEC的空间分布具有自相关性,并与土壤pH、有机质、黏粒和电导率在不同滞后距离上存在不同的空间相互关系;RF模型预测图避免了CoKriging、OLS和GWR模型预测图中土壤CEC图斑边界两侧破碎程度大、突变明显的缺陷,使土壤CEC在空间变化上表现为自然、平缓的过渡;RF模型RMSE值分别比CoKriging、OLS和GWR模型减少33.82%、20.55%和19.81%,R^2分别提高8.84%、51.92%和7.69%。RF模型考虑了样点空间位置,明显提高了插值精度且制图效果更加平缓。     

Exploring the Spatial Association between Social Deprivation and Cardiovascular Disease Mortality at the Neighborhood Level

Cardiovascular disease (CVD), the leading cause of death in the United States, is impacted by neighborhood-level factors including social deprivation. To measure the association between social deprivation and CVD mortality in Harris County, Texas, global (Ordinary Least Squares (OLS) and local (Geographically Weighted Regression (GWR)) models were built. The models explored the spatial variation in the relationship at a census-tract level while controlling for age, income by race, and education. A significant and spatially varying association (p < .01) was found between social deprivation and CVD mortality, when controlling for all other factors in the model. The GWR model provided a better model fit over the analogous OLS model (R² = .65 vs. .57), reinforcing the importance of geography and neighborhood of residence in the relationship between social deprivation and CVD mortality. Findings from the GWR model can be used to identify neighborhoods at greatest risk for poor health outcomes and to inform the placement of community-based interventions.     

Analysis of determinants of mammalian species richness in South America using spatial autoregressive models

Classically, hypotheses concerning the distribution of species have been explored by evaluating the relationship between species richness and environmental variables using ordinary least squares (OLS) regression. However, environmental and ecological data generally show spatial autocorrelation, thus violating the assumption of independently distributed errors. When spatial autocorrelation exists, an alternative is to use autoregressive models that assume spatially autocorrelated errors. We examined the relationship between mammalian species richness in South America and environmental variables, thereby evaluating the relative importance of four competing hypotheses to explain mammalian species richness. Additionally, we compared the results of ordinary least squares (OLS) regression and spatial autoregressive models using Conditional and Simultaneous Autoregressive (CAR and SAR, respectively) models. Variables associated with productivity were the most important at determining mammalian species richness at the scale analyzed. Whereas OLS residuals between species richness and environmental variables were strongly autocorrelated, those from autoregressive models showed less spatial autocorrelation, particularly the SAR model, indicating its suitability for these data. Autoregressive models also fit the data better than the OLS model (increasing R² by 5–14%), and the relative importance of the explanatory variables shifted under CAR and SAR models. These analyses underscore the importance of controlling for spatial autocorrelation in biogeographical studies.     

2000-2019年中国南方竹林区水分利用效率时空特征及驱动机制

水分利用效率(Water Use Efficiency, WUE)是深入理解生态系统碳、水循环及两者耦合关系的重要指标,然而我国重要森林类型之一的竹林的WUE时空格局及其驱动机制研究不足。通过MODIS净初级生产力(NPP)和蒸散(ET)数据得到竹林区WUE,采用线性趋势法计算WUE年际变化率表征变化趋势,并应用地理加权回归(GWR)模型分析了WUE与气候和地形等10个驱动因子的关系,探究了中国南方竹林区近20年间(2000—2019)WUE驱动机制。结果表明：(1)2000—2019年中国南方竹林区WUE多年均值为0.89 gC m^-2 mm^-1,呈显著下降趋势,下降速率为0.0028 gC m^-2 mm^-1 a^-1,ET上升速度大于NPP上升速度是造成WUE下降的主要原因;WUE呈南高北低的空间分布格局,83.5%区域的WUE呈下降趋势。(2)基于GWR模型的WUE驱动力分析发现,WUE变化最强的驱动因子是CO₂浓度和年降水量,而海拔、坡度等地形因子的...     

中国西北干旱区植被碳汇估算及其时空格局

通过修正的CASA模型估算2001—2012年间西北干旱区陆地生态系统的净第一性生产力(NPP),并结合土壤微生物呼吸方程,计算出12a的净生态系统生产力(NEP),分析了植被碳汇的时空变化规律。结果表明:研究区的NPP表现出很强的随季节变化的规律,全年7月份NPP为最高值,12月为最低值,12年间NPP的年均值变化不大。2001—2012年研究区的植被碳汇在波动变化中有所增加,其中2006年的碳汇平均值最小,为609.04 g C m~(-2)a~(-1),2012年最大,为648.02 g C m~(-2)a~(-1);年内碳汇的最大值主要出现在5—7月;碳汇能力由大到小的植被类型为针叶林农田灌丛阔叶林草原荒漠草原。研究区多年平均碳汇量呈现自西向东逐渐增加的规律,西辽河流域草原区的NPP和碳汇平均值最大,塔里木盆地暖温带荒漠区最小。