ALBERT

Description: Recent years have seen a fast-growing body of literature concerned with the statistical modeling of animal movement in the two horizontal dimensions. On the other hand, there is very little statistical work that deals with animal movement in the vertical dimension. We present an approach that provides an important step in analyzing such data. In particular, we introduce a hidden Markov-type modeling approach for time series comprising the depths of a diving marine mammal, thus modeling movement in the water column. We first develop a baseline Markov-switching model, which is then extended to incorporate feedback and semi-Markovian components, motivated by the observations made for a particular species, Blainville’s beaked whale ( Mesoplodon densirostris ). The application of the proposed model to the beaked whale data reveals both strengths and weaknesses of the suggested modeling framework. The framework is general enough that we anticipate that it can be used for many other species given minor changes in the model structure.

Description: Transcriptome sequencing (RNA-Seq) yields massive data sets, containing a wealth of information on the expression of a genome. While numerous methods have been developed for the analysis of differential gene expression, little has been attempted for the localization of transcribed regions, that is, segments of DNA that are transcribed and processed to result in a mature messenger RNA. Our understanding of genomes, mostly annotated from biological experiments or computational gene prediction methods, could benefit greatly from re-annotation using the high precision of RNA-Seq. We consider five classes of genome segmentation methods to delineate transcribed regions, including intron/exon boundaries, based on RNA-Seq data. The methods provide different functionality and include both exact and heuristic approaches, using diverse models, such as hidden Markov or Bayesian models, and diverse algorithms, such as dynamic programming or the forward-backward algorithm. We evaluate the methods in a simulation study where RNA-Seq read counts are generated from parametric models as well as by resampling of actual yeast RNA-Seq data. The methods are compared in terms of criteria that include global and local fit to a reference segmentation, Receiver Operator Characteristic (ROC) curves, and coverage of credibility intervals based on posterior change-point distributions. All compared algorithms are implemented in packages available on the Comprehensive R Archive Network (CRAN, http://cran.r-project.org ). The data set used in the simulation study is publicly available from the Sequence Read Archive (SRA, http://www.ncbi.nlm.nih.gov/sra ). While the different methods each have pros and cons, our results suggest that the EBS Bayesian approach of Rigaill, Lebarbier, and Robin ( 2012 ) performs well in a re-annotation context, as illustrated in the simulation study and in the application to actual yeast RNA-Seq data. This article has supplementary material online.

Description: Motivated by a study on factors affecting the level of photosynthetic activity in a natural ecosystem, we propose nonlinear varying-coefficient models, in which the relationship between the predictors and the response variable is allowed to be nonlinear. One-step local linear estimators are developed for the nonlinear varying-coefficient models and their asymptotic normality is established leading to point-wise asymptotic confidence bands for the coefficient functions. Two-step local linear estimators are also proposed for cases where the varying-coefficient functions admit different degrees of smoothness; bootstrap confidence intervals are utilized for inference based on the two-step estimators. We further propose a generalized  F -test to study whether the coefficient functions vary over a covariate. We illustrate the proposed methodology via an application to an ecology data set and study the finite sample performance by Monte Carlo simulation studies.

Description: Agricultural experiments often have a completely randomized design, and multiple, correlated variables are measured. This paper addresses an appropriate statistical evaluation. A multivariate t -distribution is used for the calculation of multiplicity-adjusted p -values and simultaneous confidence intervals. The number of the multiple variables as well as their correlations are taken into account this way. We consider ratios of means instead of differences, and comparisons versus the overall mean instead of all-pair comparisons. A data set from a greenhouse experiment with glucosinolates of several cultivars of Chinese cabbage ( Brassica rapa subsp. pekinensis ) is used as an example. Related code based on the R-package SimComp is presented. This package allows a wide application in many agricultural experiments with a similar design.

