Island urbanization and its ecological consequences: A case study in the Zhoushan Island,East China

Islands, which provide multiple ecosystem services, are subject to increasing urbanization pressure due to the ongoing marine development, especially in developing countries. Insights into the island urbanization mechanism and its ecological consequences are essential to sustainable development. In the present paper, the satellite images, nighttime lights, and topographic data were integrated to characterize the spatially explicit urbanization process and mechanism during 1995–2011 in the Zhoushan Island, East China. Furthermore, the corresponding spatially explicit changes in ecosystem services, including net primary productivity (NPP), carbon sequestration and oxygen production (CSOP), nutrient cycling, crop production, and habitat quality, were quantified based on the Carnegie–Ames–Stanford Approach (CASA) and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) models. The results showed that the Zhoushan Island had experienced a rapid urbanization over the years, with significant urban encroachment on the farmland and tidal flat. Moreover, the urban land expansion was positively correlated with that of the nighttime lights and negatively correlated with the elevation, slope, and the distance to shoreline. These indicated that the urban expansion was resulted from the enhancement of socioeconomic activities, and concentrated in the near-shore areas with low altitude and gentle slope. The urban encroachment on other land use types resulted in a decrease of 3.4 Gg C a⁻¹ NPP, 8.7 Gg a⁻¹ CSOP, 13.2 Gg a⁻¹ nutrient cycling, and 12.3 t a⁻¹ crop production, respectively. In addition, the habitat quality in 11% area of this island degraded substantially. Therefore, to achieve sustainable development of islands, it is urgent to implement more stringent policies, such as island spatial regulation, environmental impact assessment, intensive land use, and urban greening, etc.     

Distribution of plant functional types along gradients of disturbance intensity and resource supply in an agricultural landscape

Abstract. In this study, plant functional types are understood as groups of plants with similar biological traits displaying significant optima or maxima on a gradient plane of resource supply and disturbance intensity. The biological traits refer to expansion, vegetative regeneration, generative reproduction, dispersal and seed bank longevity. 129 vegetation samples were taken in an agricultural landscape in southwestern Germany, covering a wide range of terrestrial vegetation types – but with the exception of forests and wetlands. For each site, also soil data were recorded. Mean daily soil moisture was estimated with a simple model. Soil moisture, balanced nitrogen supply and available phosphorus were combined into a factor ‘resource supply’. In addition, disturbance intensity was estimated for each site. This factor was based on (1) frequency of disturbance, (2) disturbance depth below or above the soil surface, and (3) proportion of the area affected by a discrete disturbance event. 30 plant groups with similar biological characteristics resulted from a cluster analysis, based on a compilation of 19 biological traits for a regional species pool. Logistic regression on a gradient plane of disturbance intensity and resource supply yielded response curves for 28 groups. The dependent variable was defined as the probability of encountering all members of a group in a sample. 17 groups display a significant response curve on the gradient plane. Plants with a potential for long- range dispersal are concentrated on sites with low or high disturbance intensities (e.g. fallow land, fields, lawns). On sites with medium disturbance intensity (e.g. meadows) and low to medium resource supply, small-range dispersal predominates. There are no distinct trends concerning seed bank longevity. The potential for vertical and lateral expansion increases with decreasing disturbance intensity. Only at medium disturbance intensities does vertical expansion correlate positively with resource supply. Rapid detachment of daughter individuals occurs more often on productive sites than on less productive sites. Diversity of groups with similar biological traits is highest on sites with medium disturbance intensities.     

免疫细胞浸润在非小细胞肺癌诊断与预后中的应用

免疫细胞浸润对癌症的诊断与预后有着重要意义。文中收集TCGA数据库已收录的非小细胞肺癌肿瘤与正常组织基因表达数据,利用CIBERSORT工具得到22种免疫细胞占比来评估免疫细胞浸润情况。以22种免疫细胞占比为特征,用机器学习方法构建了非小细胞肺癌肿瘤与正常组织的分类模型,其中随机森林方法构建的模型分类效果AUC=0.987、敏感性0.98及特异性0.84。并且用随机森林方法构建的肺腺癌和肺鳞癌肿瘤组织分类模型效果AUC=0.827、敏感性0.75及特异性0.77。用LASSO回归筛选22种免疫细胞特征,保留8种强相关特征组成的免疫细胞评分结合临床特征构建了非小细胞肺癌预后模型。经评估及验证,预后模型C-index=0.71并且3年和5年的校准曲线拟合良好,可以对预后风险度进行准确预测。本研究基于免疫细胞浸润所构建的分类模型与预后模型,旨在对非小细胞肺癌的诊断与预后研究提供新的策略。     

