A new baseline model for estimating willingness to pay from discrete choice models

We show a substantive problem exists with the widely-used ratio of coefficients approach to calculating willingness to pay (WTP) from discrete choice models. The correctly calculated standard error for WTP using this approach is shown to be undefined. This occurs because the cost parameter's standard error implies some possibility the true parameter value is arbitrarily close to zero. We propose a simple yet elegant way to overcome this problem by reparameterizing the (negative) cost variable's coefficient using an exponential transformation to enforce the theoretically correct positive coefficient. With it the confidence interval for WTP is now finite and well behaved.     

A hierarchical Bayesian non-linear spatio-temporal model for the spread of invasive species with application to the Eurasian Collared-Dove

The spread of invasive species is a long studied subject that garners much interest in the ecological research community. Historically the phenomenon has been approached using a purely deterministic mathematical framework (usually involving differential equations of some form). These methods, while scientifically meaningful, are generally highly simplified and fail to account for uncertainty in the data and process, of which our knowledge could not possibly exist without error. We propose a hierarchical Bayesian model for population spread that accommodates data sources with errors, dependence structures between population dynamics parameters, and takes into account prior scientific understanding via non-linear relationships between model parameters and space-time response variables. We model the process (i.e., the bird population in this case) as a Poisson response with spatially varying diffusion coefficients as well as a logistic population growth term using a common reaction-diffusion equation that realistically mimics the ecological process. We focus the application on the ongoing invasion of the Eurasian Collared-Dove.     

Spatial modeling and prediction under stationary non-geometric range anisotropy

For modeling spatial processes, we propose a rich parametric class of stationary range anisotropic covariance structures that, when applied in R², greatly increases the scope of variogram contors. Geometric anisotropy, which provides the most common generalization of isotropy within stationarity, is a special case. Our class is built from monotonic isotropic correlation functions and special cases include the Matérn and the general exponential functions. As a result, our range anisotropic correlation specification can be attached to a second order stationary spatial process model, unlike ad hoc approaches to range anisotropy in the literature. We adopt a Bayesian perspective to obtain full inference and demonstrate how to fit the resulting model using sampling-based methods. In the presence of measurement error/microscale effect, we can obtain both the usual predictive as well as the noiseless predictive distribution. We analyze a data set of scallop catches under the general exponential range anisotropic model, withholding ten sites to compare the accuracy and precision of the standard and noiseless predictive distributions.     

An application of Bayesian variable selection to spatial concurrent linear models

Spatial concurrent linear models, in which the model coefficients are spatial processes varying at a local level, are flexible and useful tools for analyzing spatial data. One approach places stationary Gaussian process priors on the spatial processes, but in applications the data may display strong nonstationary patterns. In this article, we propose a Bayesian variable selection approach based on wavelet tools to address this problem. The proposed approach does not involve any stationarity assumptions on the priors, and instead we impose a mixture prior directly on each wavelet coefficient. We introduce an option to control the priors such that high resolution coefficients are more likely to be zero. Computationally efficient MCMC procedures are provided to address posterior sampling, and uncertainty in the estimation is assessed through posterior means and standard deviations. Examples based on simulated data demonstrate the estimation accuracy and advantages of the proposed method. We also illustrate the performance of the proposed method for real data obtained through remote sensing.     

Validating graph-based connectivity models with independent presence–absence and genetic data sets

