县级尺度的重庆市碳排放时空格局动态

碳排放具有明显的时间和空间分布特征,研究区域碳排放时空格局动态特征可为制定合理的碳减排政策和措施提供重要的依据.本文以重庆为例,基于其38个区县的碳排放数据,利用空间统计、空间自相关和位序-规模法则探讨了其县级尺度碳排放的区域差异和空间格局演变特征.结果表明,重庆市各区县都经历了快速的碳排放增长过程,但碳排放的二元空间分布结构并没有改变;重庆市县级尺度碳排放全局Moran's I指数呈现出波浪式的降低趋势,主城区区县在中心相互辐射,形成一个碳排放HH中心;位序-规模法则分析结果则表明重庆市县级尺度碳排放基本属于首位型分布,1997~2012年区县碳排放规模分布趋于分散的力量均大于趋于集中的力量;1997和2012年,第二产业比例和城市化率成为影响重庆市碳排放的最重要的因素,人口与碳排放的相关关系却并不显著.     

Temporal Trends of Non-sea Salt Sulfate and Nitrate in Wet Deposition in Japan

Temporal trends of non-sea salt (nss-) sulfate and nitrate were analyzed from nationwide precipitation chemistry measurements provided by the Ministry of the Environment (MOE) for the 1988–2002 fiscal years (April–March). The concentrations and deposition of nss-sulfate were found to be decreasing, and those of nitrate were stable or slightly increasing at most sites. These deposition trends were discussed from the viewpoint of emissions of SO₂ and NO_X during the period of interest. Because monitoring techniques have changed in the number of active sites, samplers, and analytical methods during the operation period, the median of all annual depositions measured in Japan in a specific year was selected as the annual representative. The contribution of specific emission sources was also calculated for 1990 on the basis of the nss-sulfate and nitrate deposition in Japan obtained with a model simulation in which the model did not include volcanic emissions from Mt. Oyama, Miyakejima Island, which began to erupt suddenly and violently in 2000. For nss-sulfate, the calculated deposition agrees well with the intensity and trends of the median up to 1999. After 2000, a higher deposition than calculated in the preceding years was evident, which is attributable to the volcanic SO₂ from Mt. Oyama. For nitrate, both the calculated and observed depositions were slightly increasing; however, the calculation was found to exceed the observation.     

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.     

Incorporating spatial autocorrelation into cellular automata model: An application to the dynamics of Chinese tamarisk (Tamarix chinensis Lour.)

Spatial autocorrelation (SAC) is frequently encountered in most spatial data in ecology. Cellular automata (CA) models have been widely used to simulate complex spatial phenomena. However, little has been done to examine the impact of incorporating SAC into CA models. Using image-derived maps of Chinese tamarisk (Tamarix chinensis Lour.), CA models based on ordinary logistic regression (OLCA model) and autologistic regression (ALCA model) were developed to simulate landscape dynamics of T. chinensis. In this study, significant positive SAC was detected in residuals of ordinary logistic models, whereas non-significant SAC was found in autologistic models. All autologistic models obtained lower Akaike's information criterion corrected for small sample size (AICc) values than the best ordinary logistic models. Although the performance of ALCA models only satisfied the minimum requirement, ALCA models showed considerable improvement upon OLCA models. Our results suggested that the incorporation of the autocovariate term not only accounted for SAC in model residuals but also provided more accurate estimates of regression coefficients. The study also found that the neglect of SAC might affect the statistical inference on underlying mechanisms driving landscape changes and obtain false ecological conclusions and management recommendations. The ALCA model is statistically sound when coping with spatially structured data, and the adoption of the ALCA model in future landscape transition simulations may provide more precise probability maps on landscape transition, better model performance and more reasonable mechanisms that are responsible for landscape changes.     

