Expert judgment and uncertainty regarding the protection of imperiled species

Decisions concerning the appropriate listing status of species under the U.S. Endangered Species Act (ESA) can be controversial even among conservationists. These decisions may determine whether a species persists in the near term and have long‐lasting social and political ramifications. Given the ESA's mandate that such decisions be based on the best available science, it is important to examine what factors contribute to experts’ judgments concerning the listing of species. We examined how a variety of factors (such as risk perception, value orientations, and norms) influenced experts’ judgments concerning the appropriate listing status of the grizzly bear (Ursus arctos horribilis) population in the Greater Yellowstone Ecosystem. Experts were invited to complete an online survey examining their perceptions of the threats grizzly bears face and their listing recommendation. Although experts’ assessments of the threats to this species were strongly correlated with their recommendations for listing status, this relationship did not exist when other cognitive factors were included in the model. Specifically, values related to human use of wildlife and norms (i.e., a respondent's expectation of peers’ assessments) were most influential in listing status recommendations. These results suggest that experts’ decisions about listing, like all human decisions, are subject to the use of heuristics (i.e., decision shortcuts). An understanding of how heuristics and related biases affect decisions under uncertainty can help inform decision making about threatened and endangered species and may be useful in designing effective processes for protection of imperiled species.     

Social values and biodiversity conservation in a dynamic world

Understanding what shape values (which ultimately shape human behavior) will help improve the effectiveness of conservation solutions that depend on public support. To contribute to this understanding, we investigated the influence of societal‐level changes, such as modernization, on values in a multilevel framework. We collected survey responses (n = 4183) to questionnaires mailed to a random selection of households within each county in Washington (U.S.A.) (response rate 32%). We used multilevel modeling to determine the relationship between modernization (e.g., county‐level urbanization, wealth, and education) and wildlife value orientations (values that shape thought about wildlife) while controlling for individual‐level sociodemographics. We then explored how values influence conservation support at different levels (e.g., individual and county) and how values explain conservation support in a case study of public responses to wolf (Canis lupis) recovery. We found positive associations between county‐level examples of modernization and mutualism (a wildlife value orientation that prioritizes the perceived needs of wildlife) independent of a respondent's sociodemographics, and negative associations between modernization and domination (a wildlife value orientation that prioritizes human needs). Our results suggest that context has an additive impact on one's values; certain locations exhibited domination values, whereas others exhibited a mix of value types. This finding is important because actions that restrict human interests to promote biodiversity were negatively associated with domination and positively associated with mutualism. In the wolf case study, mutualism was strongly correlated with less social conflict over wolf recovery in many, but not all, counties (e.g., Pearson's r correlation = 0.59 in one county and a nonsignificant correlation in another). Our findings suggest that modernization operates on values within a state with implications for biodiversity, but other factors in addition to values must be investigated to fully understand what leads to proconservation behavior.     

基于LLE-ELM的矿井瓦斯涌出量预测方法研究

基于局部线性嵌入(Locally Linear Embedding,LLE)算法和极限学习机(Extreme Learning Machine,ELM)神经网络建立矿井瓦斯涌出量预测模型,该预测模型运用LLE算法对矿井瓦斯涌出量影响因素样本进行数据挖掘,得到降维后的有效因子,再将这些有效因子作为ELM神经网络的输入层进行训练和预测。利用某矿井的实测数据进行实例分析,结果表明该预测模型预测速度快,精度高,能够用于矿井瓦斯涌出量预测。     

MULTIVARIATE EXTREME VALUE DISTRIBUTION WITH MIXED GUMBEL MARGINALS1

ABSTRACT: Bivariate and trivariate distributions have been derived from the logistic model for the multivariate extreme value distribution. Marginals in the models are extreme value type I distributions for two-component mixture variables (mixed Gumbel distribution). This paper is a continuation of the previous works on multivariate distribution in hydrology. Interest is focused on the analysis of floods which are generated by different types of storms. The construction of their corresponding probability distributions and density functions are described. In order to obtain the parameters of such a bivariate or trivariate distribution, a generalized maximum likelihood estimation procedure is proposed to allow for the cases of samples with different lengths of record. A region in Northern Mexico with 42 gauging stations, grouped into two homogeneous regions, has been selected to apply the models. Results produced by the multivariate distributions have been compared with those obtained by the Normal, log-Normal-2, log-Normal-3, Gamma-2, Gamma-3, log-Pearson-3, Gumbel, TCEV and General Extreme Value distributions. Goodness of fit is measured by the criterion of standard error of fit. Results suggest that the proposed models are a suitable option to be considered when performing flood frequency analysis.     

