Spatial distribution of coarse woody debris in pine forests of the Marii El Transvolga region

The spatial distribution of coarse woody debris in pine forests has been studied in the Transvolga region of the Marii El Republic. For this purpose, 30 test plots have been established in pine (Pinus sylvestris) stands of the study region. The results are indicative of a tendency toward the spatial grouping (clustering) of woody debris by fractions, decomposition classes, and stocks along with increase in stand age.     

洪泽湖底栖动物群落结构及水质季节变化分析

为阐明洪泽湖底栖动物的群落结构及水质变化状况,采用生物多样性指数法、综合污染指数、CCA分析等方法,对洪泽湖的底栖动物群落结构空间布局及其水质状况进行了季度调查,探究环境因子对底栖动物生长的影响规律.结果 表明:(1)底栖动物种类以甲壳纲、多毛纲、双壳纲为主;(2) CPI分析表明,TN、TP是洪泽湖主要的超地表水Ⅲ类...     

Expanding Kenya's protected areas under the Convention on Biological Diversity to maximize coverage of plant diversity

Biodiversity is highly valuable and critically threatened by anthropogenic degradation of the natural environment. In response, governments have pledged enhanced protected‐area coverage, which requires scarce biological data to identify conservation priorities. To assist this effort, we mapped conservation priorities in Kenya based on maximizing alpha (species richness) and beta diversity (species turnover) of plant communities while minimizing economic costs. We used plant‐cover percentages from vegetation surveys of over 2000 plots to build separate models for each type of diversity. Opportunity and management costs were based on literature data and interviews with conservation organizations. Species richness was predicted to be highest in a belt from Lake Turkana through Mount Kenya and in a belt parallel to the coast, and species turnover was predicted to be highest in western Kenya and along the coast. Our results suggest the expanding reserve network should focus on the coast and northeastern provinces of Kenya, where new biological surveys would also fill biological data gaps. Meeting the Convention on Biological Diversity target of 17% terrestrial coverage by 2020 would increase representation of Kenya's plant communities by 75%. However, this would require about 50 times more funds than Kenya has received thus far from the Global Environment Facility.     

Restoration planning to guide Aichi targets in a megadiverse country

Ecological restoration has become an important strategy to conserve biodiversity and ecosystems services. To restore 15% of degraded ecosystems as stipulated by the Convention on Biological Diversity Aichi target 15, we developed a prioritization framework to identify potential priority sites for restoration in Mexico, a megadiverse country. We used the most current biological and environmental data on Mexico to assess areas of biological importance and restoration feasibility at national scale and engaged stakeholders and experts throughout the process. We integrated 8 criteria into 2 components (i.e., biological importance and restoration feasibility) in a spatial multicriteria analysis and generated 11 scenarios to test the effect of assigning different component weights. The priority restoration sites were distributed across all terrestrial ecosystems of Mexico; 64.1% were in degraded natural vegetation and 6% were in protected areas. Our results provide a spatial guide to where restoration could enhance the persistence of species of conservation concern and vulnerable ecosystems while maximizing the likelihood of restoration success. Such spatial prioritization is a first step in informing policy makers and restoration planners where to focus local and large‐scale restoration efforts, which should additionally incorporate social and monetary cost–benefit considerations.     

Understanding the effects of different social data on selecting priority conservation areas

Conservation success is contingent on assessing social and environmental factors so that cost‐effective implementation of strategies and actions can be placed in a broad social–ecological context. Until now, the focus has been on how to include spatially explicit social data in conservation planning, whereas the value of different kinds of social data has received limited attention. In a regional systematic conservation planning case study in Australia, we examined the spatial concurrence of a range of spatially explicit social values and land‐use preferences collected using a public participation geographic information system and biological data. We used Zonation to integrate the social data with the biological data in a series of spatial‐prioritization scenarios to determine the effect of the different types of social data on spatial prioritization compared with biological data alone. The type of social data (i.e., conservation opportunities or constraints) significantly affected spatial prioritization outcomes. The integration of social values and land‐use preferences under different scenarios was highly variable and generated spatial prioritizations 1.2–51% different from those based on biological data alone. The inclusion of conservation‐compatible values and preferences added relatively few new areas to conservation priorities, whereas including noncompatible economic values and development preferences as costs significantly changed conservation priority areas (48.2% and 47.4%, respectively). Based on our results, a multifaceted conservation prioritization approach that combines spatially explicit social data with biological data can help conservation planners identify the type of social data to collect for more effective and feasible conservation actions.     

