Bayesian Ecological Regression with Latent Factors: Atmospheric Pollutants Emissions and Mortality for Lung Cancer

In this work we propose a Bayesian ecological analysis in which a latent variable summarizes data on emissions of atmospheric pollutants. We specified a hierarchical Bayesian model with spatially structured and unstructured random terms with a nested latent factor model. This can be considered a combination of the convolution spatial model of Besag et al. (1991) and an ecological regression analysis in which a latent variable plays the role of the covariate. The unified approach allows to proper account for the uncertainty in the latent score estimation in the regression analysis. The Bayesian Latent Factor model is used to summarize the information on environmental pressure derived from three stressors: Carbon Monoxide, Nitrogen Oxides and Inhalable Particles. We found evidence of positive correlation between Lung cancer mortality and environmental pressure indicators, in males, Tuscany (Italy), 1995–1999. Environmental pressure seems to be restricted to fourteen municipalities (top 5% of the Latent Factor distribution). The model identified two areas with high point source emissions.     

Characteristics of clustered particles in skimming flows on a stepped spillway

Air–water flows at hydraulic structures are commonly observed and called white waters. The free-surface aeration is characterised by some intense exchanges of air and water leading to complex air–water structures including some clustering. The number and properties of clusters may provide some measure of the level of particle-turbulence and particle–particle interactions in the high-velocity air–water flows. Herein a re-analysis of air–water clusters was applied to a highly aerated free-surface flow data set (Chanson and Carosi, Exp Fluids 42:385–401, 2007). A two-dimensional cluster analysis was introduced combining a longitudinal clustering criterion based on near-wake effect and a side-by-side particle detection method. The results highlighted a significant number of clustered particles in the high-velocity free-surface flows. The number of bubble/droplet clusters per second and the percentage of clustered particles were significantly larger using the two-dimensional cluster analysis than those derived from earlier longitudinal detection techniques only. A number of large cluster structures were further detected. The results illustrated the complex interactions between entrained air and turbulent structures in skimming flow on a stepped spillway, and the cluster detection method may apply to other highly aerated free-surface flows.     

Assessment of temporal and spatial variations in water quality using multivariate statistical methods： a case study of the Xin＇anjiang River,China

This study evaluated the temporal and spatial variations of water quality data sets for the Xin＇anjiang River through the use of multivariate statistical techniques, including cluster analysis （CA）, discriminant analysis （DA）, correlation analysis, and principal component analysis （PCA）. The water samples, measured by ten parameters, were collected every month for three years （2008-2010） from eight sampling stations located along the river. The hierarchical CA classified the 12 months into three periods （First, Second and Third Period） and the eight sampling sites into three groups （Groups 1, 2 and 3） based on seasonal differences and various pollution levels caused by physicochemical properties and anthropogenic activ- ities. DA identified three significant parameters （tempera- ture, pH and E.coli） to distinguish temporal groups with close to 76% correct assignment. The DA also discovered five parameters （temperature, electricity conductivity, total nitrogen, chemical oxygen demand and total phosphorus） for spatial variation analysis, with 80.56% correct assignment. The non-parametric correlation coefficient （Spear- man R） explained the relationship between the water quality parameters and the basin characteristics, and the GIS made the results visual and direct. The PCA identified four PCs for Groups 1 and 2, and three PCs for Group 3. These PCs captured 68.94%, 67.48% and 70.35% of the total variance of Groups 1, 2 and 3, respectively. Although natural pollution affects the Xin＇anjiang River, the main sources of pollution included agricultural activities, industrial waste, and domestic wastewater.     

Clustering economic sectors in China on a life cycle basis to achieve environmental sustainability

To improve material efficiency, industrial structure optimization becomes a focal point in Chinese industrial and environmental policies. It is crucial to cluster economic sectors and determine their priority for industrial and environmental policy implementation. Integrating a set of criteria, a hybrid input-output model and the hierarchical cluster analysis, this study clusters China’s economic sectors and determines their priority on a life cycle basis. China’s economic sectors are clustered into three clusters. Industrial structure changes (industrial policy) should encourage the development of sectors in cluster 1 and limit the development of sectors in cluster 2. Technology development and materials recycling (two environmental policies) should mainly focus on sectors in clusters 1 and 2. Future industrial policies in China should limit the development of two sectors named Manufacture of metal products and Extraction of petroleum and natural gas. Instead of limiting some industries by command-and-control, the best policy option is to remedy environmental standards and law enforcement. Enterprises belonging to the identified key sectors from the viewpoint of direct production impacts should be concerned to achieve enterprise sustainability. To achieve sustainable production chains, the identified key sectors from the viewpoint of accumulative production impacts should be concerned. For sustainable consumption, the identified key sectors from the viewpoint of consumption impacts should be concerned to transform consumption styles. Most of environmental pressure can be alleviated not only by technical improvements and material recycling, but also by the development of economic sectors in cluster 1.     

