Bayesian model-based cluster analysis for predicting macrofaunal communities

To predict macrofaunal community composition from environmental data a two-step approach is often followed: (1) the water samples are clustered into groups on the basis of the macrofauna data and (2) the groups are related to the environmental data, e.g. by discriminant analysis. For the cluster analysis in step 1 many hard, seemingly arbitrary choices have to be made that nevertheless influence the solution (similarity measure, clustering strategy, number of clusters). The stability of the solution is often of concern, e.g. in clustering by the program. In the discriminant analysis of step 2 it can occur that a water sample is misclassified on the basis of the environmental data but on further inspection happens to be a borderline case in the cluster analysis. One would then rather reclassify such a sample and iterate the two steps. Bayesian latent class analysis is a flexible, extendable model-based cluster analysis approach that recently has gained popularity in the statistical literature and that has the potential to address these problems. It allows the macrofauna and environmental data to be modelled and analyzed in a single integrated analysis. An exciting extension is to incorporate in the analysis prior information on the habitat preferences of the macrofauna taxa such as is available in lists of indicator values. The output of the analysis is not a hard assignment of water samples to clusters but a probabilistic (fuzzy) assignment. The number of clusters is determined on the basis of the Bayes factor. A standard feature of the Bayesian method is to make predictions and to assess their uncertainty. We applied this approach to a data set consisting of 70 water samples, 484 macrofauna taxa and four environmental variables for which previously a five cluster solution had been proposed. The standard for Bayesian estimation, the Gibbs sampler, worked fine on a subset with only 12 selected taxa but did not converge on the full set with 484 taxa. This is due to many configurations in which the assignment probabilities are all very close to either 0 or 1. This convergence problem is comparable with the local optima problem in classical cluster optimization algorithms, including the EM algorithm used in Latent Gold, a Windows program for latent class analysis. The convergence problem needs to be solved before the benefits of Bayesian latent class analysis can come to fruition in this application. We discuss possible solutions.     

Assessment of groundwater quality in Kavaratti Island in the Lakshadweep Archipelagos,India

This study attempts to address the processes controlling the chemical composition of the Kavaratti aquifer system of Lakshadweep Island in India. Major ions and other physico-chemical parameters were determined for the premonsoon period in the Kavaratti of the Lakshadweep Island system. The trilinear diagram confirms the ingress of sea water into the shallow lens of freshwater in the islands by the changing water types. The abundance of major cations varied in the order Na>Ca>Mg>K and anions in the order Cl>HCO₃>SO₄. The ionic relations suggest that the higher concentration Na and Cl are the results of ion exchange and evaporation. The plots of data on the Gibbs diagram suggest that chemical weathering of rock-forming minerals and evaporation are the dominant factors controlling groundwater chemistry in the area. The hydrochemistry concept was deduced by the multivariate analysis for better understanding on the dynamic and complexity of the groundwater chemical processes. By the multivariate analytical techniques, the samples were grouped into two: one with saline water dominance and the other with freshwater characteristics. Thus proper management of these aquifers should be assured to retain their freshwater yield in the future.     

上海海湾国家森林公园1 hm2样地群落结构特征及其动态监测意义

运用生态系统定位监测方法,于2011年在上海市奉贤区海湾国家森林公园内设置面积为100 m×100 m的样地,记录样地内的所有植物种类,定位调查高于1.3m的木本植物,分析其种类组成、区系成分、垂直结构和水平结构.结果表明,1 hm2样地内共记录有维管束植物31科68属73种,其中温带区系成分占总属数的51.8％,热带区系成分占46.4％.高于1.3m的木本植株3 094株,其中常绿树种6种,共1 670株,重要值占48.0％;落叶树种7种,共1 424株,重要值占52.0％.女贞(Ligustrum lucidum)、喜树(Camptotheca acuminata)、椤木石楠(Photinia davidsoniae)、乌桕(Sapium sebiferum)、黄山栾树(Koelreuteria integrifoliola)和香樟(Cinnamomum camphora)的重要值位居前6位,在垂直空间上呈分层结构.在0～50 m尺度范围内,女贞种群均呈集群分布,喜树种群呈均匀-随机-集群分布,椤木石楠和乌桕种群表现为集群-随机-均匀分布,黄山栾树和香樟种群表现为随机-集群-随机分布.从群落结构特征看,样地为以北温带成分、泛热带成分为主的常绿落叶阔叶混交林,表征了该地区自然植被的结构特征.建立长期固定样地可为长三角城市化地区平原地貌上城市森林的发生、发展和演替规律提供长期的数据基础.     

A phenomenological model without dispersal kernel to model species migration

Phenomenological approaches to model species migration are usually based on kernel-based methods. These methods require a good knowledge of the dispersal agent behaviour for a given species. They also calculate the location of individuals independently to each other (except the mother plant) and then suppress some of them according to additional interactions such as competition, facilitation and recruitment. In this paper, we propose to use a new phenomenological method, the Gibbs method, to model tree species migration at large scale. The Gibbs method handles the location of adult individuals in terms of pairwise interactions described by a potential function. This function summarizes the set of known and unknown factors determining the spatial distribution of the individuals (or cohorts). The principle of the Gibbs method is to minimize the sum of all pairwise interactions, also called the cost function, in order to optimize the spatial point pattern according to the chosen potential function.     

