Point-Based Mark-Recapture Distance Sampling

Avian surveys using point sampling for abundance estimation have either focused on distance sampling or more commonly mark-recapture to correct for detection bias. Combining mark-recapture and distance sampling (MRDS) has become an effective tool for line transects, but it has been largely ignored in point sampling literature. We describe MRDS and show that the previously published methods for point sampling are special cases. Using simulated data and golden-cheeked warbler (Dendroica chrysoparia) survey data from Texas, we demonstrate large differences in abundance estimates resulting from different independence assumptions. Data and code are provided in supplementary materials.     

Hierarchical Nonlinear Spatio-temporal Agent-Based Models for Collective Animal Movement

Modeling complex collective animal movement presents distinct challenges. In particular, modeling the interactions between animals and the nonlinear behaviors associated with these interactions, while accounting for uncertainty in data, model, and parameters, requires a flexible modeling framework. To address these challenges, we propose a general hierarchical framework for modeling collective movement behavior with multiple stages. Each of these stages can be thought of as processes that are flexible enough to model a variety of complex behaviors. For example, self-propelled particle (SPP) models (e.g., Vicsek et al. in Phys Rev Lett 75:1226–1229, 1995) represent collective behavior and are often applied in the physics and biology literature. To date, the study and application of these models has almost exclusively focused on simulation studies, with less attention given to rigorously quantifying the uncertainty. Here, we demonstrate our general framework with a hierarchical version of the SPP model applied to collective animal movement. This structure allows us to make inference on potential covariates (e.g., habitat) that describe the behavior of agents and rigorously quantify uncertainty. Further, this framework allows for the discrete time prediction of animal locations in the presence of missing observations. Due to the computational challenges associated with the proposed model, we develop an approximate Bayesian computation algorithm for estimation. We illustrate the hierarchical SPP methodology with a simulation study and by modeling the movement of guppies.Supplementary materials accompanying this paper appear online.     

Another Look at Bayesian Analysis of AMMI Models for Genotype-Environment Data

Linear–bilinear models are frequently used to analyze two-way data such as genotype-by-environment data. A well-known example of this class of models is the additive main effects and multiplicative interaction effects model (AMMI). We propose a new Bayesian treatment of such models offering a proper way to deal with the major problem of overparameterization. The rationale is to ignore the issue at the prior level and apply an appropriate processing at the posterior level to be able to arrive at easily interpretable inferences. Compared to previous attempts, this new strategy has the great advantage of being directly implementable in standard software packages devoted to Bayesian statistics such as WinBUGS/OpenBUGS/JAGS. The method is assessed using simulated datasets and a real dataset from plant breeding. We discuss the benefits of a Bayesian perspective to the analysis of genotype-by-environment interactions, focusing on practical questions related to general and local adaptation and stability of genotypes. We also suggest a new solution to the estimation of the risk of a genotype not exceeding a given threshold.     

Using a Bayesian Hierarchical Linear Mixing Model to Estimate Botanical Mixtures

In grazing systems, estimating the dietary choices of animals is challenging but can be achieved using plant-wax markers, natural compounds that provide a signature of individual plants. If sufficiently distinct, these signatures can be used to characterize the makeup of a botanical mixture or diet. Bayesian hierarchical models for linear unmixing (BHLU) have been widely used for hyperspectral image analysis and geochemistry, but not diet mixtures. The aim of this study was to assess the efficiency of BHLU to estimate botanical mixtures. Plant-wax marker concentrations from eight forages found in Nebraska were used for simulating combinations of two, three, five and eight species. Also, actual forage mixtures were constructed in laboratory and evaluated. Analyses were performed using BHLU with 2 prior choices for forage proportions (uniform and Gaussian), 2 covariance structures (independent and correlated markers), stable isotope mixing models (SIMM), and nonnegative least squares (NNLS). Accounting for correlations between markers increased efficiency. Estimation error increased when Gaussian priors were used to model forage proportions. Performance of BHLU, SIMM, and NNLS was reduced with the more complex botanical mixtures and the limited number of markers. For simple mixtures, BHLU is a reliable alternative to NNLS for estimation of forage proportions. Supplementary materials accompanying this paper appear online.     

Effective Sample Size for Line Transect Sampling Models with an Application to Marine Macroalgae

This paper provides a framework for estimating the effective sample size in a spatial regression model context when the data have been sampled using a line transect scheme and there is an evident serial correlation due to the chronological order in which the observations were collected. We propose a linear regression model with a partially linear covariance structure to address the computation of the effective sample size when spatial and serial correlations are present. A recursive algorithm is described to separately estimate the linear and nonlinear parameters involved in the covariance structure. The kriging equations are also presented to explore the kriging variance between our proposal and a typical spatial regression model. An application in the context of marine macroalgae, which motivated the present work, is also presented.     

