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1.
Partitioning beta diversity into its two components of spatial turnover and nestedness is a more robust method for checking spatial variability in biological communities than calculating the total beta diversity alone. The relative contribution of spatial turnover and nestedness has been used to test the effects of climatic, environmental, spatial and temporal variables on community composition. In this study, we tested the effects of environmental factors and microhabitat features on total beta diversity and its spatial turnover and nestedness components using a comprehensive dataset of aquatic Heteroptera collected from four types of permanent freshwater habitats (i.e. streams, ponds, rock tanks and reservoirs) in the Western Ghats of India. We observed that communities in all four types of habitats were predominantly shaped by dissimilarity caused due to spatial turnover (>85 %). Each type of habitat showed the presence of one or more species uniquely associated with it, which might contribute to the turnover between communities. The abiotic environment (climatic factors, topological factors, soil characteristics and microhabitat features) as well as assemblage structure differed significantly between habitat types. Communities in each type of habitat were affected by different environmental factors, such as precipitation and temperature patterns for streams, altitude and rocky substrate for rock tanks, and soil characteristics and the presence of aquatic macrophytes for ponds and reservoirs. Assemblages observed in the four types of permanent habitats are thus compositionally distinct due to species replacements between local communities, which in turn are strongly influenced by environmental variables. Similar to previous studies, our results show that spatial turnover largely measures the same phenomenon as total beta diversity on a regional scale.  相似文献   
2.
《地学前缘(英文版)》2020,11(3):871-883
Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions.  相似文献   
3.
随着化肥、农膜等在农业生产中的过量投入,耕地面源污染的程度随之加重。文章选取塔里木河流域上游和田地区为研究区域,依据P-S-R框架理论,构建和田地区耕地面源污染生态风险评价指标体系,加入土壤理化数据,使用生态风险评价模型对和田地区1980 年及2016 年耕地面源污染状况进行生态风险评价,运用耕地生态风险模型、生态风险转移矩阵、Arcgis分析和田地区耕地面源污染时空分异状况。研究结论如下:和田地区1980 年耕地生态风险等级均为II级或III级,呈“中间高,两侧低”分布;2016 年耕地生态风险等级上升至IV级或V级,呈“倒W型”分布,各县耕地面源污染程度较1980 年均有较大幅度的上升,其中墨玉县和于田县在2016 年耕地生态风险等级达到最高的V级,而民丰县因自身生态环境的强脆弱性,同样需要提高关注。根据面源污染“从源头治理”的原则,应切实推进和田地区耕地生态环境保护与治理,提高政府重视程度,增强技术指导,开展试点工作,改善和田地区耕地面源污染现状。  相似文献   
4.
李雪梅 《干旱区地理》2019,42(1):180-186
绿洲城镇组群是新疆特殊区域形成的规模相对较小的单一中心空间自组织模式。运用城市中心性指数、城市经济联系模型和Theil系数对新疆八大绿洲城镇组群内部城镇中心性、经济联系及空间差异测度。结果显示:绿洲城镇组群内部的中心城市的中心性职能较强,周边城镇的中心性职能相对较弱,形成了单中心的空间自组织模式;绿洲城镇组群内部经济联系量和经济联系隶属度大小的排序一致,离中心城市的距离越近、经济发展水平越高,经济联系隶属度越高;近10 a年来绿洲城镇组群的整体空间差异一直在扩大,且呈现出继续扩大趋势。在此基础上,提出了建立区域合作协调机制、明确城镇组群发展方向、增强中心城市的辐射带动作用、实现产业合理分工以及构建制度保障体系促进绿洲城镇组群的协同发展。  相似文献   
5.