Description: The identification of sea regimes from environmental multivariate times series is complicated by the mixed linear–circular support of the data, by the occurrence of missing values, by the skewness of some variables, and by the temporal autocorrelation of the measurements. We address these issues simultaneously by a hidden Markov approach, and segment the data into pairs of toroidal and skew-elliptical clusters by means of the inferred sequence of latent states. Toroidal clusters are defined by a class of bivariate von Mises densities, while skew-elliptical clusters are defined by mixed linear models with positive random effects. The core of the classification procedure is an EM algorithm accounting for missing measurements, unknown cluster membership, and random effects as different sources of incomplete information. Moreover, standard simulation routines allow for the efficient computation of bootstrap standard errors. The proposed procedure is illustrated for a multivariate marine time series, and identifies a number of wintertime regimes in the Adriatic Sea.

Description: We study a Bayes factor based on a robust test statistic, MAX3, for case-control genetic association studies. MAX3 is the maximum of three trend tests derived under the recessive, additive and dominant genetic models, respectively. The proposed Bayes factor, denoted as BFM, models the asymptotic distributions of MAX3 under the null and alternative hypotheses. It is compared to an existing Bayes factor based on Bayesian model averaging (BMA). Through simulation studies, we show that both BFM and BMA are robust under genetic model uncertainty. They both depend on specifying the prior distribution for the genetic model, and have similar performance when common objective priors are used. When the prior places a large probability on the true (wrong) genetic model, BMA (BFM) is more powerful. Applications to real data from a genome-wide association study are presented to illustrate their use and show their sensitivity under genetic model uncertainty.

Description: Both existing models for non-symmetric distributions on 3-dimensional rotations and their associated one-sample inference methods have serious limitations in terms of both interpretability and ease of use. Based on the intuitively appealing Uniform Axis- Random Spin (UARS) construction of Bingham, Nordman, and Vardeman ( 2009 ) for symmetric families of distributions, we propose new highly interpretable and tractable classes of non-symmetric distributions that are derived from mixing UARS distributions. These have an appealing Preferred Axis-Random Spin (PARS) construction and (unlike existing models) directly interpretable parameters. Non-informative one-sample Bayes inference in these models is a direct generalization of UARS methods introduced in Bingham, Vardeman, and Nordman ( 2009 ), where credible levels were found to be essentially equivalent to frequentist coverage probabilities. We apply the new models and inference methods to a problem in biomechanics, where comparison of model parameters provides meaningful comparisons for the nature of movement about the calcaneocuboid joint of three different primate subjects.

Description: Parametric identification of plant growth models formalized as discrete dynamical systems is a challenging problem due to specific data acquisition (system observation is generally done with destructive measurements), non-linear dynamics, model uncertainties and high-dimensional parameter space. In this study, we present a novel idea of modeling plant growth in the framework of non-homogeneous hidden Markov models (Cappé, Moulines, and Rydén 2005 ), for a certain class of plants with known organogenesis (structural development). Unknown parameters of the models are estimated via a stochastic variant of a generalized EM (Expectation-Maximization) algorithm and approximate confidence intervals are given via parametric bootstrap. The complexity of the model makes both the E-step (expectation step) and the M-step (maximization step) non-explicit. For this reason, the E-step is approximated via a sequential Monte Carlo procedure (sequential importance sampling with resampling) and the M-step is separated into two steps (Conditional-Maximization), where before applying a numerical maximization procedure (quasi-Newton type), a large subset of unknown parameters is updated explicitly conditioned on the other subset. A simulation study and a case-study with real data from the sugar beet are considered and a model comparison is performed based on these data. Appendices are available online.

Description: We consider the problem of designing a depletion or removal survey as part of estimating animal abundance for populations with imperfect capture or detection rates. In a depletion survey, animals are captured from a given area, counted, and withheld from the population. This process is then repeated some number of times at the same location and the decreasing catches of the local population inform jointly local abundance and the capture or detection rate, which we call catchability. The aim of such a survey may be to learn about the catchability process, and this information may then be applied to a broader survey of the population so as to accurately estimate total abundance. In this manuscript we consider the problem of how to optimally allocate sampling effort at the depletion sites. Allocating sampling effort involves determining how many times to repeat the depletion process at a given site versus how many different sites to include in the sampling. By maximizing the Fisher information of the parameter describing catchability as a function of the survey design, we attempt to estimate the optimal number of depletions per site, which depends on the catchability value itself. We also discuss other aspects of depletion sampling apparent from the derivation of Fisher information, including the difficulties of sampling with low catchability values (e.g. below 0.15), and we consider our results with respect to the annual Chesapeake Bay blue crab abundance survey conducted by the Maryland Department of Natural Resources.