Bayes Estimation of a Distribution Function Using Ranked Set Samples

Aranked set sample (RSS), if not balanced, is simply a sample of independent order statistics gener- ated from the same underlying distribution F. Kvam and Samaniego (1994) derived maximum likelihood estimates of F for a general RSS. In many applications, including some in the environ- mental sciences, prior information about F is available to supplement the data-based inference. In such cases, Bayes estimators should be considered for improved estimation. Bayes estimation (using the squared error loss function) of the unknown distribution function F is investigated with such samples. Additionally, the Bayes generalized maximum likelihood estimator (GMLE) is derived. An iterative scheme based on the EM Algorithm is used to produce the GMLE of F. For the case of squared error loss, simple solutions are uncommon, and a procedure to find the solution to the Bayes estimate using the Gibbs sampler is illustrated. The methods are illustrated with data from the Natural Environmental Research Council of Great Britain (1975), representing water discharge of floods on the Nidd River in Yorkshire, England     

Comments on "Ion adsorption components in liquid/solid systems"

Recently, Wu et al. (J Environ Sci 18(2006) 1167-1175) published a paper entitled as above. In the paper, the authors proposed a plotting method for describing adsorption isotherm, where adsorption density (q e) was plotted against the ratio of equilibrium concentration/particle concentration (Ce/W0) rather than (Ce) as traditionally defined. The authors claimed that this plot can eliminate the "particle concentration effect" (i.e., adsorption isotherm declines with increasing particle concentration), which may otherwise be inevasible with traditionally defined adsorption isotherms. We think that their conclusion is conceptually flawed and the plot may cause substantial inconstancy problems in practice.     

中条山东段植被垂直带的数量分类研究

在野外调查的基础上,应用有序样方聚类法对中条山东段植 垂直带进行了数量分类研究,结果为：１．落叶阔叶林带,包括４个亚带：（１）侧柏林带（５００￣８００ｍ）,（２０松栎林带（８００￣１８００ｍ）,（３）栓皮林带（１４００￣２０００ｍ）和（４）杨桦林带（１９００￣２２００ｍ）;２．山地草甸带（２１００￣２３５８ｍ）。此外,与前人的划分结果进行了比较,结果表明,有序样方聚类法的结果更符合实际情况。     

Protocol for using compound‐specific isotope analysis in environmental forensics

Forensic arguments must be demonstrably objective. A protocol is proposed to supply a framework to achieve this when using data from compound specific isotope analysis (CSIA) in developing forensic arguments about volatile organic carbon (VOC) contamination in groundwater. The protocol uses a “line of evidence” approach to reiteratively refine a hypothesis by testing it against each of three criteria, modifying the hypothesis if it fails or accumulating supporting evidence if it passes. The criteria are intended to organize data interpretation and to maximize use of both site data and supporting literature. Specifically, the criteria are: 1) degradation: enrichment factors and dual isotope plot (DIP) slopes; 2) hydrogeology: groundwater flow and plume shape; and 3) site history: VOC use and previous remediation work. Each of these criteria is described in detail and examples are provided for these criteria to show how each criterion can be used to identify the more plausible explanation when alternate hypotheses are presented. The protocol built upon these criteria is intended to maximize the use of supporting data and to document that support, as well as to ensure that interpretations are self consistent. The protocol is intended to be flexible and provides the framework to use exceptions as a line of evidence supporting an explanation.     

基于沿江开发建设的生态安全格局研究——以九江市为例

随着长江沿江开发的不断深入，区域生态安全与开发建设的矛盾也越来越突出。以九江市沿江开发建设活动与区域生态系统空间结构、生态服务功能相互作用为理念，通过遥感、GIS技术手段，划分小流域单元。并将这些单元作为空间分析与评价的基础，选择工业集中区、城镇居民地、路网布局作为自然生态环境的影响因子或施压因子，以河流级别、湖泊数量和面积、地貌类型、坡度、植被覆盖程度、重要生态功能区类别等作为承压因子，综合叠加形成生态环境压力区划。并以此为基础，构建由5个生态源区和9条生态隔离、缓冲、生物多样性保护廊道组成的生态空间安全体系，用以减缓生态环境压力、消纳水气及土壤污染、稳定区域生态、推进沿江开发建设，实现生态保护空间与区域经济社会发展空间的有序融合，并对重要生态功能保护区提出管制的要求与措施。     

Using hidden Markov chains and empirical Bayes change-point estimation for transect data

Consider a lattice of locations in one dimension at which data are observed. We model the data as a random hierarchical process. The hidden process is assumed to have a (prior) distribution that is derived from a two-state Markov chain. The states correspond to the mean values (high and low) of the observed data. Conditional on the states, the observations are modelled, for example, as independent Gaussian random variables with identical variances. In this model, there are four free parameters: the Gaussian variance, the high and low mean values, and the transition probability in the Markov chain. A parametric empirical Bayes approach requires estimation of these four parameters from the marginal (unconditional) distribution of the data and we use the EM-algorithm to do this. From the posterior of the hidden process, we use simulated annealing to find the maximum a posteriori (MAP) estimate. Using a Gibbs sampler, we also obtain the maximum marginal posterior probability (MMPP) estimate of the hidden process. We use these methods to determine where change-points occur in spatial transects through grassland vegetation, a problem of considerable interest to plant ecologists.