Testing spatial cluster occurrence in maps equipped with environmentally defined structures

We propose a novel tool for testing hypotheses concerning the adequacy of environmentally defined factors for local clustering of diseases, through the comparative evaluation of the significance of the most likely clusters detected under maps whose neighborhood structures were modified according to those factors. A multi-objective genetic algorithm scan statistic is employed for finding spatial clusters in a map divided in a finite number of regions, whose adjacency is defined by a graph structure. This cluster finder maximizes two objectives, the spatial scan statistic and the regularity of cluster shape. Instead of specifying locations for the possible clusters a priori, as is currently done for cluster finders based on focused algorithms, we alter the usual adjacency induced by the common geographical boundary between regions. In our approach, the connectivity between regions is reinforced or weakened, according to certain environmental features of interest associated with the map. We build various plausible scenarios, each time modifying the adjacency structure on specific geographic areas in the map, and run the multi-objective genetic algorithm for selecting the best cluster solutions for each one of the selected scenarios. The statistical significances of the most likely clusters are estimated through Monte Carlo simulations. The clusters with the lowest estimated p-values, along with their corresponding maps of enhanced environmental features, are displayed for comparative analysis. Therefore the probability of cluster detection is increased or decreased, according to changes made in the adjacency graph structure, related to the selection of environmental features. The eventual identification of the specific environmental conditions which induce the most significant clusters enables the practitioner to accept or reject different hypotheses concerning the relevance of geographical factors. Numerical simulation studies and an application for malaria clusters in Brazil are presented.     

Compositional analysis of overdispersed counts using generalized estimating equations

Multivariate abundance data are commonly collected in ecology, and used to explore questions of “community composition”—how relative abundance of different taxa changes with environmental conditions. In this paper, we propose a log-linear marginal modeling approach for analyzing such compositional count data, via generalized estimating equations. This method exploits the multiplicative nature of log-linear models for counts, by reparameterizing models that describe marginal effects on mean abundance. This allows partitioning into “main effects” and compositional effects, which is appealing for interpretation. We apply the proposed approach to reanalyze compositional counts of benthic invertebrates from Delaware Bay, and data of invertebrate communities inhabiting Acacia plants in eastern Australia. In both cases we resort to a resampling approach to make inferences about regression parameters, because the number of clusters was not large compared to cluster size.     

Neustonic distributions

In order to determine the effects of environmental parameters on neustonic population structure, correspondence analysis, a multivariate technique of ordination, was applied to data on 47 taxa of neustonic animals, collected at 149 stations between Fiji and the Bay of Biscay from August, 1979 to December, 1980. The positions of taxa and samples on the first 3 axes extracted from the similarity matrix were related to environmental variables. The 3 variables which exerted the greatest influence on neustonic distributions were the quantities of floating tar, plastic and pumice, time of day, and surface water temperature. Floating abiotic material provided settlement areas for epifauna, particularly in the Mediterranean (tar and plastic) and the western Pacific (pumice). Many taxa were more numerous in the surface waters at night. Only 3 of the neustonic taxa examined were most common in boreal waters, all others were primarily tropical or sub-tropical in distribution. The taxa formed 8 associations of fauna which had different distribution patterns.     

西苕溪中上游流域水生甲虫分布与环境的关系

水生甲虫(昆虫纲,鞘翅目)群落结构受多种环境因素的制约.西苕溪中上游流域38个样点共发现水生甲虫12科36个分类单元.优势属是长角泥甲科的Stenelmis属和扁泥甲科的Psephenoides属,出现频度分别为30和22,但有29属的出现频率不大于5.典范对应分析(CCA)表明,鞘翅目群落组成受荫蔽度、总氮、电导率和栖境质量影响最大.样点环境指标聚类结果与CCA结果一致,样点被分为3组.从第1组至第3组,水体中的总氮(2.80→3.52 mg/L)和化学需氧量(14.5→20.29 mg/L)明显上升,荫蔽度(18→6)明显降低.第1组(18个分类单元)和第3组(16个分类单元)的水生甲虫种类组成差异极显著(P＜0.01),第2组水生甲虫多样性最高(26个分类单元).Macronychus,Zaitzevia,Scirtes,Stenocolus,Berosus,Laccobius和Ectopria属可作为清洁水体指示生物,Hydrocassis和Ancyronyx属是重污染水体指示生物.图3表4参16     