The discovery of spatial clusters formed by proximal spatial units with similar non-spatial attribute values plays an important role in spatial data analysis. Although several spatial contiguity-constrained clustering methods are currently available, almost all of them discover clusters in a geographical dataset, even though the dataset has no natural clustering structure. Statistically evaluating the significance of the degree of homogeneity within a single spatial cluster is difficult. To overcome this limitation, this study develops a permutation test approach Specifically, the homogeneity of a spatial cluster is measured based on the local variance and cluster member permutation, and two-stage permutation tests are developed to determine the significance of the degree of homogeneity within each spatial cluster. The proposed permutation tests can be integrated into the existing spatial clustering algorithms to detect homogeneous spatial clusters. The proposed tests are compared with four existing tests (i.e., Park’s test, the contiguity-constrained nonparametric analysis of variance (COCOPAN) method, spatial scan statistic, and q-statistic) using two simulated and two meteorological datasets. The comparison shows that the proposed two-stage permutation tests are more effective to identify homogeneous spatial clusters and to determine homogeneous clustering structures in practical applications.  相似文献   
6.
李鸿宇  袁桂平 《地震》2018,38(1):157-166
收集全国102个地磁台站2008年至2015年数字化地磁资料, 运用地磁空间相关法计算每日凌晨02时各台站地磁总场F之间的空间相关系数。 在使用相同的异常判别标准的情况下, 研究时段的17个中强地震中发现在2009年9月19日陕西宁强5.1级地震, 2011年11月1日四川青川5.4级地震, 2013年1月23日辽宁灯塔5.1级地震, 2013年7月22日甘肃岷县6.6级地震和2013年10月31日吉林前郭5.5级地震前均具有较为明显的空间相关低值异常现象。 通过总结5个震例的异常特征, 笔者发现其异常形态极其相似, 且平均的异常持续时间为20天, 而地震就发生在异常开始后3个月内; 同时, 地震发生在异常集中区中心附近, 且这个异常区域大小在500 km左右。 这一研究结果对于进一步分析地震前地磁空间相关异常特征积累了丰富的资料。  相似文献   
7.
了解物种利用资源和占据生态空间的能力,对维持完善和科学保育荒漠戈壁植物群落的多样性具有重要意义。在综合反映各生态因子作用的群落类型和海拔梯度组合而成的两条资源轴上,测度分析了甘肃酒泉荒漠戈壁灌木群落主要优势种的生态位特征。结果表明:(1)在群落类型和海拔梯度两条资源轴上,红砂(Reaumuria songarica)、泡泡刺(Nitraria sphaerocarpa)和合头草(Sympegma regelii)的重要值和生态位宽度均较大,说明这些物种适应能力强,能够较好地利用环境资源,分布范围大,作为荒漠戈壁灌木群落中的广域种具有重要的生态地位和作用。(2)荒漠戈壁优势物种间的生态位重叠值多数较小,在群落类型和海拔梯度资源轴上生态位重叠值小于0.5的分别占总种对的62.63%和77.89%。生态位宽度大的物种之间一般生态位重叠值较高,物种利用资源能力强且存在竞争关系;然而,生态位宽度较小的物种与其他物种之间的生态位重叠程度较低,不同物种在环境资源的需求上产生互补,可以和谐共存;生态位宽度小的物种之间生态位重叠值仍较高,物种分布呈斑块现象;因此,生态位重叠与生态位宽度之间无显著相关性。(3)荒漠戈壁优势物种间总体表现为不显著的正关联,表明该植被群落结构及其物种之间处于稳定共存的状态。  相似文献   
8.
The phase identification and travel time picking are critical for seismic tomography, yet it will be challenging when the numbers of stations and earthquakes are huge. We here present a method to quickly obtain P and S travel times of pre-determined earthquakes from mobile dense array with the aid from long term phase records from co-located permanent stations. The records for 1 768 M ≥ 2.0 events from 2011 to 2013 recorded by 350 ChinArray stations deployed in Yunnan Province are processed with an improved AR-AIC method utilizing cumulative envelope and rectilinearity. The reference arrivals are predicted based on phase records from 88 permanent stations with similar spatial coverage, which are further refined with AR-AIC. Totally, 718 573 P picks and 512 035 S picks are obtained from mobile stations, which are 28 and 22 times of those from permanent stations, respectively. By comparing the automatic picks with manual picks from 88 permanent stations, for M ≥ 3.0 events, 81.5% of the P-pick errors are smaller than 0.5 second and 70.5% of S-pick errors are smaller than 1 second. For events with a lower magnitude, 76.5% P-pick errors fall into 0.5 second and 69.5% S-pick errors are smaller than 1 second. Moreover, the Pn and Sn phases are easily discriminated from directly P/S, indicating the necessity of combining traditional auto picking and integrating machine learning method.  相似文献   
9.