Description: Farm accountancy data network normally serves two objectives: estimating the national agricultural income and providing detailed data at production branch level. This study proposes a new sampling design with two independent samples A and B each addressing one of these objectives. The design is based on stratified sampling along with a combination of optimal and power allocation. Violations of precision targets will be avoided by the collapsing of strata. We assess the accuracy of key structural and economic variables by means of a Monte Carlo simulation. Multiple linear regression has been shown to be a powerful tool for imputing financial data to individual census farms. The results illustrate that the proposed design meets prescribed precision and feasibility restrictions at both the single-strata and national levels. It is further demonstrated that unifying samples A and B helps to significantly reduce the survey costs.

Description: The models presented in this paper are motivated by a stop-over study of semipalmated sandpipers, Calidris pusilla . Two sets of data were collected at the stop-over site: a capture–recapture–resighting data set and a vector of counts of unmarked birds. The two data sets are analyzed simultaneously by combining a new model for the capture–recapture–resighting data set with a binomial likelihood for the counts. The aim of the analysis is to estimate the total number of birds that used the site and the average duration of stop-over. The combined analysis is shown to be highly efficient, even when just 1 % of birds are recaptured, and is recommended for similar investigations. This article has supplementary material online.

Description: Heat waves take a major toll on human populations, with negative impacts on the economy, agriculture, and human health. As a result, there is great interest in studying the changes over time in the probability and magnitude of heat waves. In this paper we propose a hierarchical Bayesian model for serially-dependent extreme temperatures. We assume the marginal temperature distribution follows the generalized Pareto distribution (GPD) above a location-specific threshold, and capture dependence between subsequent days using a transformed max-stable process. Our model allows both the parameters in the marginal GPD and the temporal dependence function to change over time. This allows Bayesian inference on the change in likelihood of a heat wave. We apply this methodology to daily high temperatures in nine cities in the western US for 1979–2010. Our analysis reveals increases in the probability of a heat wave in several US cities. This article has supplementary material online.

Description: We consider mark–recapture–recovery data with additional individual time-varying continuous covariate data. For such data it is common to specify the model parameters, and in particular the survival probabilities, as a function of these covariates to incorporate individual heterogeneity. However, an issue arises in relation to missing covariate values, for (at least) the times when an individual is not observed, leading to an analytically intractable likelihood. We propose a two-step multiple imputation approach to obtain estimates of the demographic parameters. Firstly, a model is fitted to only the observed covariate values. Conditional on the fitted covariate model, multiple “complete” datasets are generated (i.e. all missing covariate values are imputed). Secondly, for each complete dataset, a closed form complete data likelihood can be maximised to obtain estimates of the model parameters which are subsequently combined to obtain an overall estimate of the parameters. Associated standard errors and 95 % confidence intervals are obtained using a non-parametric bootstrap. A simulation study is undertaken to assess the performance of the proposed two-step approach. We apply the method to data collected on a well-studied population of Soay sheep and compare the results with a Bayesian data augmentation approach. Supplementary materials accompanying this paper appear on-line.

Description: Forest composition in the western region of the United States has seen a dramatic change over the past few years due to an increase in mountain pine beetle damage. In order to mitigate the pine beetle epidemic, statistical modeling is needed to predict both the occurrence and the extent of pine beetle damage. Using data on the front range mountains in Colorado between the years 2001–2010 from the National Forest Service, we develop a zero-augmented spatio-temporal beta regression model to predict both the occurrence of pine beetle damage (a binary outcome) and, given damage occurred, the percent of the region infected. Temporal evolution of the pine beetle damage is captured using a dynamic linear model where both the probability and extent of damage depend on the amount of damage incurred in neighboring regions in the previous time period. A sparse conditional autoregressive model is used to capture any spatial information not modeled by spatially varying covariates. We find that the occurrence and extent of pine beetle damage are positively associated with slope and damage in previous time periods.