我国环境污染与经济发展空间格局分析

以2006年我国31个省(市、自治区)的环境和经济统计数据为基础,利用三次曲线拟合了各地区人均污染物指标与人均GDP的相关性,同时利用系统聚类分析方法,将不同省(市、自治区)按人均污染物指标和人均GDP的相关性分为5类,并通过计算各类别相关指标的空间自相关系数,揭示了不同类别中各省(市、自治区)环境污染和经济发展的空间相关性.结果表明:在空间尺度上,我国没有出现环境库兹涅茨曲线特征,经济越发达地区的环境污染越严重;在人均污染物指标和人均GDP的空间关系上,东南沿海经济较发达地区为正相关,广大中西部地区为随机分布,少数经济落后地区为负相关.      

The effect of species response form on species distribution model prediction and inference

Ecological theory and current evidence support the validity of various species response curves according to a variety of environmental gradients. Various methods have been developed for building species distribution models but it is not well known how these methods perform under various assumptions about the form of the underlying species response. It is also not well known how spatial correlation in species occurrence affects model performance. These effects were investigated by applying an environmental envelope method (BIOCLIM) and three regression-based methods: logistic regression (LR), generalized additive modelling (GAM), and classification and regression tree (CART) to simulated species occurrence data. Each simulated species was constructed as a sum of responses with varying weights. Three basic species response curves were assumed: Gaussian (bell-shaped), Beta (skew) and linear. The two non-linear responses conform to standard ecological niche theory. All three responses were applied in turn to three simulated environmental variables, each with varying degrees of spatial autocorrelation. GAM produced the most consistent model performance over all forms of simulated species response. BIOCLIM and CART were inclined to underrate the performance of variables with a linear response. BIOCLIM was less sensitive to data density. LR was susceptible to model misspecification. The use of a linear function in LR underestimated the performance of variables with non-linear species response and contributed to increased spatial autocorrelation in model residuals. Omission of important environmental variables with non-linear species response also contributed to increased spatial autocorrelation in model residuals. Adding a spatial autocovariate term to the LR model (autologistic model) reduced the spatial autocorrelation and improved model performance, but did not correct the misidentification of the dominant environmental determinant. This is to be expected since the autologistic approach was designed primarily for prediction and not for inference. Given that various forms of species response to environmental determinants arise commonly in nature: (1) higher order functions should always be tested when applying LR in modelling species distribution; (2) spatial autocorrelation in species distribution model residuals can indicate that environmental determinants with non-linear response are missing from the model; and (3) deficiencies in LR model performance due to model misspecification can be addressed by adding a spatial autocovariate to the model, but care should be taken when interpreting the coefficients of the model parameters.     

松花湖水质空间差异及富营养化空间自相关分析

为识别中国东北地区最大的人工湖——松花湖水质空间差异与富营养化的空间分布特征,运用方差分析与因子分析对丰满水库主库区、松花江三湖保护区实验区和蛟河回水区这3个区域2017年的水质采样检测结果进行了分析,运用综合营养状态指数法评价了湖区的营养状况,采用空间自相关分析对松花湖湖区富营养化水平的空间相关性与聚集度状况进行了研究.主要结果如下：①方差分析结果表明,除溶解氧（DO）与叶绿素a（Chla）外,总氮（TN）、总磷（TP）、水温（WT）和pH值在松花湖3个区域内存在显著性差异（P<0.05）,其中TN与TP区域差异最为明显;②因子分析结果表明,气候条件因子（WT）、营养盐因子（TN、TP）与藻类生物量因子（Chla）变化主导着湖区水质的变化;③综合营养状态指数法结果表明,松花湖湖区营养状态整体为轻度富营养化,3个区域的富营养化水平由强到弱依次为：蛟河回水区 > 丰满水库主库区 > 松花江三湖保护区实验区;④全局空间自相关的结果表明,湖区富营养化水平整体呈现显著的正空间自相关关系,由于区域富营养化水平影响范围较大,松花湖富营养化水平的空间异质性不强;⑤局部空间自相关的结果表明,蛟河回水区的中部及北部地区是湖区富营养化水平的热点（高/高集聚）区域（P<0.01）,松花江三湖保护区实验区的中部是湖区富营养化水平的冷点（低/低集聚）（P<0.05）.因此进行松花湖水环境治理时,应将富营养化防治的重点区域落在蛟河回水区与丰满水库主库区.     