Accounting for Lack of Independence and Partial Overlap of Observation Zones in Line-Transect Mark-Recapture Distance Sampling

Line-transect mark-recapture distance sampling methods can be used to estimate abundance when at least two observers sight and record distances to detected groups of individuals within the survey area. However, a lack of independence between the observer’s detections will cause biased abundance estimates. Studies are also typically designed such that there is complete overlap of the regions searched by the two observers, but that may not always be possible. Here we detail an intuitive approach for line-transect distance sampling applications based upon logistic regression to account for a potential lack of independence by using the detections of one observer as a covariate in the detection function of the second observer. Partial overlap of the observer survey regions can be addressed by constraining detection probability to equal 0 for the respective observer outside of the overlap zone. We show via simulation that the method provides reliable estimates of abundance and is not affected by random unmodeled heterogeneity in detection probability. The method is illustrated by estimating abundance within the covered region of an aerial line-transect survey for New Zealand’s endemic Hector’s dolphin (Cephalorhynchus hectori hectori) conducted in the austral summer of 2013, the motivating application for this work. Supplementary materials accompanying this paper appear on-line.     

A Bayesian Approach to Spatial Prediction With Flexible Variogram Models

A Bayesian approach to covariance estimation and spatial prediction based on flexible variogram models is introduced. In particular, we consider black-box kriging models. These variogram models do not require restrictive assumptions on the functional shape of the variogram; furthermore, they can handle quite naturally non isotropic random fields. The proposed Bayesian approach does not require the computation of an empirical variogram estimator, thus avoiding the arbitrariness implied in the construction of the empirical variogram itself. Moreover, it provides a complete assessment of the uncertainty in the variogram estimation. The advantages of this approach are illustrated via simulation studies and by application to a well known benchmark dataset.     

A Varying Coefficients Model For Estimating Finite Population Totals: A Hierarchical Bayesian Approach

In some finite sampling situations, there is a primary variable that is sampled, and there are measurements on covariates for the entire population. A Bayesian hierarchical model for estimating totals for finite populations is proposed. A nonparametric linear model is assumed to explain the relationship between the dependent variable of interest and covariates. The regression coefficients in the linear model are allowed to vary as a function of a subset of covariates nonparametrically based on B-splines. The generality of this approach makes it robust and applicable to data collected using a variety of sampling techniques, provided the sample is representative of the finite population. A simulation study is carried out to evaluate the performance of the proposed model for the estimation of the population total. Results indicate accurate estimation of population totals using the approach. The modeling approach is used to estimate the total production of avocado for a large group of groves in Mexico.     

Sampling Soybean Roots: A Comparison of Excavation and Coring Methods

Abstract

Accurate estimates of soybean root productivity are needed to estimate carbon (C) inputs to soil. Soil excavation and coring methods were compared where soybean was subject to ambient, elevated carbon dioxide (CO₂) and ozone (O₃) treatments. We evaluated within‐season changes in biomass and shoot–root production, labor requirements, and damage to plots. Estimates of root biomass were similar, but excavation‐based estimates required less total time. Core‐based estimates provided similar levels of precision, allowed sampling of deeper depths, and reduced both plot disturbance and the amount of effort devoted to tasks performed in the field. Correlations between root and shoot biomass were weak and varied with time of sampling. Collectively, results suggest caution should be exercised when making predictions about C allocation to roots or soils based on shoot–root ratios or when scaling up field‐based findings to predict larger or longer‐scale trends.     

采样间距对有机质与环境因子相关性识别的影响

以黑龙江省海伦县为研究区,采用限制最小样点间距抽样和方差分解方法研究了采样点间距对土壤有机质(SOM)与环境因子相关关系识别的影响。结果表明,温度、土壤类型、坡度及作物生物量(NDVI)是研究区SOM变异性的主要环境控制因子;采样点数量为125个,最小样点间距为3.5 km时,SOM数据所包含的多尺度信息与多尺度的环境因子达到最佳尺度匹配,环境因子与SOM相关性最高,此时环境因子单独解释的方差及环境因子与空间变量共同解释的方差也最高,而空间变量解释的SOM方差则最小,因此,最适于识别研究区SOM与主要环境控制因子间的相关关系。     

Multi-scale Modeling of Animal Movement and General Behavior Data Using Hidden Markov Models with Hierarchical Structures

Hidden Markov models (HMMs) are commonly used to model animal movement data and infer aspects of animal behavior. An HMM assumes that each data point from a time series of observations stems from one of N possible states. The states are loosely connected to behavioral modes that manifest themselves at the temporal resolution at which observations are made. Due to advances in tag technology and tracking with digital video recordings, data can be collected at increasingly fine temporal resolutions. Yet, inferences at time scales cruder than those at which data are collected and, which correspond to larger-scale behavioral processes, are not yet answered via HMMs. We include additional hierarchical structures to the basic HMM framework, incorporating multiple Markov chains at various time scales. The hierarchically structured HMMs allow for behavioral inferences at multiple time scales and can also serve as a means to avoid coarsening data. Our proposed framework is one of the first that models animal behavior simultaneously at multiple time scales, opening new possibilities in the area of animal movement and behavior modeling. We illustrate the application of hierarchically structured HMMs in two real-data examples: (i) vertical movements of harbor porpoises observed in the field, and (ii) garter snake movement data collected as part of an experimental design. Supplementary materials accompanying this paper appear online.     

