Two-compartment model of radionuclide accumulation into marine organisms. I. Accumulation from a medium of constant activity

The interpretation of kinetics of radionuclide accumulation into biological organisms can be performed by using the well-known multicompartment models. The application of a two-compartment model in the interpretation of radionuclide accumulation into marine organisms, when this does not markedly deplete the medium, is considered. It has been found that most of the loss experiments cannot be interpreted without the use of uptake data. The agreement between the uptake and loss parameters is discussed. Explicit expressions for different kinds of two-compartment models are evaluated. The interpretation of irreversible and other special cases is proposed.     

Estimating age from recapture data: integrating incremental growth measures with ancillary data to infer age-at-length

Estimating the age of individuals in wild populations can be of fundamental importance for answering ecological questions, modeling population demographics, and managing exploited or threatened species. Significant effort has been devoted to determining age through the use of growth annuli, secondary physical characteristics related to age, and growth models. Many species, however, either do not exhibit physical characteristics useful for independent age validation or are too rare to justify sacrificing a large number of individuals to establish the relationship between size and age. Length-at-age models are well represented in the fisheries and other wildlife management literature. Many of these models overlook variation in growth rates of individuals and consider growth parameters as population parameters. More recent models have taken advantage of hierarchical structuring of parameters and Bayesian inference methods to allow for variation among individuals as functions of environmental covariates or individual-specific random effects. Here, we describe hierarchical models in which growth curves vary as individual-specific stochastic processes, and we show how these models can be fit using capture-recapture data for animals of unknown age along with data for animals of known age. We combine these independent data sources in a Bayesian analysis, distinguishing natural variation (among and within individuals) from measurement error. We illustrate using data for African dwarf crocodiles, comparing von Bertalanffy and logistic growth models. The analysis provides the means of predicting crocodile age, given a single measurement of head length. The von Bertalanffy was much better supported than the logistic growth model and predicted that dwarf crocodiles grow from 19.4 cm total length at birth to 32.9 cm in the first year and 45.3 cm by the end of their second year. Based on the minimum size of females observed with hatchlings, reproductive maturity was estimated to be at nine years. These size benchmarks are believed to represent thresholds for important demographic parameters; improved estimates of age, therefore, will increase the precision of population projection models. The modeling approach that we present can be applied to other species and offers significant advantages when multiple sources of data are available and traditional aging techniques are not practical.     

Modeling and optimization of Nile blue sulfate mineralization by heterogeneous fenton oxidation

Artificial neural network and response surface methodology have been used to develop a model for simulation and optimization of the removal of Nile blue sulfate by heterogeneous Fenton oxidation. Experimental data were used to train an artificial neural network model with linear transfer function at the output layer and a tangent sigmoid transfer function at the hidden layer. A Box–Behnken design was employed to assess the effects of input process parameters on the total organic carbon removal. First order kinetics and lumped kinetics models were used to describe the reaction; a high regression coefficient indicated that the latter fitted best. The formation of non-oxidizable compounds was shown by liquid chromatography–mass spectrometry.     

Development of Lake Ladoga ecosystem models: modeling of the phytoplankton succession in the eutrophication process. I

New models of Lake Ladoga ecosystem and the results of modeling are presented. In the first part the model of phytoplankton succession in the process of anthropogenic eutrophication of the lake is considered under the evolution of the phosphorus loading. The still continued anthropogenic eutrophication of the lake started in 1962 when the phosphorus load began to increase. Since 1962 during the evolution of the lake’s state from oligotrophic to developed mezotrophic one, the structure of phytoplankton community dominating species was significantly changed as well as its total productivity. The system state in the model is described by 14 parameters: nine phytoplankton complexes, zooplankton, dissolved organic matter, detritus, dissolved mineral phosphorus and dissolved oxygen. The number of parameters of this model is noticeably larger than that of previous models created by the authors. The relative dynamics of phytoplankton complexes in the lake’s ecosystem evolution was simulated by the new model. It is shown that the modeling results are adequately corresponding to the observation data. The results of phytoplankton structure modeling allow to estimate the impact of phytoplankton on the water quality as well as give the prediction of the lake’s ecosystem evolution with the changes of the phosphorus loading.     