Factors influencing the spatial distribution of forest plant species in hedgerows of North-western Germany

In North-western Germany woodland fragmentation has caused a decline in many forest plant species. Hedgerows partly offer a similar environment as forests and have been identified as potential habitats for forest plants in various studies from North America and Western Europe. The objective of this study was to examine whether this applies also to Central Europe and which variables affect the spatial distribution and abundance of forest plant species in hedgerows on a local scale. Three hedgerow networks north of the city of Bremen, Germany, were selected as study areas and divided into totally 515 hedgerow segments. In each segment we recorded all vascular plants and a large number of explanatory variables relating to structure, spatial configuration, environment and management. Averaged across species there was a predominant effect of environmental factors on the occurrence of forest species in the hedgerows, followed by spatial configuration and management. Hedgerow structure was found to be less important. In general, forest species were favored by low nutrient and light availability as well as high connectivity with other hedgerows or forest; they avoided hedgerows with a west-easterly orientation and an adjacent land use in the form of fields or grasslands. Forest species found and not found in hedgerows did not differ in their environmental preferences or life history traits. The number of threatened forest species in the hedgerows, however, was lower than expected with respect to their overall proportion to the total number of forest species in the region.     

Canopy closure estimation in a temperate forest using airborne LiDAR and LANDSAT ETM+ data

Aims　Forest canopy closure is one of the essential factors in forest survey, and plays an important role in forest ecosystem management. It is of great significance to study how to apply LiDAR (light detection and ranging) data efficiently in remote sensing estimation of forest canopy closure. LiDAR can be used to obtain data fast and accurately and therefore be used as training and validation data to estimate forest canopy closure in large spatial scale. It can compensate for the insufficiency (e.g. labor-intensive, time-consuming) of conventional ground survey, and provide foundations to forest inventory.Methods　In this study, we estimated canopy closure of a temperate forest in Genhe forest of Da Hinggan Ling area, Nei Mongol, China, using LiDAR and LANDSAT ETM+ data. Firstly, we calculated the canopy closure from ALS (Airborne Laser Scanning) high density point cloud data. Then, the estimated canopy closure from ALS data was used as training and validation data to modeling and inversion from eight vegetation indices computed from LANDSAT ETM+ data. Three approaches, multi-variable stepwise regression (MSR), random forest (RF) and Cubist, were developed and tested to estimate canopy closure from these vegetation indices, respectively.Important findings The validation results showed that the Cubist model yielded the highest accuracy compared to the other two models (determination coefficient (R²) = 0.722, root mean square error (RMSE) = 0.126, relative root mean square error (rRMSE) = 0.209, estimation accuracy (EA) = 79.883%). The combination of LiDAR data and LANDSAT ETM+ showed great potential to accurately estimate the canopy closure of the temperate forest. However, the model prediction capability needs to be further improved in order to be applied in larger spatial scale. More independent variables from other remotely sensed datasets, e.g. topographic data, texture information from high-resolution imagery, should be added into the model. These variables can help to reduce the influence of optical image, vegetation indices, terrain and shadow and so on. Moreover, the accuracy of the LiDAR-derived canopy closure needs to be further validated in future studies.     

Non‐parametric habitat models with automatic interactions

Questions: Can a statistical model be designed to represent more directly the nature of organismal response to multiple interacting factors? Can multiplicative kernel smoothers be used for this purpose? What advantages does this approach have over more traditional habitat modelling methods? Methods: Non‐parametric multiplicative regression (NPMR) was developed from the premises that: the response variable has a minimum of zero and a physiologically‐determined maximum, species respond simultaneously to multiple ecological factors, the response to any one factor is conditioned by the values of other factors, and that if any of the factors is intolerable then the response is zero. Key features of NPMR are interactive effects of predictors, no need to specify an overall model form in advance, and built‐in controls on overfitting. The effectiveness of the method is demonstrated with simulated and real data sets. Results: Empirical and theoretical relationships of species response to multiple interacting predictors can be represented effectively by multiplicative kernel smoothers. NPMR allows us to abandon simplistic assumptions about overall model form, while embracing the ecological truism that habitat factors interact.