Habitat connectivity is a key objective of current conservation policies and is commonly modeled by landscape graphs (i.e., sets of habitat patches [nodes] connected by potential dispersal paths [links]). These graphs are often built based on expert opinion or species distribution models (SDMs) and therefore lack empirical validation from data more closely reflecting functional connectivity. Accordingly, we tested whether landscape graphs reflect how habitat connectivity influences gene flow, which is one of the main ecoevolutionary processes. To that purpose, we modeled the habitat network of a forest bird (plumbeous warbler [Setophaga plumbea]) on Guadeloupe with graphs based on expert opinion, Jacobs’ specialization indices, and an SDM. We used genetic data (712 birds from 27 populations) to compute local genetic indices and pairwise genetic distances. Finally, we assessed the relationships between genetic distances or indices and cost distances or connectivity metrics with maximum-likelihood population-effects distance models and Spearman correlations between metrics. Overall, the landscape graphs reliably reflected the influence of connectivity on population genetic structure; validation R² was up to 0.30 and correlation coefficients were up to 0.71. Yet, the relationship among graph ecological relevance, data requirements, and construction and analysis methods was not straightforward because the graph based on the most complex construction method (species distribution modeling) sometimes had less ecological relevance than the others. Cross-validation methods and sensitivity analyzes allowed us to make the advantages and limitations of each construction method spatially explicit. We confirmed the relevance of landscape graphs for conservation modeling but recommend a case-specific consideration of the cost-effectiveness of their construction methods. We hope the replication of independent validation approaches across species and landscapes will strengthen the ecological relevance of connectivity models.     

Simulating light-saturation curves for photosynthesis and calcification by reef-building corals

Light-saturation curves for photosynthesis by reef-building corals have previously been simulated by three functions: the right rectangular hyperbola, a simple exponential function, and the hyperbolic tangent function. Studies of photosynthesis by other organisms have also frequently considered the application of a rectilinear function. This communication analyzes lightsaturation curves for photosynthesis by the Atlantic rose coral Manicina aerolata, the Atlantic staghorn coral Acropora cervicornis, and the Pacific staghorn coral A. formosa. It also analyzes light-saturation curves for calcification by A. cervicornis and A. formosa. This communication demonstrates that the two most accurate functions (as measured by coefficients of determination) are the simple exponential function and the hyperbolic tangent function. The hyperbolic tangent function is preferred because parameter estimates obtained with this function have narrower confidence intervals than those obtained through the application of the simple exponential function. The hyperbolic tangent function can also be used successfully to simulate light-saturation curves for light-enhanced calcification.     

Determination of 1‐octanol/water partition coefficients of isocyanate compounds by an HPLC method

The octanol/water partition coefficients (P _ow) of six monoisocyanate and five diisocyanate compounds were determined by high performance liquid chromatograph (HPLC) methods. Two HPLC peaks, a broad tailed peak followed by a sharp one, were observed with all compounds. The later peak was identified as the isocyanate compound. The P _ow value for each isocyanate compound was determined by fitting the capacity factor of the peak to the regression equation drawn from those of reference compounds. The hydrophobic substituent constants for the isocyanate group were calculated with each of the compounds. The value of this constant was strongly dependent on the type of carbon bound to the isocyanate group and the average values for the aromatic and aliphatic isocyanate groups were 0.35 and — 0.52, respectively. Because of the reactivity of those compounds in the aquatic environment, the P _ow has little relevance to calculation of their environmental fate and ecotoxicity.     

Risk-Based Viable Population Monitoring

Abstract:  We describe risk-based viable population monitoring, in which the monitoring indicator is a yearly prediction of the probability that, within a given timeframe, the population abundance will decline below a prespecified level. Common abundance-based monitoring strategies usually have low power to detect declines in threatened and endangered species and are largely reactive to declines. Comparisons of the population's estimated risk of decline over time will help determine status in a more defensible manner than current monitoring methods. Monitoring risk is a more proactive approach; critical changes in the population's status are more likely to be demonstrated before a devastating decline than with abundance-based monitoring methods. In this framework, recovery is defined not as a single evaluation of long-term viability but as maintaining low risk of decline for the next several generations. Effects of errors in risk prediction techniques are mitigated through shorter prediction intervals, setting threshold abundances near current abundance, and explicitly incorporating uncertainty in risk estimates. Viable population monitoring also intrinsically adjusts monitoring effort relative to the population's true status and exhibits considerable robustness to model misspecification. We present simulations showing that risk predictions made with a simple exponential growth model can be effective monitoring indicators for population dynamics ranging from random walk to density dependence with stable, decreasing, or increasing equilibrium. In analyses of time-series data for five species, risk-based monitoring warned of future declines and demonstrated secure status more effectively than statistical tests for trend.     