Description: Toxicological studies often depend on laboratory assays that have thresholds below which environmental pollutants cannot be measured with accuracy. Exposure levels below this limit of detection may well be toxic and hence it is vital to use data analytic methods that handle such left-censored data with as little estimation bias as possible. In an on-going study for which our methodology is developed, levels of residential exposure to polychlorinated biphenyls (PCBs) and the interrelationships of their subtypes (congeners) are characterized. In any given sample many of the congeners may fall below the detection limit. The main problem tackled in this paper is estimation of mean exposure levels and corresponding covariance and correlation matrices for a large number of potentially left-censored measures that have very low bias and are computationally feasible. The proposed methods are likelihood based, using marginal likelihoods for means and variances and pairwise pseudo-likelihoods for correlations and covariances. In the simple bivariate case, head-to-head comparisons show the proposed methods to be computationally more stable than ordinary maximum likelihood estimates (MLEs) and still maintain comparable bias. When the number of variables is much larger than 2, the proposed methods are far more computationally feasible than MLE. Furthermore, they exhibit much less bias when compared to popular imputation procedures. Analysis of the PCB data uncovered interesting correlational structures.

Description: Designs exhibiting super-valid restricted randomization have been proposed as alternatives to efficient row–column designs in situations where the column variance component is small. This paper examines some existing uniformity data and results from two field variety trial programs and concludes that in these situations efficient row–column designs are to be preferred.