Bayesian spatial prediction

This paper presents a complete Bayesian methodology for analyzing spatial data, one which employs proper priors and features diagnostic methods in the Bayesian spatial setting. The spatial covariance structure is modeled using a rich class of covariance functions for Gaussian random fields. A general class of priors for trend, scale, and structural covariance parameters is considered. In particular, we obtain analytic results that allow easy computation of the predictive distribution for an arbitrary prior on the parameters of the covariance function using importance sampling. The computations, as well as model diagnostics and sensitivity analysis, are illustrated with a set of precipitation data.     

Local multiplicity adjustments for spatial cluster detection

The spatial scan statistic is a widely applied tool for cluster detection. The spatial scan statistic evaluates the significance of a series of potential circular clusters using Monte Carlo simulation to account for the multiplicity of comparisons. In most settings, the extent of the multiplicity problem varies across the study region. For example, urban areas typically have many overlapping clusters, while rural areas have few. The spatial scan statistic does not account for these local variations in the multiplicity problem. We propose two new spatially-varying multiplicity adjustments for spatial cluster detection, one based on a nested Bonferroni adjustment and one based on local averaging. Geographic variations in power for the spatial scan statistic and the two new statistics are explored through simulation studies, and the methods are applied to both the well-known New York leukemia data and data from a case–control study of breast cancer in Wisconsin.     

Population structure of humpback whales in the western and central South Pacific Ocean as determined by vocal exchange among populations

For cetaceans, population structure is traditionally determined by molecular genetics or photographically identified individuals. Acoustic data, however, has provided information on movement and population structure with less effort and cost than traditional methods in an array of taxa. Male humpback whales (Megaptera novaeangliae) produce a continually evolving vocal sexual display, or song, that is similar among all males in a population. The rapid cultural transmission (the transfer of information or behavior between conspecifics through social learning) of different versions of this display between distinct but interconnected populations in the western and central South Pacific region presents a unique way to investigate population structure based on the movement dynamics of a song (acoustic) display. Using 11 years of data, we investigated an acoustically based population structure for the region by comparing stereotyped song sequences among populations and years. We used the Levenshtein distance technique to group previously defined populations into (vocally based) clusters based on the overall similarity of their song display in space and time. We identified the following distinct vocal clusters: western cluster, 1 population off eastern Australia; central cluster, populations around New Caledonia, Tonga, and American Samoa; and eastern region, either a single cluster or 2 clusters, one around the Cook Islands and the other off French Polynesia. These results are consistent with the hypothesis that each breeding aggregation represents a distinct population (each occupied a single, terminal node) in a metapopulation, similar to the current understanding of population structure based on genetic and photo‐identification studies. However, the central vocal cluster had higher levels of song‐sharing among populations than the other clusters, indicating that levels of vocal connectivity varied within the region. Our results demonstrate the utility and value of using culturally transmitted vocal patterns as a way of defining connectivity to infer population structure. We suggest vocal patterns be incorporated by the International Whaling Commission in conjunction with traditional methods in the assessment of structure.     

Geographic assignment of seabirds to their origin: combining morphologic, genetic, and biogeochemical analyses.