Sensitivity of species-distribution models to error,bias, and model design: An application to resource selection functions for woodland caribou

Models that predict distribution are now widely used to understand the patterns and processes of plant and animal occurrence as well as to guide conservation and management of rare or threatened species. Application of these methods has led to corresponding studies evaluating the sensitivity of model performance to requisite data and other factors that may lead to imprecise or false inferences. We expand upon these works by providing a relative measure of the sensitivity of model parameters and prediction to common sources of error, bias, and variability. We used a one-at-a-time sample design and GPS location data for woodland caribou (Rangifer tarandus caribou) to assess one common species-distribution model: a resource selection function. Our measures of sensitivity included change in coefficient values, prediction success, and the area of mapped habitats following the systematic introduction of geographic error and bias in occurrence data, thematic misclassification of resource maps, and variation in model design. Results suggested that error, bias and model variation have a large impact on the direct interpretation of coefficients. Prediction success and definition of important habitats were less responsive to the perturbations we introduced to the baseline model. Model coefficients, prediction success, and area of ranked habitats were most sensitive to positional error in species locations followed by sampling bias, misclassification of resources, and variation in model design. We recommend that researchers report, and practitioners consider, levels of error and bias introduced to predictive species-distribution models. Formal sensitivity and uncertainty analyses are the most effective means for evaluating and focusing improvements on input data and considering the range of values possible from imperfect models.     

Simulation experiments comparing alternative process formulations using a factorial design

This paper reviews methods for exploring the differences between alternative equations in complex ecosystem models. A factorial design is proposed as a method for exposing possible interactions between equation forms in their effect on model output as well as to clarify differences between the main candidate equations. A number of display methods arising from statistical analysis are used including normal Q-Q plots, linear rank plots and interaction diagrams. The methods were illustrated using a complex ecosystem model of Lake Ontario. We found the methods effective at illustrating major differences between equations although several difficulties arose due to the complexity of the models and the diffuse nature of the data supporting model validation. Questions of the method for standardization of equation forms so that the compared equations are in some way analogous, are important. These methods are probably most useful in cases where the data are of sufficient quality to indicate not only how different equation forms affect model output but also which are to be preferred.     

Numerical simulation of non-breaking solitary wave run-up using exponential basis functions

A meshless method based on exponential basis functions (EBFs) is developed to simulate the propagation of solitary waves and run-up on the slope. The presented method is a boundary-type meshless method applying the exponential basis functions with complex exponents. The solution of governing equations is considered as a series of these basis functions. Boundary conditions are satisfied through a point-wise collocation approach. Based on the presented EBF meshless method, a new formula is introduced for the maximum run-up height on different slopes, valuable for engineering applications. The results obtained through the numerical method in the prediction of solitary wave propagation and estimation of run-up are verified through the comparison with experimental data. The comparison with 159 experimental data indicates that this new formula is more accurate than the preceding formulas in predicting the maximum run-up of non-breaking solitary waves. Minimum calculation time and convenient performances are the other advantages of this method.     

Deterministic particle tracking simulation of pollutant discharges in rivers and estuaries

A two-dimensional deterministic particle tracking model, in which the anisotropic-dispersive process is described by a particle strength exchange scheme, is established for the simulation of pollutant transport in vertically well-mixed rivers and estuaries. By simulating two benchmark problems with analytic solutions, the PSE scheme is shown to be accurate even if the anisotropic ratio of dispersion coefficients is very high. Further simulations of two specific problems concerning the optimal effluent discharge location and procedure are presented. The major conclusion is that in a tidal estuary with a relatively large fresh-water flow, setting the discharge position at the mixing center and making the discharge rate proportional to flow speed may minimize the peaks of concentration.     