Description:    A common goal in environmental epidemiologic studies is to undertake logistic regression modeling to associate a continuous measure of exposure with binary disease status, adjusting for covariates. A frequent complication is that exposure may only be measurable indirectly, through a collection of subject-specific variables assumed associated with it. Motivated by a specific study to investigate the association between lung function and exposure to metal working fluids, we focus on a multiplicative-lognormal structural measurement error scenario and approaches to address it when external validation data are available. Conceptually, we emphasize the case in which true untransformed exposure is of interest in modeling disease status, but measurement error is additive on the log scale and thus multiplicative on the raw scale. Methodologically, we favor a pseudo-likelihood (PL) approach that exhibits fewer computational problems than direct full maximum likelihood (ML) yet maintains consistency under the assumed models without necessitating small exposure effects and/or small measurement error assumptions. Such assumptions are required by computationally convenient alternative methods like regression calibration (RC) and ML based on probit approximations. We summarize simulations demonstrating considerable potential for bias in the latter two approaches, while supporting the use of PL across a variety of scenarios. We also provide accessible strategies for obtaining adjusted standard errors to accompany RC and PL estimates. Content Type Journal Article Pages 1-17 DOI 10.1007/s13253-012-0115-9 Authors Robert H. Lyles, Department of Biostatistics and Bioinformatics, The Rollins School of Public Health of Emory University, 1518 Clifton Rd. N.E., Mailstop 1518-002-3AA, Atlanta, GA 30322, USA Lawrence L. Kupper, Department of Biostatistics, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7420, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description:    This paper uses Generalized Additive Models to evaluate model-based designs for wildlife abundance surveys where substantial pre-existing data are available. This is often the case in fisheries with historical catch and effort data. Compared to conventional stratified design or design-based designs, our model-based designs can be both efficient and flexible, for example in allowing uneven sampling due to survey logistics, and providing a general framework to answer specific design questions. As an example, we describe the design and preliminary implementation of a trawl survey for eleven fish species along the continental slope off South-East Australia. Content Type Journal Article Pages 1-21 DOI 10.1007/s13253-012-0114-x Authors D. Peel, Wealth from Oceans National Research Flagship and CSIRO Mathematics, Informatics and Statistics, Castray Esplanade, Hobart, TAS 7001, Australia M. V. Bravington, Wealth from Oceans National Research Flagship and CSIRO Mathematics, Informatics and Statistics, Castray Esplanade, Hobart, TAS 7001, Australia N. Kelly, Wealth from Oceans National Research Flagship and CSIRO Mathematics, Informatics and Statistics, Castray Esplanade, Hobart, TAS 7001, Australia S. N. Wood, Mathematical Sciences, University of Bath, Bath, BA2 7AY UK I. Knuckey, Fishwell Consulting, 22 Bridge St., Queenscliff, VIC 3225, Australia Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description:    There are two key types of selection in a plant breeding program, namely selection of hybrids for potential commercial use and the selection of parents for use in future breeding. Oakey et al. (in Theoretical and Applied Genetics 113, 809–819, 2006 ) showed how both of these aims could be achieved using pedigree information in a mixed model analysis in order to partition genetic effects into additive and non-additive effects. Their approach was developed for field trial data subject to spatial variation. In this paper we extend the approach for data from trials subject to interplot competition. We show how the approach may be used to obtain predictions of pure stand additive and non-additive effects. We develop the methodology in the context of a single field trial using an example from an Australian sorghum breeding program. Content Type Journal Article Pages 1-11 DOI 10.1007/s13253-012-0117-7 Authors Colleen H. Hunt, Queensland Department of Agriculture, Fisheries and Forestry, Hermitage Research Station, 604 Yangan Rd, Warwick, Qld 4370, Australia Alison B. Smith, School of Mathematics and Applied Statistics, Faculty of Informatics, University of Wollongong, Wollongong, Australia David R. Jordan, Queensland Alliance for Agriculture and Food Innovation, Hermitage Research Station, 604 Yangan Rd, Warwick, Qld 4370, Australia Brian R. Cullis, School of Mathematics and Applied Statistics, Faculty of Informatics, University of Wollongong, Wollongong, Australia Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description:    Neutral zone classifiers allow for a region of neutrality when there is inadequate information to assign a predicted class with suitable confidence. A neutral zone classifier is defined by classification regions that trade off the cost of an incorrect classification against the cost of remaining neutral. In this paper, we derive a Bayes neutral zone classifier and demonstrate that it outperforms previous neutral zone classifiers with respect to the expected cost of misclassifications and also with respect to computational complexity. We apply the neutral zone classifier to a microbial community profiling application in which no training data are available, thereby illustrating how it can be extended to unsupervised settings. This article has supplementary material online. Content Type Journal Article Pages 1-14 DOI 10.1007/s13253-012-0116-8 Authors Scott Benecke, Department of Statistics, University of California, Riverside, CA 92521, USA Daniel R. Jeske, Department of Statistics, University of California, Riverside, CA 92521, USA Paul Reugger, Department of Plant Pathology and Microbiology, University of California, Riverside, CA 92521, USA James Borneman, Department of Plant Pathology and Microbiology, University of California, Riverside, CA 92521, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description: Claus Thorn Ekstrom and Helle Sorensen: Statistical Data Analysis for the Life Sciences Content Type Journal Article Pages 1-2 DOI 10.1007/s13253-012-0113-y Authors Mi-Ok Kim, Division of Biostatistics and Epidemiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description: Richard E. Plant: Spatial Data Analysis in Ecology and Agriculture Using R Content Type Journal Article Pages 1-2 DOI 10.1007/s13253-012-0112-z Authors Dipankar Bandyopadhyay, Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description:    Projections of future climatic changes are a key input to the design of climate change mitigation and adaptation strategies. Current climate change projections are deeply uncertain. This uncertainty stems from several factors, including parametric and structural uncertainties. One common approach to characterize and, if possible, reduce these uncertainties is to confront (calibrate in a broad sense) the models with historical observations. Here, we analyze the problem of combining multiple climate models using Bayesian Model Averaging (BMA) to derive future projections and quantify uncertainty estimates of spatiotemporally resolved temperature hindcasts and projections. One advantage of the BMA approach is that it allows the assessment of the predictive skill of a model using the training data, which can help identify the better models and discard poor models. Previous BMA approaches have broken important new ground, but often neglected space–time dependencies and/or imposed prohibitive computational demands. Here we improve on the current state-of-the-art by incorporating space–time dependence while using historical data to estimate model weights. We achieve computational efficiency using a kernel mixing approach for representing a space–time process. One key advantage of our new approach is that it enables us to incorporate multiple sources of uncertainty and biases, while remaining computationally tractable for large data sets. We introduce and apply our approach using BMA to an ensemble of Global Circulation Model output from the Intergovernmental Panel on Climate Change Fourth Assessment Report of surface temperature on a grid of space–time locations. Content Type Journal Article Pages 606-628 DOI 10.1007/s13253-011-0069-3 Authors K. Sham Bhat, Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA Murali Haran, Department of Statistics, Pennsylvania State University, University Park, PA 16802, USA Adam Terando, Department of Biology, North Carolina State University, Raleigh, NC 27695, USA Klaus Keller, Department of Geosciences, Pennsylvania State University, University Park, PA 16802, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 4