Data-directed modelling of Daphnia dynamics in a long-term micro-ecosystem experiment

The micro-ecosystem under consideration consists of three compartments forming a closed chain in which water circulates. Three trophic levels are represented in different compartments: autotrophs (algae, mainly Chlorella vulgaris), herbivores (Daphnia magna) and microbial decomposers. From a 20 years experiment with this system, data has been selected for this study. The dynamics of algae and Daphnia magna in only one of the compartments were modeled by different systems of differential and difference equations. We describe the successive steps in the process of model development, and the fitting of parameters using a Nelder-Mead simplex calibration method. Identification problems were overcome by taking values for physiological parameters in agreement with the literature. It turned out that a logistic type of model gives the best result for the structured Daphnia population because of the set up of the experiment: algae grow and reproduce in the upstream compartment. For this reason well-known plant–herbivore models did not comply with the data. The results of the parameter estimation procedure are discussed. The estimated grazing rate by Daphnia was smaller than expected. Possibly the Daphnia fed also on detritus and decomposing algae which were not measured.     

雷州半岛旱地砖红壤非点源氮、磷淋溶损失模拟研究

大量施用化学肥料所引起的养分淋失和环境污染正逐渐受到人们的重视。通过土柱模拟氮、磷素养分淋溶试验．研究了不同施肥处理下砖红壤氮、磷素淋溶损失特点。试验结果表明，随着尿素用量的增加，渗漏水中NH4^＋-N质量浓度和TN淋失量也相应增加，而NO3^--N的质量浓度变化幅度较大，渗漏水中氮素淋失形态主要是以NO3^--N为主。由于土壤磷素很难移动，所以TP累积淋失量非常少，氮、磷肥混施对非点源氮、磷素的淋失都有影响，其中过量磷肥配合尿素施用可以增加土壤中NH4^＋-N、NO3^- -N、TN的淋失程度。     

Point patterns of forest fire locations

In this paper I demonstrate some of the techniques for the analysis of spatial point patterns that have become available due to recent developments in point process modelling software. These developments permit convenient exploratory data analysis, model fitting, and model assessment. Efficient model fitting, in particular, makes possible the testing of statistical hypotheses of genuine interest, even when interaction between points is present, via Monte Carlo methods. The discussion of these techniques is conducted jointly with and in the context of some preliminary analyses of a collection of data sets which are of considerable interest in their own right. These data sets (which were kindly provided to me by the New Brunswick Department of Natural Resources) consist of the complete records of wildfires which occurred in New Brunswick during the years 1987 through 2003. In treating these data sets I deal with data-cleaning problems, methods of exploratory data analysis, means of detecting interaction, fitting of statistical models, and residual analysis and diagnostics. In addition to demonstrating modelling techniques, I include a discussion on the nature of statistical models for point patterns. This is given with a view to providing an understanding of why, in particular, the Strauss model fails as a model for interpoint attraction and how it has been modified to overcome this difficulty. All actual modelling of the New Brunswick fire data is done only with the intent of illustrating techniques. No substantive conclusions are or can be drawn at this stage. Realistic modelling of these data sets would require incorporation of covariate information which I do not so far have available.

Using stochastic volatility models to analyse weekly ozone averages in Mexico City

In this paper we make use of some stochastic volatility models to analyse the behaviour of a weekly ozone average measurements series. The models considered here have been used previously in problems related to financial time series. Two models are considered and their parameters are estimated using a Bayesian approach based on Markov chain Monte Carlo (MCMC) methods. Both models are applied to the data provided by the monitoring network of the Metropolitan Area of Mexico City. The selection of the best model for that specific data set is performed using the Deviance Information Criterion and the Conditional Predictive Ordinate method.     

Selecting parameters for calibration via sensitivity analysis: An individual-based model of mosquitofish population dynamics

A stochastic individual-based model (IBM) of mosquitofish population dynamics in experimental ponds was constructed in order to increase, virtually, the number of replicates of control populations in an ecotoxicology trial, and thus to increase the statistical power of the experiments. In this context, great importance had to be paid to model calibration as this conditions the use of the model as a reference for statistical comparisons. Accordingly, model calibration required that both mean behaviour and variability behaviour of the model were in accordance with real data. Currently, identifying parameter values from observed data is still an open issue for IBMs, especially when the parameter space is large. Our model included 41 parameters: 30 driving the model expectancy and 11 driving the model variability. Under these conditions, the use of “Latin hypercube” sampling would most probably have “missed” some important combinations of parameter values. Therefore, complete factorial design was preferred. Unfortunately, due to the constraints of the computational capacity, cost-acceptable “complete designs” were limited to no more than nine parameters, the calibration question becoming a parameter selection question. In this study, successive “complete designs” were conducted with different sets of parameters and different parameter values, in order to progressively narrow the parameter space. For each “complete design”, the selection of a maximum of nine parameters and their respective n values was carefully guided by sensitivity analysis. Sensitivity analysis was decisive in selecting parameters that were both influential and likely to have strong interactions. According to this strategy, the model of mosquitofish population dynamics was calibrated on real data from two different years of experiments, and validated on real data from another independent year. This model includes two categories of agents; fish and their living environment. Fish agents have four main processes: growth, survival, puberty and reproduction. The outputs of the model are the length frequency distribution of the population and the 16 scalar variables describing the fish populations. In this study, the length frequency distribution was parameterized by 10 scalars in order to be able to perform calibration. The recently suggested notion of “probabilistic distribution of the distributions” was also applied to our case study, and was shown to be very promising for comparing length frequency distributions (as such).     