Geochemical interpretation of cancer maps of Finland

In order to reveal possible cause-and-effect relationships and correlations between geochemical variables and the incidences of various forms of cancer, geochemical maps (soil and groundwater) and cancer maps of Finland are compared using standard methods of correlation analysis. The cancer incidence maps published by the Finnish Cancer Registry and soil and groundwater geochemical maps published by the Geological Survey of Finland, both in colour, were decoded to numerical incidence or concentration values by placing a rectangular grid of 684 evenly spaced observation points over each map representing the entire area of the mainland of Finland,i.e. the points were located at intervals of about 25 kilometres on the ground. Bivariate correlation coefficients were calculated between the variables for cancer incidence and the geochemical data matrices. As a general rule, the results show a low degree of correlation between the variables (r = 0.00 – 0.40), which suggests that the types studied of cancer are not related to the geochemical variables. There are a few possible exceptions, however, such as cancer of the colon in males and females in relation to arsenic and uranium in the soil and hardness of the groundwater, where the Spearman product-moment correlation coefficients are 0.59, 0.55 and 0.51 respectively, so that the cancer case may have a geochemical factor implicated in their aetiology, albeit very vaguely. The relatively high correlation coefficients (0.61, 0.62 and 0.63 respectively) recorded for the dependence of total cancer in females on groundwater hardness and uranium and arsenic in till must be regarded as meaningless in view of the multicausative aetiology of total cancer (all forms combined).     

Evidence-based guidelines for automated conservation assessments of plant species

Assessing species’ extinction risk is vital to setting conservation priorities. However, assessment endeavors, such as those used to produce the IUCN Red List of Threatened Species, have significant gaps in taxonomic coverage. Automated assessment (AA) methods are gaining popularity to fill these gaps. Choices made in developing, using, and reporting results of AA methods could hinder their successful adoption or lead to poor allocation of conservation resources. We explored how choice of data cleaning type and level, taxonomic group, training sample, and automation method affect performance of threat status predictions for plant species. We used occurrences from the Global Biodiversity Information Facility (GBIF) to generate assessments for species in 3 taxonomic groups based on 6 different occurrence-based AA methods. We measured each method's performance and coverage following increasingly stringent occurrence cleaning. Automatically cleaned data from GBIF performed comparably to occurrence records cleaned manually by experts. However, all types of data cleaning limited the coverage of AAs. Overall, machine-learning-based methods performed well across taxa, even with minimal data cleaning. Results suggest a machine-learning-based method applied to minimally cleaned data offers the best compromise between performance and species coverage. However, optimal data cleaning, training sample, and automation methods depend on the study group, intended applications, and expertise.     

Generalized functional responses for species distributions

Researchers employing resource selection functions (RSFs) and other related methods aim to detect correlates of space-use and mitigate against detrimental environmental change. However, an empirical model fit to data from one place or time is unlikely to capture species responses under different conditions because organisms respond nonlinearly to changes in habitat availability. This phenomenon, known as a functional response in resource selection, has been debated extensively in the RSF literature but continues to be ignored by practitioners for lack of a practical treatment. We therefore extend the RSF approach to enable it to estimate generalized functional responses (GFRs) from spatial data. GFRs employ data from several sampling instances characterized by diverse profiles of habitat availability. By modeling the regression coefficients of the underlying RSF as functions of availability, GFRs can account for environmental change and thus predict population distributions in new environments. We formulate the approach as a mixed-effects model so that it is estimable by readily available statistical software. We illustrate its application using (1) simulation and (2) wolf home-range telemetry. Our results indicate that GFRs can offer considerable improvements in estimation speed and predictive ability over existing mixed-effects approaches.     