Quantifying the Indicator Power of an Indicator Species

Abstract:  Biodiversity indicator species are needed for classifying biotopes and sites for conservation, and a number of methods have been developed for determining indicator species for this purpose. Nevertheless, in addition to site classification, there is sometimes a need to define an indicator species that indicates the occurrence of another species. For example, when a species of interest (target species) is difficult to detect or identify, a reliable indicator species can function as a tool that saves time and money. We derived a method that provides a quantitative measure of the indicator power (IP) of an indicator species for the target species or any species assemblage. We calculated the measure of IP from a presence–absence matrix that covered several sites. The method provided a list of indicator species, the presence of which reliably indicated the presence of another species (e.g., a threatened or rare species in a given area). The IP of the species was highest when the number of shared occurrences between the indicator species and the target species was high and, simultaneously, when the indicator species and the target species occurred separately in only a few cases. The IP was also positively influenced by the number of sites with no occurrences of either the indicator or the target species. Our method can also be used to quantify different types of species occurrence indications. We refer to these types as presence–presence, presence–absence, absence–presence, and absence–absence indications. To clarify the use of the method, we examined the situation with red-listed polypores in White-backed Woodpecker (Dendrocopos leucotos) habitats in Fennoscandia and found some suitable indicator species. Our method provides a new, objective way to evaluate the IP of an indicator species.     

环境伦理学价值关系的评价——论罗尔斯顿的内在价值理论

罗尔斯顿把价值评价关系引入了自然系统,想从自然的创造性关系引出价值的关系,并把价值最重要的属性看作是创造性,因而,他也就偏离了主体设定的价值取向,把价值的生成看成是自然本身的事情了。     

重要基础设施脆弱性评价模型及其应用

将重要基础设施脆弱性定义为威胁情景、保护作用和重要性的函数,并提出一种可定量化的重要基础设施脆弱性评价模型。模型结合系统理论知识分解系统,选取威慑、延迟、检测、响应4个评价因子评估系统各环节。模型由相关领域专家确定系统各环节权重以及为评价因子赋值,应用线性加权聚集模拟技术,综合评价因子信息;依据系统状况,建立系统各环节价值函数,运行模型得到脆弱性概率密度函数,以数学期望值表示系统脆弱性总值。并将模型应用于天津市部分供水系统,不仅得到系统各环节的实际评价值,而且可得整个供水系统脆弱性评价值,为更好地改进天津市供水系统各环节的安全运行提供了科学依据。     

龙舌兰科植物在室内装饰的应用

龙舌兰科植物形态化美、色彩丰富，是室内植物装饰的好材料。本文对进一步开展龙舌兰科植物在室内装饰的应用作了探讨。     

Projected Climate‐Induced Habitat Loss for Salmonids in the John Day River Network,Oregon, U.S.A.

Abstract: Climate change will likely have profound effects on cold‐water species of freshwater fishes. As temperatures rise, cold‐water fish distributions may shift and contract in response. Predicting the effects of projected stream warming in stream networks is complicated by the generally poor correlation between water temperature and air temperature. Spatial dependencies in stream networks are complex because the geography of stream processes is governed by dimensions of flow direction and network structure. Therefore, forecasting climate‐driven range shifts of stream biota has lagged behind similar terrestrial modeling efforts. We predicted climate‐induced changes in summer thermal habitat for 3 cold‐water fish species—juvenile Chinook salmon, rainbow trout, and bull trout (Oncorhynchus tshawytscha, O. mykiss, and Salvelinus confluentus, respectively)—in the John Day River basin, northwestern United States. We used a spatially explicit statistical model designed to predict water temperature in stream networks on the basis of flow and spatial connectivity. The spatial distribution of stream temperature extremes during summers from 1993 through 2009 was largely governed by solar radiation and interannual extremes of air temperature. For a moderate climate change scenario, estimated declines by 2100 in the volume of habitat for Chinook salmon, rainbow trout, and bull trout were 69–95%, 51–87%, and 86–100%, respectively. Although some restoration strategies may be able to offset these projected effects, such forecasts point to how and where restoration and management efforts might focus.     

中国陆地生态信息空间化技术研究(Ⅰ)——气象/气候信息的空间化技术途径

论文提出了构建中国陆地生态系统空间化信息系统的基本设想,并且在回顾国内外气象/气候信息空间化研究的现状基础上,评价了已有的气象/气候信息要素空间化技术的发展,探讨了中国陆地生态系统气象/气候信息空间化的技术途径,概要地介绍了研究小组在近年来的工作中所取得的阶段性研究成果,展望了这些成果的应用前景。我国陆地生态信息空间化技术研究和数据产品的开发是生态学、资源科学和环境科学发展的迫切需求。但建立一个适应于不同科学研究的精度、时空分辨率要求的陆地生态信息空间数据库需要较长期的努力和多学科领域的合作,也需要大量的作为科学研究基础数据平台建设的国家投资。