Description:    The goal of this work is to characterize the annual temperature for regional climate models. Of interest for impacts studies, these profiles and the potential change in these profiles are a new way to describe climate change and the inherent uncertainty. To that end, we propose a Bayesian hierarchical spatial model to simultaneously model the temperature profile for the four seasons of the year, current and future. These profiles are then analyzed focusing on understanding how they change over time, how they vary spatially, and how they vary between five different regional climate models. The results show that for temperature, the regional models have different profile shapes depending on a number of factors including spatial location, driving climate model, and regional climate model. This article has supplementary material online. Content Type Journal Article Pages 571-585 DOI 10.1007/s13253-011-0072-8 Authors Tamara A. Greasby, Institute for Mathematics Applied to Geosciences, National Center for Atmospheric Research, Boulder, CO, USA Stephan R. Sain, Institute for Mathematics Applied to Geosciences, National Center for Atmospheric Research, Boulder, CO, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 4

Description:    Deterministic computer models or simulators are used regularly to assist researchers in understanding the behavior of complex physical systems when real-world observations are limited. However, simulators are often imperfect representations of physical systems and may introduce layers of uncertainty into model-based inferences that are hard to quantify. To formalize the use of expert judgment in assessing simulator uncertainty, Goldstein and Rougier in J. Stat. Plan. Inference 139:1221–1239 ( 2009 ) propose a method, called reification, that decomposes the discrepancy between simulator predictions and reality by an improved, hypothetical computer model known as a “reified simulator”. One criticism of reification is that validation is, at best, challenging; only expert critiques can validate the subjective judgments used to specify a reified simulator. For this paper, we develop a procedure to quantify the advantages of reification for fast, modular simulators. The procedure is explained and implemented within the context of a rainfall-runoff that was developed by Iorgulescu, Beven, and Musy in Hydrol. Process. 19:2557–2573 ( 2005 ). We show that reification leads to informed judgments of simulator uncertainty Content Type Journal Article Pages 513-530 DOI 10.1007/s13253-011-0075-5 Authors Leanna House, Department of Statistics, Virginia Tech, Blacksburg, VA 24061, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 4

Description:    Kernel-based models for space–time data offer a flexible and descriptive framework for studying atmospheric processes. Nonstationary and anisotropic covariance structures can be readily accommodated by allowing kernel parameters to vary over space and time. In addition, dimension reduction strategies make model fitting computationally feasible for large datasets. Fitting these models to data derived from instruments onboard satellites, which often contain significant amounts of missingness due to cloud cover and retrieval errors, can be difficult. In this paper, we propose to overcome the challenges of missing satellite-derived data by supplementing an analysis with output from a computer model, which contains valuable information about the space–time dependence structure of the process of interest. We illustrate our approach through a case study of aerosol optical depth across mainland Southeast Asia. We include a cross-validation study to assess the strengths and weaknesses of our approach. Content Type Journal Article Pages 495-512 DOI 10.1007/s13253-011-0068-4 Authors Catherine A. Calder, Department of Statistics, The Ohio State University, Columbus, OH, USA Candace Berrett, Department of Statistics, Brigham Young University, Provo, UT, USA Tao Shi, Department of Statistics, The Ohio State University, Columbus, OH, USA Ningchuan Xiao, Department of Geography, The Ohio State University, Columbus, OH, USA Darla K. Munroe, Department of Geography, The Ohio State University, Columbus, OH, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 4