有机溶剂,表面活性剂及NaCl对对二氯苯在水中活度系数的影响

本文利用气相色谱法测定了对二氯苯在不同组成的水溶液体系中的活度系数,其中包括二组份与三组份共溶剂体系、表面活性剂-水体系与NaCl-水体系。实验结果表明:对二氯苯在二组份和三组份共溶剂体系中的活度系数的对数值与各组份的体积分数之间有线性加和关系;表面活性剂在达到临界胶束浓度以上时,可显著降低对二氯苯在水中的活度系数;对二氯苯在NaCl水溶液中的活度系数与NaCI的含量之间成正比关系。作者还利用UNIFAC法估算了对二氯苯在各种共溶剂体系中的活度系数,所得估算值与测定值相接近。     

平衡分配法在土壤环境质量基准推导中的相关问题研究

为了促进土壤环境质量标准的修订进程,势必要全面系统地开展土壤环境质量基准的研究工作。理论上,水、土之间是一个相互联系、互相依赖和关系密切的系统;同时,水生态毒理及其质量基准研究起步早,研究方法相对成熟。因此,从水质基准来推导土壤环境质量基准具有一定的科学依据与实践意义。本文首先从土-水系统中污染物分布的影响因素及其环境行为两方面简要阐述污染物在土-水系统中的分布规律;然后,扼要介绍了平衡分配(Eq P)理论及其在环境质量基准研究中的应用;之后,从Eq P方法的不确定性、毒理数据选用的争议性、Koc的局限性和分配系数的确定方法选择性等方面,探讨了平衡分配法在土壤环境质量基准研究中应用的瓶颈问题;最后,对此项研究进行了总结与展望。     

Non-homogeneous Poisson models with a change-point: an application to ozone peaks in Mexico city

In this paper, we use some non-homogeneous Poisson models in order to study the behavior of ozone measurements in Mexico City. We assume that the number of ozone peaks follows a non-homogeneous Poisson process. We consider four types of rate function for the Poisson process: power law, Musa–Okumoto, Goel–Okumoto, and a generalized Goel–Okumoto rate function. We also assume that a change-point may or may not be present. The analysis of the problem is performed by using a Bayesian approach via Markov chain Monte Carlo methods. The best model is chosen using the DIC criterion as well as graphical approach.     

Interpolation performance of a spatio-temporal model with spatially varying coefficients: application to PM<Subscript>10</Subscript> concentrations in Rio de Janeiro

In this work we present a Bayesian analysis in linear regression models with spatially varying coefficients for modeling and inference in spatio-temporal processes. This kind of model is particularly appealing in situations where the effect of one or more explanatory processes on the response present substantial spatial heterogeneity. We describe for this model how to make inference about the regression coefficients and response processes under two scenarios: when the explanatory processes are known throughout the study region, and when they are known only at the sampling locations. Using a simulation experiment we investigate how parameter inference and interpolation performance are affected by some features of the data and prior distribution that is used. The proposed methodology is used to model the dataset on PM₁₀ levels in the metropolitan region of Rio de Janeiro presented in Paez and Gamerman (2003).     

Characterising strongly advected discharges in the initial dilution zone

Mean concentration fields of strongly advected non-buoyant discharges are characterised with a double-Gaussian assumption. Comparisons with experimental data show that the approximation provides a reasonable representation of the cross-sectional profiles. The self-similarity of these profiles enables their form to be represented by two additional parameters, one describing the relative separation of the peaks and the other the ratio of the cross-sectional spreads. Values for these additional parameters are determined from experimental data. This systematic approach to characterising the strongly advected flows provides a consistent framework for determining spreading rates and concentration ratios, such as the peak to centreline maximum and the peak to top hat. The double-Gaussian framework also provides a basis for comparisons with the CorJet and VisJet numerical models. In addition the double-Gaussian assumption is employed to interpret data obtained using the Light Attenuation technique. This is a relatively simple measuring system, which provides depth integrated concentration information. The data obtained using this technique is shown to be generally consistent with that from previous studies.