转基因种子检测的二维取样分析方法

PCR技术鉴定种子纯度，具有准确、快速等特点，且极适合于单粒种子分析的要求，但较高的检测成本制约了其推广应用。本研究提出了一种对种子样品二维取样混合的PCR分析策略，以降低PCR检测的成本。     

Hierarchical scheme for LC-MSn identification of chlorogenic acids

The fragmentation behavior of 18 chlorogenic acids that are not substituted at position 1 has been investigated using LC-MS(4) applied to a methanolic coffee bean extract and commercial cider (hard cider). Using LC-MS(3), it is possible to discriminate between each of the three isomers of p-coumaroylquinic acid, caffeoylquinic acid, feruloylquinic acid, and dicaffeoylquinic acid, and a hierarchical key has been prepared to facilitate this process when standards are not available. MS(4) fragmentations further support these assignments, but were not essential in reaching them. The distinctive behavior of 4-acyl and 3-acyl chlorogenic acids compared with the 5-acyl chlorogenic acids is a key factor permitting these assignments. The fragmentation patterns are dependent upon the particular stereochemical relationships between the individual substituents on the quinic acid moiety. Fragmentation is facilitated by 1,2-acyl participation and proceeds through quinic acid conformers in which the relevant substituents transiently adopt a 1,3-syn-diaxial relationship. Selected ion monitoring at m/z 529 clearly indicated the presence in coffee of six caffeoylferuloylquinic acid isomers, whereas previously only two or three had been demonstrated. The hierarchical key permitted specific structures to be assigned to each of the six isomers. These assignments are internally consistent and consistent with the limited data previously available.     

农村电网实时安全风险管理模型与方法研究

随着科学技术的发展,农村电网的可靠性研究在算法方面有了新的进步。蒙特卡罗仿真法具有抽样次数与电网规模无关而只与要求的精度有关的特点,在农村电网安全风险管理中得到了广泛的应用。介绍了序贯仿真、非序贯仿真基本的蒙特卡罗概率仿真算法的基本原理和特点。     

Measurement of Atmospheric Mercury Species with Manual Sampling and Analysis Methods in a Case Study in Indiana

Ground-level concentrations of three atmospheric mercury species were measured using manual sampling and analysis to provide data for estimates of mercury dry deposition. Three monitoring stations were operated simultaneously during winter, spring, and summer 2004, adjacent to three mercury wet-deposition monitoring stations in northern, central, and southern Indiana. The monitoring locations differed in land-use setting and annual mercury-emissions level from nearby sources. A timer-controlled air-sampling system that contained a three-part sampling train was used to isolate reactive gaseous mercury, particulate-bound mercury, and elemental mercury. The sampling trains were exchanged every 6 days, and the mercury species were quantified in a laboratory. A quality-assurance study indicated the sampling trains could be held at least 120 h without a significant change in reactive gaseous or particulate-bound mercury concentrations. The manual sampling method was able to provide valid mercury concentrations in 90 to 95% of samples. Statistical differences in mercury concentrations were observed during the project. Concentrations of reactive gaseous and elemental mercury were higher in the daytime samples than in the nighttime samples. Concentrations of reactive gaseous mercury were higher in winter than in summer and were highest at the urban monitoring location. The results of this case study indicated manual sampling and analysis could be a reliable method for measurement of atmospheric mercury species and has the capability for supplying representative concentrations in an effective manner from a long-term deposition-monitoring network.     

Sampling and preparation of sample for chemical examination

Sampling and methods for reducing a laboratory sample to a test sample are discussed, with particular emphasis on sampling peanuts for aflatoxin analysis as a practical example. The only way to control the total error in the analysis of this heterogeneous product is to take and to analyze many and large samples.     

Sampling and characterization of air for organic compounds

Computerized gas chromatography/mass spectrometry was applied to the analysis of organic compounds in air at a National Air Surveillance Network station in Kansas City. Sampling approaches used included particulate sampling, adsorption on XAD-2 resin followed by solvent elution, and adsorption on Tenax GC followed by direct desorption onto a gas chromatography column. Eighty-two compounds were identified. Unusually large amounts of phenol and phenolformaldehyde resin intermediates were found.