Significance of Sighting Rate in Inferring Extinction and Threat

Abstract:  We are now entering a time of immense environmental upheaval in which, increasingly, experts are required to provide conservation assessments. Quantitative assessment of trends in species' range and abundance is costly, requiring extensive field studies over a long period of time. Unfortunately, many species are only known through a few "chance" sightings or a handful of specimens, and therefore extinction may be even harder to ascertain. Several methods have been proposed for estimating the probability of extinction. However, comparison within and between species is difficult because of variations in sighting rates. We applied a probabilistic method that incorporates sighting rate to the sighting record of Vietnamese slipper orchids ( Paphiopedilum ). The method generates a probability that another sighting will occur given the previous sighting rate and the time since last observation. This allows greater comparability between species discovered at different times. Its predictions were more highly correlated with the World Conservation Union criteria than previous methods. Trends in data collection and the political climate of a country, which affects access to material, are important potential sources of variation that affect sighting rates. A lack of understanding of the process by which data are generated makes inferring extinction from sighting records difficult because extinction status depends on how the sighting rate varies. However, such methods allow rapid conservation prioritization of taxa that are poorly known and would otherwise go unassessed.     

Air pollution effects on hospital admissions: a statistical analysis of parallel time series

A statistical model for longitudinal count data is used to examine the potential adverse health effects of ambient air pollution. Daily respiratory admissions to 164 acute care hospitals in Ontario are obtained for the period 1983 to 1988. Estimates of ozone levels in the vicinity of each hospital are determined from air pollution monitoring stations maintained by the Ontario Ministry of the Environment. Generalized estimating equation methods are used to make inferences about the regression and overdispersion parameters. The admission data display little evidence of serial correlation and extra Poisson variation. However, admission rates vary considerably among hospitals. This latter source of variation needs to be taken into account in examining the effects of air pollution on respiratory health status.     

基于BP网络的水质综合评价模型及其应用

讨论了BP网络模型存在的不足及建模条件,提出了建立合理的BP网络模型的基本原则和步骤.针对水质评价问题,通过在各类水质污染指标浓度区间内生成随机分布样本的方法,组成足够多用于BP网络训练、检验和测试用的样本,建立了辽河水质综合评价的BP网络模型;给出了区分不同类别水质的模型分界值样本和模型输出分界值.     

空气自动监测系统软件开发及数据整合模式探讨

空气自动监测系统的仪器仪表多样化，以及数据采集装置和数据格式的差异，给系统软硬件的集成，以及数据应用和管理带来了不便，也是构成空气自动监测信息化进程中的技术瓶颈之一，通过对空气自动监测系统软件开发和数据整合模式的对比分析，探讨了解决问题的途径。     

Incorporating Collateral Data in Conservation Biology

Abstract:  Conservation biologists often need to set ecological modeling assumptions or estimate parameters from sparse data. In some cases this problem can be addressed by incorporating data from closely related species or from the same species at different sites (i.e., collateral data). Currently no structured methods exist for incorporating such information. An analogous problem in Actuarial science is to set premium rates in situations with little direct data on claim frequency or size. The rates are estimated using actuarial credibility theory, which incorporates collateral data with the direct data. actuarial credibility theory combined with the actuarial control cycle financial management process also provides an adaptive mechanism for updating assumptions. This theory may have some utility for ecologists wanting to incorporate collateral data in an adaptive management framework, a companion to approaches such as Bayesian updating. We describe the historical development of actuarial credibility theory from early ad hoc methods to empirical Bayes approaches. We explore some of the theory's strengths, such as relative simple formulae for incorporation collateral data, and we explore some of the theory's weaknesses, such as the use of the best linear approximation to the Bayes estimate. We illustrate potential applications of the theory using an example on the mortality rate of the Powerful Owl (  Ninox strenua ).     

Empirical Bayes calculations of concordance between endpoints in environmental toxicity experiments

Hierarchical models are considered for estimating the probability of agreement between two outcomes or endpoints from an environmental toxicity experiment. Emphasis is placed on generalized regression models, under which the prior mean is related to a linear combination of explanatory variables via a monotone function. This function defines the scale over which the systematic effects are modelled as additive. Specific illustration is provided for the logistic link function. The hierarchical model employs a conjugate beta prior that leads to parametric empirical Bayes estimators of the individual agreement parameters. An example from environmental carcinogenesis illustrates the methods, with motivation derived from estimation of the concordance between two species carcinogenicity outcomes. Based on a large database of carcinogenicity studies, the inter-species concordance is seen to be reasonably informative, i.e. in the range 67–84%. Stratification into pertinent potency-related sub-groups via the logistic model is seen to improve concordance estimation: for environmental stimuli at the extremes of the potency spectrum, concordance can reach well above 90%.     

基于Client/Server数据完整性约束之研究

本文主要讨论基于Client/Server数据完整性约束及其如何实施企业业务规则,并以SQLServer和PowerBuilder为例,介绍了数据完整性约束的实现技术.     

Bayesian testing for trend in a power model for clustered binary data

Developmental toxicity studies are widely used to investigate the potential risk of environmental hazards. In dose–response experiments, subjects are randomly allocated to groups receiving various dose levels. Tests for trend are then often applied to assess possible dose effects. Recent techniques for risk assessment in this area are based on fitting dose–response models. The complexity of such studies implies a number of non-trivial challenges for model development and the construction of dose-related trend tests, including the hierarchical structure of the data, litter effects inducing extra variation, the functional form of the dose–response curve, the adverse event at dam or at fetus level, the inference paradigm, etc. The purpose of this paper is to propose a Bayesian trend test based on a non-linear power model for the dose effect and using an appropriate model for clustered binary data. Our work is motivated by the analysis of developmental toxicity studies, in which the offspring of exposed and control rodents are examined for defects. Simulations show the performance of the method over a number of samples generated under typical experimental conditions.     

Measures of preferred direction for environmental and ecological circular data

Circular or angular variables indicating direction or cyclical time can be of great interest to scientists studying ecology, biology or environmental issues. A common problem of interest in circular data is estimating a preferred direction and its corresponding distribution. This problem is complicated by the so-called “wrap-around effect” on the circle, which exists because there is no natural minimum or maximum. The usual statistics employed for linear data are inappropriate for directional data, as they do not account for its circular nature. Three choices for summarizing the preferred direction (the sample circular mean, sample circular median and a circular analog of the Hodges–Lehmann estimator) are discussed, with examples from environmental and ecological applications. Similar to the linear data case, the relative emphases of different methods sometimes yield different measures of preferred direction in the presence of outliers or lack of symmetry in the original data. Received: November 2003 / Revised: June 2004     

Robust benchmark dose determination based on profile score methods

We investigate several methods commonly used to obtain a benchmark dose and show that those based on full likelihood or profile likelihood methods might have severe shortcomings. We propose two new profile likelihood-based approaches which overcome these problems. Another contribution is the extension of the benchmark dose determination to non full likelihood models, such as quasi-likelihood, generalized estimating equations, which are widely used in settings such as developmental toxicity where clustered data are encountered. This widening of the scope of application is possible by the use of (robust) score statistics. Benchmark dose methods are applied to a data set from a developmental toxicity study.