Longline fisheries, oil spills, and offshore wind farms are some of the major threats increasing seabird mortality at sea, but the impact of these threats on specific populations has been difficult to determine so far. We tested the use of molecular markers, morphometric measures, and stable isotope (delta15N and delta13C) and trace element concentrations in the first primary feather (grown at the end of the breeding period) to assign the geographic origin of Calonectris shearwaters. Overall, we sampled birds from three taxa: 13 Mediterranean Cory's Shearwater (Calonectris diomedea diomedea) breeding sites, 10 Atlantic Cory's Shearwater (Calonectris diomedea borealis) breeding sites, and one Cape Verde Shearwater (C. edwardsii) breeding site. Assignment rates were investigated at three spatial scales: breeding colony, breeding archipelago, and taxa levels. Genetic analyses based on the mitochondrial control region (198 birds from 21 breeding colonies) correctly assigned 100% of birds to the three main taxa but failed in detecting geographic structuring at lower scales. Discriminant analyses based on trace elements composition achieved the best rate of correct assignment to colony (77.5%). Body measurements or stable isotopes mainly succeeded in assigning individuals among taxa (87.9% and 89.9%, respectively) but failed at the colony level (27.1% and 38.0%, respectively). Combining all three approaches (morphometrics, isotopes, and trace elements on 186 birds from 15 breeding colonies) substantially improved correct classifications (86.0%, 90.7%, and 100% among colonies, archipelagos, and taxa, respectively). Validations using two independent data sets and jackknife cross-validation confirmed the robustness of the combined approach in the colony assignment (62.5%, 58.8%, and 69.8% for each validation test, respectively). A preliminary application of the discriminant model based on stable isotope delta15N and delta13C values and trace elements (219 birds from 17 breeding sites) showed that 41 Cory's Shearwaters caught by western Mediterranean long-liners came mainly from breeding colonies in Menorca (48.8%), Ibiza (14.6%), and Crete (31.7%). Our findings show that combining analyses of trace elements and stable isotopes on feathers can achieve high rates of correct geographic assignment of birds in the marine environment, opening new prospects for the study of seabird mortality at sea.     

Biological benthic tools as indicators of coastal marine ecosystems health

Coastal marine ecosystems are increasingly subjected to environmental stress and degradation due to pollution. Several research programmes have addressed this problem and produced relevant data sets for specific areas, often including consistent sets of environmental and biological variables. The value of existing information gathered from these types of data can be largely increased by combining them into a common data set to determine globally applicable relationships. To perform this exercise, the Intergovernmental Oceanographic Commission (IOC) of UNESCO has recently formed the Ad hoc Study Group on Benthic Indicators (http://www.ioc.unesco.org/benthicindicators) with the aim of developing robust indicators of benthic health. In this paper, initial products and ongoing activities of this international initiative are described and discussed. An expansion of initial IOC/UNESCO research on benthic fauna-organic carbon relationships is also presented. As part of this follow-up research, the relationship between total organic carbon concentrations of sediment and abundance, biomass and species diversity of benthic macrofauna was evaluated using data sets from 2 different regions of the world comprising 3 different coastal marine environments. The ability of identifying threshold levels in selected variables that could serve as indicators of related adverse environmental conditions leading to stress in the benthos is envisaged within the frame of a larger joint analysis, carried out by the IOC/UNESCO Study Group on Benthic Indicators, of merged data sets from several coastal regions worldwide.     

Power evaluation of focused cluster tests

Many statistical tests have been developed to assess the significance of clusters of disease located around known sources of environmental contaminants, also known as focused disease clusters. The majority of focused-cluster tests were designed to detect a particular spatial pattern of clustering, one in which the disease cluster centers around the pollution source and declines in a radial fashion with distance. However, other spatial patterns of environmentally related disease clusters are likely given that the spatial dispersion patterns of environmental contaminants, and thus human exposure, depend on a number of factors (i.e., meteorology and topography). For this study, data were simulated with five different spatial patterns of disease clusters, reflecting potential pollutant dispersion scenarios: (1) a radial effect decreasing with increasing distance, (2) a radial effect with a defined peak and decreasing with distance, (3) a simple angular effect, (4) an angular effect decreasing with increasing distance and (5) an angular effect with a defined peak and decreasing with distance. The power to detect each type of spatially distributed disease cluster was evaluated using Stone’s Maximum Likelihood Ratio Test, Tango’s Focused Test, Bithell’s Linear Risk Score Test, and variations of the Lawson–Waller Score Test. Study findings underscore the importance of considering environmental contaminant dispersion patterns, particularly directional effects, with respect to focused-cluster test selection in cluster investigations. The effect of extra variation in risk also is considered, although its effect is not substantial in terms of the power of tests.     

Biological benthic tools as indicators of coastal marine ecosystems health

Coastal marine ecosystems are increasingly subjected to environmental stress and degradation due to pollution. Several research programmes have addressed this problem and produced relevant data sets for specific areas, often including consistent sets of environmental and biological variables. The value of existing information gathered from these types of data can be largely increased by combining them into a common data set to determine globally applicable relationships. To perform this exercise, the Intergovernmental Oceanographic Commission (IOC) of UNESCO has recently formed the Ad hoc Study Group on Benthic Indicators (http://www.ioc.unesco.org/benthicindicators) with the aim of developing robust indicators of benthic health. In this paper, initial products and ongoing activities of this international initiative are described and discussed. An expansion of initial IOC/UNESCO research on benthic fauna-organic carbon relationships is also presented. As part of this follow-up research, the relationship between total organic carbon concentrations of sediment and abundance, biomass and species diversity of benthic macrofauna was evaluated using data sets from 2 different regions of the world comprising 3 different coastal marine environments. The ability of identifying threshold levels in selected variables that could serve as indicators of related adverse environmental conditions leading to stress in the benthos is envisaged within the frame of a larger joint analysis, carried out by the IOC/UNESCO Study Group on Benthic Indicators, of merged data sets from several coastal regions worldwide.     