Residential radon concentration in the Abruzzo region (Italy): a different perspective for identifying radon prone areas

Indoor radon is an important risk factor for human health. Indeed radon inhalation is considered the second cause of lung cancer after smoking. During the last decades, in many countries huge efforts have been made in order to measuring, mapping and predicting radon levels in dwellings. Various researches have been devoted to identify those areas within the country where high radon concentrations are more likely to be found. Data collected through indoor radon surveys have been analysed adopting various statistical approaches, among which hierarchical Bayesian models and geostatistical tools are worth noting. The essential goal of this paper regards the identification of high radon concentration areas (the so-called radon prone areas) in the Abruzzo Region (Italy). In order to accurately pinpoint zones deserving attention for mitigation purpose, we adopt spatial cluster detection techniques, traditionally employed in epidemiology. As a first step, we assume that indoor radon measurements do not arise from a continuous spatial process; thus the geographic locations of dwellings where the radon measurements have been taken can be viewed as a realization of a spatial point process. Following this perspective, we adopt and compare recent cluster detection techniques: the simulated annealing scan statistic, the case event approach based on distance regression on the selection order and the elliptic spatial scan statistic. The analysis includes data collected during surveys carried out by the Regional Agency for the Environment Protection of Abruzzo (ARTA) in 1,861 random sampled dwellings across 277 municipalities of the Abruzzo region. The radon prone areas detected by the selected approaches are provided along with the summary statistics of the methods. Finally, the methodologies considered in this paper are tested on simulated data in order to evaluate their power and the precision of cluster location detection.     

Simulation of dam/levee-break hydrodynamics with a three-dimensional implicit unstructured-mesh finite element model

Dam failures usually cause huge economic and life losses , especially in urban areas where there is a high concentration of inhabitants and economic actors. In order to understand the physical mechanisms of the formation and development of dam-break flooding, lots of efforts have been put into different types of modelling techniques. However, most of existing models are 1D (one-dimensional) or 2D models based on the shallow water equations. In this paper, we present a 3D numerical modelling investigation of dam-break flow hydrodynamics in an open L-shape channel. A newly developed 3D unstructured mesh finite element model is used here. An absorption-like term is introduced to the Navier–Stokes equations in order to control the conditioning of the matrix equation in the numerical solution process and thus improve the stability. A wetting and drying algorithm is used here to allow the free surface height to be treated with a high level of implicitness and stability. The 3D model has been validated by comparing the results with the published experimental data. Good agreement has been achieved at six selected locations. This study shows that the 3D unstructured mesh model is capable of capturing the 3D hydraulic aspects and complicated local flows around structures in simulation of dam-break flows.     

Selecting a binary Markov model for a precipitation process

This paper uses rth-order categorical Markov chains to model the probability of precipitation. Several stationary and non-stationary high-order Markov models are proposed and compared using BIC. The number of parameters increases exponentially by adding the Markov order. Several classes of high-order Markov models are proposed which their increase of number of parameters are modest. For example models that use the number of precipitation days in a period prior to date, temperature of the previous day and sines/cosines periodic functions (to model the seasonality) are considered. The theory of partial likelihood is used to estimate the parameters. Parsimonious non-stationary first order Markov models with few seasonal terms are found optimal using BIC and temperature does not turn out to be a useful covariate. However BIC seems to underestimate the number of seasonal terms. We have also compared the results with AIC in some cases which tends to pick parsimonious models with more seasonal terms and higher order. We also show that ignoring seasonal terms result in picking higher order Markov chains. Finally we apply the methods to build confidence intervals for the probability of periods with no precipitation or low number of precipitation days in Calgary using historical data from 1980 to 2000.     

