Copper distribution in chemical soil fractions and relationships with maize crop yield

Agricultural practices can lead to copper accumulation in soils and at high concentration it can become toxic for plants. One common toxic effect of copper on plants is a decrease of crop yield. Here, we studied 1) the crop yield of maize grown on plots of a soil intentionally enriched with copper sulphate and 2) the possible relationship between the copper concentration in chemical soil fractions and the maize crop yield. Anthropogenic copper is mainly bound to manganese oxides, to iron oxides and to the organic matter. Maize (Zea maize L.) was grown on outdoor experimental plots. The crop yield was evaluated for three development stages: the 6–10 leaf stage, the female flowering stage and the maturity stage, 2, 4 and 6 years after the soil copper enrichment. Strong crop yield reductions, proving a toxic effect of copper on maize growth were noted 2 years after the copper input at the maturity stage and 4 years after the copper input at the 6–10 leaf stage. Variations in maize crop yield are described with linear multiple regression equations including the variable copper content in soil, and other variables when needed such as soil pH, soil organic carbon level and the climatic variables, the precipitation rate and the ambient temperature. The crop yield study at the 6–10 leaf stage and at the female flowering stage does not provide significant regression equations, while the crop yield study at the maturity stage does. Request variables for the models are the total copper content or the copper bound to the organic matter and the meteorological data. Electronic Publication     

Extinction Rate Estimates for Plant Populations in Revisitation Studies: Importance of Detectability

Abstract:  Many researchers have obtained extinction-rate estimates for plant populations by comparing historical and current records of occurrence. A population that is no longer found is assumed to have gone extinct. Extinction can then be related to characteristics of these populations, such as habitat type, size, or species, to test ideas about what factors may affect extinction. Such studies neglect the fact that a population may be overlooked, however, which may bias estimates of extinction rates upward. In addition, if populations are unequally detectable across groups to be compared, such as habitat type or population size, comparisons become distorted to an unknown degree. To illustrate the problem, I simulated two data sets, assuming a constant extinction rate, in which populations occurred in different habitats or habitats of different size and these factors affected their detectability. The conventional analysis implicitly assumed that detectability equalled 1 and used logistic regression to estimate extinction rates. It wrongly identified habitat and population size as factors affecting extinction risk. In contrast, with capture-recapture methods, unbiased estimates of extinction rates were recovered. I argue that capture-recapture methods should be considered more often in estimations of demographic parameters in plant populations and communities.     

沈阳市垃圾填埋处理甲烷产量研究

分析了4种典型的生活垃圾填埋处理甲烷产气量模型：IPCC模型、化学计量式模型、COD模型和生物降解理论模型;利用这4种模型估算沈阳市生活垃圾的甲烷产气总量,对计算结果的差异进行比较讨论;分析了4种模型的优缺点及其不同的适用性;根据沈阳市城市生活垃圾的特点,建议采用生物降解理论模型来估算沈阳市垃圾填埋甲烷产气量。     

Testing the importance of accurate meteorological input fields and parameterizations in atmospheric transport modelling using dream - validation against ETEX-1

A tracer model, the DREAM, which is based on a combination of a near-range Lagrangian model and a long-range Eulerian model, has been developed. The meteorological meso-scale model, MM5V1, is implemented as a meteorological driver for the tracer model. The model system is used for studying transport and dispersion of air pollutants caused by a single but strong source as, e.g. an accidental release from a nuclear power plant. The model system including the coupling of the Lagrangian model with the Eulerian model are described. Various simple and comprehensive parameterizations of the mixing height, the vertical dispersion, and different meterological input data have been implemented in the combined tracer model, and the model results have been validated against measurements from the ETEX-1 release. Several different statistical parameters have been used to estimate the differences between the parameterizations and meterological input data in order to find the best performing solution.     