Radial basis function networks with partially classified data

The problem of estimating a classification rule with partially classified observations, which often occurs in biological and ecological modelling, and which is of major interest in pattern recognition, is discussed. Radial basis function networks for classification problems are presented and compared with the discriminant analysis with partially classified data, in situations where some observations in the training set are unclassified. An application on a set of morphometric data obtained from the skulls of 288 specimens of Microtus subterraneus and Microtus multiplex is performed. This example illustrates how the use of both classified and unclassified observations in the estimate of the hidden layer parameters has the potential to greatly improve the network performances.     

Prioritization of coastal properties for conservation in New York State

Conservation of coastal lands reduces non-point source pollution loads into oceans and estuaries, retains natural areas and saves ecological communities from disappearance and change. A recent agreement for protection of Long Island Sound waters in New York and Connecticut established 30 environmental and management goals. One of them is establishment of a listing of existing undeveloped properties and their prioritization for natural resource conservation and outdoor recreation. The optimal prioritization approach poses strong constraints and methodological challenges on selection of data for analysis, assignment of a priority score to each property unit and the assessment of this assignment. To be a practical tool, the prioritization model should be reproducible and include a mechanism for evaluation of obtained prioritization scenarios. Presented study uses Geographic Information System (GIS) to assign conservation priority scores to unprotected and undeveloped parcels greater than five acres in size within New York’s Long Island Sound coastal area. The method combines spatial multi-criteria analysis and statistical methods. The results of this project include identification and prioritization of more than 700 undeveloped properties on New York coast. The most important finding of GIS analysis was the discovery of clusters of vacant parcels that together form large areas available for future conservation. These results offer new conservation tools and strategies to coastal managers and government in New York State.     

Réponse d'une communauté de diatomées de glace à un gradient de salinité (baie d'Hudson)

Sea ice offers a physical support to the growth of microalgae (epontic community). Almost all the studies on ice microflora in the Arctic and the Antarctic have been performed in waters of high salinities, and they generally reported a very high standing crop. From February through May 1978, 15 stations were sampled in the southeastern part of Hudson Bay (Manitounuk Sound), Quebec, Canada. The peculiar physical condition of the water bodies establishes a salinity gradient in the underlying waters from the mouth of the Great Whale River to the upper reaches of Manitounuk Sound, leading to a parallel gradient in the ice dynamics. The main goal of this study was to examine the effect of these gradients on the biomass and the taxonomic composition of the epontic community. Thus, there are two distinct gradients in the ice, one ruled by the surface-water salinity gradient controlling the ice thickness and the settlement efficiency, which triggers the biomass of the epontic community (higher standing crop in the upper reaches of Manitounuk Sound). In addition, the ice salinity gradient influences the taxonomic composition (lower number of microalgae taxa toward the mouth of the Great Whale River). Salinity of the underlying surface waters, following discriminant analyses of species-based clusters of observations, emerges as a major environmental variable controlling the distribution and abundance of species. In addition to adequate light intensity and nutrient supply, salinity of the underlying waters is therefore another limiting factor to the settlement and growth of an ice microflora. This factor is of great potential significance in coastal and esturine glacial waters.     

Neutron activation and atomic absorption analysis of Ulva Lactuca and Gracilaria Verrucosa from Thermaikos Gulf,Greece

NAA and AAS have been applied to determine the content of As, Br, Ca, Cd, Co, Cr, Cu, Fe, K, La, Mn, Na, Ni, Pb, Sb, Sc, Sm and Zn in Ulva lactuca and Gracilaria verrucosa from Thermaikos Gulf, Greece. The analytical data has been subjected to cluster and stepwise discriminant analysis. Thus the influence of some industrial sources on the pollution of water in the Gulf is detected and correlation between Zn‐Co‐Cr‐La; Br‐K; Cu‐Ni; Cd‐Sb‐Mn in the investigated algae is established.