Application of Sample Selection Model in Estimating Response Functions for Nitrate Percolation

The Seymour aquifer region of Texas has been identified as containing elevated levels of nitrate in ground water. Various state and federal agencies are currently studying policy options for the region by gathering more site-specific information. However, because of lack of sufficient information, cause and effect relationships between water quality and agricultural practices have not been well established for the region. Some recently available biophysical simulation models have impressive capabilities in generating large amounts of data on environmental pollution resulting from agricultural production practices. In this study, the data generated by a biophysical simulation model were used to estimate the nitrate percolation response functions for the Seymour aquifer region. Interestingly, nitrate percolation values obtained from simulation models often comprise acensoredsample because the non-zero percolation values are only observed under certain climatic events and input levels. It has been shown in the econometric literature that the use of Ordinary Least Squares (OLS) on censored sample data produces biased and inconsistent parameter estimates. Thus, a sample selection model was used in this study to estimate the response functions for nitrate percolation. The study provides some insight into the relationship between nitrate percolation and agricultural production practices. In particular, the study demonstrates the potential of selected design standards in minimizing agricultural nonpoint-source (NPS) pollution for the study area.     

渤海油田中深层低含H2S气井油套管选材研究

目的 解决渤海油田中深层低含H2S井下环境的油套管安全选材问题.方法 利用高压釜模拟气井的腐蚀环境,对不同材质的备选油套管钢在不同井深模拟工况条件下的腐蚀速率和应力腐蚀开裂行为进行研究,采用电子扫描显微镜(SEM)及其能谱仪(EDS)和X射线衍射分析仪(XRD),分析或检测腐蚀形貌、腐蚀产物的组成,综合考虑均匀腐蚀和开...     

化学防护服的分类及选用

本文较为系统地介绍了化学防护服的分类方法、典型化学防护服的特点和适用场合及化学防护服选择应考虑的主要因素和一般程序,对化学防护服的选用具有一定的指导意义。     

Sensitivity of species-distribution models to error,bias, and model design: An application to resource selection functions for woodland caribou

Models that predict distribution are now widely used to understand the patterns and processes of plant and animal occurrence as well as to guide conservation and management of rare or threatened species. Application of these methods has led to corresponding studies evaluating the sensitivity of model performance to requisite data and other factors that may lead to imprecise or false inferences. We expand upon these works by providing a relative measure of the sensitivity of model parameters and prediction to common sources of error, bias, and variability. We used a one-at-a-time sample design and GPS location data for woodland caribou (Rangifer tarandus caribou) to assess one common species-distribution model: a resource selection function. Our measures of sensitivity included change in coefficient values, prediction success, and the area of mapped habitats following the systematic introduction of geographic error and bias in occurrence data, thematic misclassification of resource maps, and variation in model design. Results suggested that error, bias and model variation have a large impact on the direct interpretation of coefficients. Prediction success and definition of important habitats were less responsive to the perturbations we introduced to the baseline model. Model coefficients, prediction success, and area of ranked habitats were most sensitive to positional error in species locations followed by sampling bias, misclassification of resources, and variation in model design. We recommend that researchers report, and practitioners consider, levels of error and bias introduced to predictive species-distribution models. Formal sensitivity and uncertainty analyses are the most effective means for evaluating and focusing improvements on input data and considering the range of values possible from imperfect models.     

发展资源再生产业是中国资源战略的一场革命

中国已经成为资源消耗大国，实施再生型资源战略，发展循环经济是我国的必然选择。资源再生产业与“循环经济”取代“线性经济”、经济全球化形成“国际大分工”和“国际大循环”的发展趋势同步，是解决资源和环境问题的根本出路。资源再生产业是一座“富矿”，没有“资源再生”就没有中国的“环境保护”。中国资源再生产业的发展为什么步履艰难？它和进口渠道不畅。国民对资源短缺认识不足、传统的“线性思维方式”的影响，来自国内外“利益集团”的阻力，以及政策不刭位等因素密切相关。因此，提出中国发展资源再生产业的对策和建议：①在经济相对滞后、交通便利的地区建立“资源再生加工区”；②建立畅通的全球物质回收“绿色通道”；③建立“国际再生资源交易市场”；④成立“国际资源再生促进会”；⑤改变“线性经济”背景下的传统观念。     

Tradition without social learning: scent-mark-based communal roost formation in a Neotropical harvestman (Prionostemma sp.)

Most models of habitat selection assume that individual animals choose and either reuse or abandon sites based on a constant reassessment of site quality. When survival is a function of the presence of conspecifics, however, the benefits of returning to traditional sites may override resource assessment. Many animals form roosting aggregations at what appear to be traditional sites. At our study site in Nicaragua, the harvestman Prionostemma sp. forms diurnal roosting aggregations on a small subset of the available spiny palm trees. With respect to physical characteristics and microclimate, the spiny palms used by the harvestmen resembled a random sample of those available, yet the same subset of trees was used in two different years (2001, 2003). This suggests that the location of aggregation sites is traditional, not a product of habitat limitation. Individual harvestmen were not faithful to particular roost sites, however, which raises the question of how the tradition could be maintained over time. In this paper, we present evidence, derived from a series of small-scale field experiments, that the harvestmen mark roosting sites chemically and enter marked sites preferentially when searching for places to roost. We also show that the harvestmen are sensitive to changes in site quality (the presence of spines) but will continue to use degraded traditional sites when no intact spiny palms are nearby. This system provides an example of how animal traditions could be maintained over multiple generations without learning. Site-labeling can be viewed as an external form of social memory.     