Spatial predictions of forest variables are required for supporting modern national and sub-national forest planning strategies, especially in the framework of a climate change scenario. Nowadays methods for constructing wall-to-wall maps and calculating small-area estimates of forest parameters are becoming essential components of most advanced National Forest Inventory (NFI) programs. Such methods are based on the assumption of a relationship between the forest variables and predictor variables that are available for the entire forest area. Many commonly used predictors are based on data obtained from active or passive remote sensing technologies. Italy has almost 40% of its land area covered by forests. Because of the great diversity of Italian forests with respect to composition, structure and management and underlying climatic, morphological and soil conditions, a relevant question is whether methods successfully used in less complex temperate and boreal forests may be applied successfully at country level in Italy.For a study area of more than 48,657 km2 in central Italy of which 43% is covered by forest, the study presents the results of a test regarding wall-to-wall, spatially explicit estimation of forest growing stock volume (GSV) based on field measurement of 1350 plots during the last Italian NFI. For the same area, we used potential predictor variables that are available across the whole of Italy: cloud-free mosaics of multispectral optical satellite imagery (Landsat 5 TM), microwave sensor data (JAXA PALSAR), a canopy height model (CHM) from satellite LiDAR, and auxiliary variables from climate, temperature and precipitation maps, soil maps, and a digital terrain model.Two non-parametric (random forests and k-NN) and two parametric (multiple linear regression and geographically weighted regression) prediction methods were tested to produce wall-to-wall map of growing stock volume at 23-m resolution. Pixel level predictions were used to produce small-area, province-level model-assisted estimates. The performances of all the methods were compared in terms of percent root mean-square error using a leave-one-out procedure and an independent dataset was used for validation. Results were comparable to those available for other ecological regions using similar predictors, but random forests produced the most accurate results with a pixel level R2 = 0.69 and RMSE% = 37.2% against the independent validation dataset. Model-assisted estimates were more precise than the original design-based estimates provided by the NFI.  相似文献   
10.
国土空间规划的海洋分区研究   总被引:2,自引:0,他引:2  
周鑫  陈培雄  黄杰  王权明 《海洋通报》2020,39(4):408-415
国土空间分区作为国土空间规划编制和自然资源统一管理的基础,对整个规划体系具有重要的支撑作用。在统一的国土空间规划体系下,用海分区既有其独立性与特殊性,也面临着陆海统筹、生态文明建设、海洋高质量发展等政策提出的 新要求。文章系统梳理了 1983 年以来海洋功能分区体系 4 次演变历程,结合国土空间规划改革对分区体系的内在需求,指出海洋功能分区体系存在未覆盖海域全域,分区层级的尺度、分工、内容和结构与国土空间规划的要求不一致,分区方案未考虑用途管制的需求,以及陆海统筹不足等问题。在剖析现有各类规划政策分区、管制分区和功能分区在国土空间规划体系中的定位和作用的基础上,提出以海洋主体功能区为基础,海洋生态红线为底线,以原海洋功能区划分区为主体,将无居民海岛纳入规划范围,并在生态、农业、城镇空间的大框架下统筹协调海陆分区,形成国土空间规划中的海洋分区体系。全国和省级国土空间规划统筹海陆主体功能,划定主体功能区,并建立海洋产业保障区名录、无居民海岛保护与利用名录和海洋自然保护地名录。市县级国土空间规划划定“功能+管制”的利用分区,共划分出海洋保护区、海洋发展区和海洋保留3个一级分区以及核心保护区、一般控制区、海水增养殖区等 12 个二级分区。  相似文献   
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