Some stochastic differential equation models of an aquatic ecosystem

A set of stochastic differential equations has been used to model an aquatic ecosystem. The randomness in the system has been introduced through initial conditions of the state variables, parameters, and input variables (light and temperature). These models were analysed using Monte Carlo simulation procedures and the results were similar to those observed in the experimental and field data. They were different, however, from the results of a deterministic simulation. This approach allows us to incorporate the maximum degree of information in the model and to study the behavior of the system without arbitrarily manipulating the values of the parameters. Some possible refinements and generalizations of this approach are also discussed.     

Use of ambiguous detections to improve estimates from species distribution models

As large carnivores recover throughout Europe, their distribution needs to be studied to determine their conservation status and assess the potential for human-carnivore conflicts. However, efficient monitoring of many large carnivore species is challenging due to their rarity, elusive behavior, and large home ranges. Their monitoring can include opportunistic sightings from citizens in addition to designed surveys. Two types of detection errors may occur in such monitoring schemes: false negatives and false positives. False-negative detections can be accounted for in species distribution models (SDMs) that deal with imperfect detection. False-positive detections, due to species misidentification, have rarely been accounted for in SDMs. Generally, researchers use ad hoc data-filtering methods to discard ambiguous observations prior to analysis. These practices may discard valuable ecological information on the distribution of a species. We investigated the costs and benefits of including data types that may include false positives rather than discarding them for SDMs of large carnivores. We used a dynamic occupancy model that simultaneously accounts for false negatives and positives to jointly analyze data that included both unambiguous detections and ambiguous detections. We used simulations to compare the performances of our model with a model fitted on unambiguous data only. We tested the 2 models in 4 scenarios in which parameters that control false-positive detections and true detections varied. We applied our model to data from the monitoring of the Eurasian lynx (Lynx lynx) in the European Alps. The addition of ambiguous detections increased the precision of parameter estimates. For the Eurasian lynx, incorporating ambiguous detections produced more precise estimates of the ecological parameters and revealed additional occupied sites in areas where the species is likely expanding. Overall, we found that ambiguous data should be considered when studying the distribution of large carnivores through the use of dynamic occupancy models that account for misidentification.     

南京地区PCB52多介质迁移归趋行为模拟及环境风险评价

运用多介质逸度模型对PCB52在南京地区空气、水体、土壤和沉积物中的浓度分布与多介质间的迁移、归趋行为进行模拟研究并分析PCB52在研究区域环境多介质间的迁移通量,确定其在环境中的主要迁移过程;结合文献中的监测数据,对模型的可靠性进行验证;对模型的输入参数进行灵敏度分析;通过模拟浓度与实际浓度的对比,表明模型在该地区具有很好的适用性。结果表明,环境系统达到平衡时,PCB52在沉积物中的含量占其在环境系统总含量的96.12%;PCB52从环境系统中的消失途径主要为空气平流输出和空气降解;环境温度和空气平流输入是影响化合物在环境相中浓度分布的最主要因素。此外,PCB52初步的生态风险评价表明其对生态环境未构成严重威胁,但潜在危害仍不容忽视。     

Population level risk assessment: practical considerations for evaluation of population models from a risk assessor's perspective

Population models are increasingly being considered as a tool for pesticide risk assessment in order to evaluate how potential effects act on the population level and population recovery. While the importance and difficulties of such models have been discussed by various authors during the past decade, mainly with a focus on how to describe or develop such models, several biological and methodological aspects have never been addressed so far, which are relevant for the application of models in risk assessment. These include a critical review of our knowledge of a species, the use of field data by taking methodological constraints into account, how to include uncertainty in model validation or how to measure effects. Although these aspects will be critical for the acceptance of population models by authorities, most of them apply not only to population models, but also to standard risk assessment. In the present article, we give practical recommendations for addressing these questions in population level risk assessments.     

表面流人工湿地中氮磷的去除机理

人工湿地作为一种高效、低耗的污水处理新工艺已被广泛接受，特别是其在脱氮、除磷方面的应用逐步为人们所重视。本文深入地讨论了表面流人工湿地中各种生物、物理、化学过程对污水中各种形态含氮、含磷化合物的去除机理，及其具体途径、相关反应和反应类型，总结了国内外对各个过程影响因素、控制条件、反应速度、去除能力及相互之间协调拮抗作用的研究结果。虽然硝化／反硝化作用和土壤吸附沉淀作用已被公认为是表面流人工湿地脱氮、除磷的主要途径，但不同研究结果之间仍存在着明显差异，鲜有多介质环境条件下各种脱氮、除磷过程中多种氮、磷形态的质量平衡研究，而以此为基础的人工湿地生态动力学模型的研究则是深入了解人工湿地运行机理、设计和预测其处理效率，以及推动人工湿地污水处理工艺广泛应用的关键。