CONTINUOUS SIMULATION OF RECEIVING WATER QUALITY TRANSIENTS1

: This paper presents solutions to the one-dimensional, transient conservation of mass equations for the coupled biochemical oxygen demand-dissolved oxygen (BOD-DO) reactions, based on the principle of superposition, for continuously discharging plane sources. The solutions are applied within the framework of a continuous simulation model to allow the derivation of water quality frequency curves and frequency histograms of consecutive hourly dissolved oxygen violations, for any desired standard. Receiving water response is determined for waste inputs from urban wet weather, dry weather, and upstream sources. An application to Des Moines, Iowa, and Des Moines River indicated that urban storm water impacts on the stream can be masked in the cumulative frequency curve representation, but the benefits of storm water control are clearly shown in frequency histograms of the duration of consecutive stream standard violations.     

Roads and Landscape Pattern in Northern Wisconsin Based on a Comparison of Four Road Data Sources

Abstract:  Roads are important components of landscapes; they fragment habitat, facilitate invasive species spread, alter hydrology, and influence patterns of land use. Previous research on the ecological impacts of roads may have underestimated their effect because currently available sources of road data do not include the full road network. We compared differences in road density and landscape pattern among U.S. Census Bureau TIGER line files, U.S. Geological Survey 1:100,000-scale digital line graphs, and U.S. Geological Survey 1:24,000-scale digital raster graphics in northern Wisconsin to road data derived from 1:40,000-scale digital orthophotos. Road density measured from digital orthophotos (2.82 km/km²) was significantly greater than that of digital raster graphics (1.62 km/km²) and more than double that of digital line graphs (1.21 km/km²) and TIGER (1.27 km/km²) data. The increased road densities in raster graphics and orthophoto data were mainly due to the addition of minor roads. When all roads were used to define patch boundaries, landscape metrics produced with orthophoto data showed significantly greater levels of fragmentation than those based on line or raster graphics. For example, maximum patch size was 1074 ha and total edge was 109 km for line graphs, compared with 686 ha and 211 km for orthophoto data. Roads are missing in commonly used data, primarily because mapping standards systematically exclude minor roads. These standards are not ecologically based and may result in false assumptions about the ecological effects of roads. We recommend that future studies take special consideration of the completeness of road data and consider whether all ecologically relevant roads are included.     

Developing an empirical model of phytoplankton primary production: a neural network case study

We describe the development of a neural network model for estimating primary production of phytoplankton. Data from an enriched estuary in the eastern United States, Chesapeake Bay, were used to train, validate and test the model. Two error backpropagation multilayer perceptrons were trained: a simpler one (3-5-1) and a more complex one (12-5-1). Both neural networks outperformed conventional empirical models, even though only the latter, which exploits a larger suite of predictive variables, provided truly accurate outputs. The application of this neural network model is thoroughly discussed and the results of a sensitivity analysis are also presented.     

Multistate models for monitoring individual trees in permanent observation plots

This interdisciplinary research on forest ecosystems begins with some characteristics of ecosystems which are the basis for the derivation of statistical models for the development and vitality of trees. Several ecological problems which could be solved by longitudinal studies are mentioned. Statistical methods for the evaluation of the crowns of spruce trees (Picea abies Karst) in three permanent observation plots in Switzerland are described. In particular, the time-dependent proportional odds model and a transitional model are used. Through application of these multistate models the data give information on the dependence of an ordered categorical response variable on covariates characterizing the ecosystem. The response variable is observed through infrared aerial photographs. This monitoring system gives insight into the dynamic behaviour of the forest ecosystem. The need for more eco-systematically motivated statistical research using longitudinal studies is identified.     

COMPARISON OF REGRESSION AND TIME-SERIES METHODS FOR SYNTHESIZING MISSING STREAMFLOW RECORDS1

ABSTRACT: Regression and time-series techniques have been used to synthesize and predict the stream flow at the Foresta Bridge gage from information at the upstream Pohono Bridge gage on the Merced River near Yosemite National Park. Using the available data from two time periods (calendar year 1979 and water year 1986), we evaluated the two techniques in their ability to model the variation in the observed flows and in their ability to predict stream flow at the Foresta Bridge gage for the 1979 time period with data from the 1986 time period. Both techniques produced reasonably good estimates and forecasts of the flow at the downstream gage. However, the regression model was found to have a significant amount of autocorrelation in the residuals, which the time-series model was able to eliminate. The time-series technique presented can be of great assistance in arriving at reasonable estimates of flow in data sets that have large missing portions of data.