Representation of critical natural capital in China

Traditional means of assessing representativeness of conservation value in protected areas depend on measures of structural biodiversity. The effectiveness of priority conservation areas at representing critical natural capital (CNC) (i.e., an essential and renewable subset of natural capital) remains largely unknown. We analyzed the representativeness of CNC‐conservation priority areas in national nature reserves (i.e., nature reserves under jurisdiction of the central government with large spatial distribution across the provinces) in China with a new biophysical‐based composite indicator approach. With this approach, we integrated the net primary production of vegetation, topography, soil, and climate variables to map and rank terrestrial ecosystems capacities to generate CNC. National nature reserves accounted for 6.7% of CNC‐conservation priority areas across China. Considerable gaps (35.2%) existed between overall (or potential) CNC representativeness nationally and CNC representation in national reserves, and there was significant spatial heterogeneity of representativeness in CNC‐conservation priority areas at the regional and provincial levels. For example, the best and worst representations were, respectively, 13.0% and 1.6% regionally and 28.9% and 0.0% provincially. Policy in China is transitioning toward the goal of an ecologically sustainable civilization. We identified CNC‐conservation priority areas and conservation gaps and thus contribute to the policy goals of optimization of the national nature reserve network and the demarcation of areas critical to improving the representativeness and conservation of highly functioning areas of natural capital. Moreover, our method for assessing representation of CNC can be easily adapted to other large‐scale networks of conservation areas because few data are needed, and our model is relatively simple.     

A multiscale hierarchical Markov transition matrix model for generating and analyzing thematic raster maps

A model is described for generating hierarchically scaled spatial pattern as represented in a thematic raster map. The model involves a series of Markov transition matrices, one for each level in the scaling hierarchy. In full generality, the model allows the transition matrices to be different at each level, potentially making available a large number of parameters for landscape characterization. The model is self-similar when the transition matrices are all equal. A method is presented for fitting the model to data that take the form of a single-resolution thematic raster map. Explicit analytic solutions are obtained for the fitted parameters. The fitting method is based on a relationship between the hierarchical transitions in the model and spatial transitions at varying distance scales in the data map, a categorical analogy of the geostatistical variogram.     

A state-space model approach to optimum spatial sampling design based on entropy

We consider the spatial sampling design problem for a random field X. This random field is in general assumed not to be directly observable, but sample information from a related variable Y is available. Our purpose in this paper is to present a state-space model approach to network design based on Shannon's definition of entropy, and describe its main points with regard to some of the most common practical problems in spatial sampling design. For applications, an adaptation of Ko et al.'s (1995) algorithm for maximum entropy sampling in this context is provided. We illustrate the methodology using piezometric data from the Velez aquifer (Malaga, Spain). © Rapid Science 1998     

川南6市空气中SO2和NO2空间分布特征

利用简单、方便、不用电源的被动采样技术分别对川南地区6市区域范围内空气中SO2和NO2在雨季和旱季的空间分布特征进行研究。按16 km×16 km的均匀网格布设点位162个,并在雨季于6市各选1个空气自动站布设城区点位采集空气中SO2和NO2。获得的样品经0.3%的双氧水浸提后采用离子色谱法进行分析,经换算后获得空气中SO2和NO2的浓度,了解了其含量水平,绘制了SO2和NO2的空间分布图,掌握了其空间分布特征,并对可能的来源进行了浅析。将被动采样监测结果与自动站监测结果进行相关分析,两者高度线性相关,NO2和SO2线性相关系数分别为0.901 3和0.874 5,均大于r0.05(4)=0.811 4。     

危岩块体稳定性分析的蒙特卡罗边界法

在危岩块体的稳定性的可靠度分析中,本文采用了一种新的可靠度分析方法──蒙特卡罗边界法．并将此方法应用于清红电站V#号危岩块体的计算和稳定性评价,其结果与实际监测成果基本吻合．