Estimates of Natural Selection in a Salmon Population in Captive and Natural Environments

Abstract:  Captive breeding is a commonly used strategy for species conservation. One risk of captive breeding is domestication selection—selection for traits that are advantageous in captivity but deleterious in the wild. Domestication selection is of particular concern for species that are bred in captivity for many generations and that have a high potential to interbreed with wild populations. Domestication is understood conceptually at a broad level, but relatively little is known about how natural selection differs empirically between wild and captive environments. We used genetic parentage analysis to measure natural selection on time of migration, weight, and morphology for a coho salmon ( Oncorhynchus kisutch ) population that was subdivided into captive and natural components. Our goal was to determine whether natural selection acting on the traits we measured differed significantly between the captive and natural environments. For males, larger individuals were favored in both the captive and natural environments in all years of the study, indicating that selection on these traits in captivity was similar to that in the wild. For females, selection on weight was significantly stronger in the natural environment than in the captive environment in 1 year and similar in the 2 environments in 2 other years. In both environments, there was evidence of selection for later time of return for both males and females. Selection on measured traits other than weight and run timing was relatively weak. Our results are a concrete example of how estimates of natural selection during captivity can be used to evaluate this common risk of captive breeding programs.     

避免不可抗力对工厂安全的威胁

洪水、地震、泥石流、台风等不可抗力对工厂安全造成威胁,因此厂址选择阶段如何考虑不可抗力因素,如何处理不可抗力因素与其它建厂条件间的关系十分重要.提出如何避免或减小不可抗力对工厂安全的威胁,并列举工程实例进一步说明处理方法.     

Nest selection by snow petrels Pagodroma nivea in East Antarctica: Validating predictive habitat selection models at the continental scale

Little is known on the factors controlling distribution and abundance of snow petrels in Antarctica. Studying habitat selection through modeling may provide useful information on the relationships between this species and its environment, especially relevant in a climate change context, where habitat availability may change. Validating the predictive capability of habitat selection models with independent data is a vital step in assessing the performance of such models and their potential for predicting species’ distribution in poorly documented areas.From the results of ground surveys conducted in the Casey region (2002–2003, Wilkes Land, East Antarctica), habitat selection models based on a dataset of 4000 nests were created to predict the nesting distribution of snow petrels as a function of topography and substrate. In this study, the Casey models were tested at Mawson, 3800 km away from Casey. The location and characteristics of approximately 7700 snow petrel nests were collected during ground surveys (Summer 2004–2005). Using GIS, predictive maps of nest distribution were produced for the Mawson region with the models derived from the Casey datasets and predictions were compared to the observed data. Models performance was assessed using classification matrixes and Receiver operating characteristic (ROC) curves. Overall correct classification rates for the Casey models varied from 57% to 90%. However, two geomorphologically different sub-regions (coastal islands and inland mountains) were clearly distinguished in terms of habitat selection by Casey model predictions but also by the specific variations in coefficients of terms in new models, derived from the Mawson data sets. Observed variations in the snow petrel aggregations were found to be related to local habitat availability.We discuss the applicability of various types of models (GLM, CT) and investigate the effect of scale on the prediction of snow petrel habitats. While the Casey models created with data collected at the nest scale did not perform well at Mawson due to regional variations in nest micro-characteristics, the predictive performance of models created with data compiled at a coarser scale (habitat units) was satisfactory. Substrate type was the most robust predictor of nest presence between Casey and Mawson. This study demonstrate that it is possible to predict at the large scale the presence of snow petrel nests based on simple predictors such as topography and substrate, which can be obtained from aerial photography. Such methodologies have valuable applications in the management and conservation of this top predator and associated resources and may be applied to other Antarctic, Sub-Antarctic and lower latitudes species and in a variety of habitats.     

Importance of movement constraints in habitat selection studies

The aim of this study is to empirically illustrate the importance of taking movement constraints into account when testing for habitat selection with telemetry data. Global Positioning System relocations of two Scandinavian brown bears were used to compare the results of two different tests of habitat selection by the bears within their home range. Both relied on the comparison of observed dataset with datasets simulated under the hypothesis of random habitat use. The first analysis did not take movement constraints into account (simulations were carried out by randomly distributing a set of points in the home range) whereas the second analysis accounted for these constraints (simulations were carried out by building random trajectories in the home range). The results for the two analyses showed contrasted results. Therefore, not accounting for movement constraints in analyses may result in a misleading biological interpretation. Autocorrelation between relocations is not undesirable: it contains information about ecological processes that should be integrated in habitat selection analyses.