Simulating the temporal pattern of waste production in farmed gilthead seabream (Sparus aurata), European seabass (Dicentrarchus labrax) and Atlantic bluefin tuna (Thunnus thynnus)

Most fish farming waste output models provide gross waste rates as a function of stocked or produced biomass for a year or total culture cycle, but without contemplating the temporality of the discharges. This work aims to ascertain the temporal pattern of waste loads by coupling available growth and waste production models and developing simulation under real production rearing conditions, considering the overlapping of batches and management of stocks for three widely cultured species in the Mediterranean Sea: gilthead seabream (Sparus aurata), European seabass (Dicentrarchus labrax) and Atlantic bluefin tuna (Thunnus thynnus). For a similar annual biomass production, the simulations showed that waste output and temporal dumping patterns differ between the three species as a result of the disparities in growth velocity, nutrient digestibility, maintenance metabolic budget and husbandry. The simulations allowed the temporal patterns including the periods of maximum discharge and the dissolved and particulate nitrogen and phosphorus content in the wastes released to be determined, both of which were seen to be species-specific.     

Ensuring the environmental effectiveness of linked emissions trading schemes over time

Linking emissions trading schemes allows the combined emissions cap to be achieved at lower cost. Linking is usually environmentally neutral, but some design features can lead to higher aggregate emissions if schemes are linked. Technical solutions to limit the potential emissions increases due to design differences implemented when schemes are linked are not sufficient to ensure the environmental effectiveness of the linked schemes over time. Technological, economic, administrative and other changes that can lead to higher aggregate emissions are inevitable. The administrators of the linked schemes must ensure the stringency of the emissions cap relative to the “business as usual” emissions of affected sources, the accuracy of the emissions reported by affected sources, the integrity of the allowance registry, effective compliance enforcement, and the environmental integrity of the credits issued for emission reduction projects over time. This will require a process for agreeing on revisions to the regulations of the linked schemes, a mechanism to provide assurance of the environmental effectiveness of each of the linked schemes, and a procedure for terminating the linking agreement.

Reconstructing historical trends of Berre lagoon contamination from surface sediment datasets: influences of industrial regulations and anthropogenic silt inputs

These last decades, the Berre lagoon (in southeastern France) has been deeply affected since the 1930s by strong inputs of contaminants associated with industrial development and since 1966 by huge inputs of freshwater and silts due to the installation of a hydroelectric power plant. Surveys of the surface sediment contamination have been sparsely performed since 1964 for management and research purposes. These surveys were performed by various laboratories that investigated different chemicals and sampling areas using different analysis protocols. Therefore, the available data are disconnected in time and space and differ in quality. In order to reconstruct coherent time series of sediment contamination from this heterogeneous datasets and to discuss the influences of industrial and hydroelectric discharges we used a statistical approach. This approach is based on Principal Component Analysis (PCA) and Fuzzy clustering analysis on data from one extensive survey realized on surface sediments in 1976. The PCA allowed identifying two geochemical indexes describing the main surface sediment geochemical characteristics. The fuzzy clustering analysis on these indexes allowed identifying sub-areas under the specific influence of industrial or hydroelectric discharges. This allowed us to reconstruct, for each sub-area, a coherent and interpretable long-term time series of sediment contamination from the available database. Reconstructed temporal trends allowed us to estimate: (i) the overall decrease of sediment contamination since the mid-1970 attributed to industrial discharge regulations enacted at this period and (ii) the dilution of the concentrations of sediment bound contaminants induced by the hydroelectric power plant and its associated particulate matter inputs.