Description:    The evaluation of physically based computer models for air quality applications is crucial to assist in control strategy selection. Selecting the wrong control strategy has costly economic and social consequences. The objective comparison of the means and variances of modeled air pollution concentrations with the ones obtained from observed field data is the common approach for the assessment of model performance. One drawback of this strategy is that it fails to calibrate properly the tails of the modeled air pollution distribution. Improving the ability of these numerical models to characterize high pollution events is of critical interest for air quality management. In this work we introduce an innovative framework to assess model performance, not only based on the two first moments of the model outputs and field data, but also on their entire distributions. Our approach also compares the spatial dependence and variability in two data sources. More specifically, we estimate the spatial-quantile functions for both the model output and field data, and we apply a nonlinear monotonic regression approach to the quantile functions taking into account the spatial dependence to compare the density functions of numerical models and field data. We use a Bayesian approach for estimation and fitting to characterize uncertainties in data and statistical models. We apply our methodology to assess the performance of the US Environmental Protection Agency (EPA) Community Multiscale Air Quality (CMAQ) model to characterize ozone ambient concentrations. Our approach shows a 50.23% reduction in the root mean square error (RMSE) compared to the default approach based on the first 2 moments of the model output and field data. Content Type Journal Article Pages 531-553 DOI 10.1007/s13253-011-0076-4 Authors Jingwen Zhou, Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA Montserrat Fuentes, Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA Jerry Davis, US Environmental Protection Agency, Research Triangle Park, NC 27511, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 4

Description:    Monitoring populations of hosts as well as insect vectors is an important part of agricultural and public health risk assessment. In applications where pathogen prevalence is likely low, it is common to test pools of subjects for the presence of infection, rather than to test subjects individually. This technique is known as pooled (group) testing. In this paper, we revisit the problem of estimating the population prevalence p from pooled testing, but we consider applications where inverse binomial sampling is used. Our work is unlike previous research in pooled testing, which has largely assumed a binomial model. Inverse sampling is natural to implement when there is a need to report estimates early on in the data collection process and has been used in individual testing applications when disease incidence is low. We consider point and interval estimation procedures for p in this new pooled testing setting, and we use example data sets from the literature to describe and to illustrate our methods. Content Type Journal Article Pages 70-87 DOI 10.1007/s13253-010-0036-4 Authors Nicholas A. Pritchard, Department of Mathematics and Statistics, Coastal Carolina University, Conway, SC 29528, USA Joshua M. Tebbs, Department of Statistics, University of South Carolina, Columbia, SC 29208, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 1

Description:    Hourly pedometer counts and irregularly measured concentration of the hormone progesterone were available for a large number of dairy cattle. A hidden semi-Markov was applied to this bivariate time-series data for the purposes of monitoring the reproductive status of cattle. In particular, the ability to identify oestrus is investigated as this is of great importance to farm management. Progesterone concentration is a more accurate but more expensive method than pedometer counts, and we evaluate the added benefits of a model that includes this variable. The resulting model is biologically sensible, but validation is difficult. We utilize some auxiliary data to demonstrate the model’s performance. Content Type Journal Article Pages 1-16 DOI 10.1007/s13253-010-0033-7 Authors Jared O’Connell, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, England UK Frede Aakmann Tøgersen, Modeling, Statistics and Risk Analysis, Vestas R&D, Alsvej, Denmark Nicolas C. Friggens, INRA UMR 791 Modélisation Systémique Appliquée aux Ruminants (MoSAR), AgroParisTech, Paris Cedex, France Peter Løvendahl, Department of Genetics and Biotechnology, Aarhus University, Århus, Denmark Søren Højsgaard, Department of Genetics and Biotechnology, Aarhus University, Århus, Denmark Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 1

Description:    Olfactometer experiments are used to study the responses of arthropods to potential attractants, for purposes such as understanding natural defenses of plants against their herbivores. Such experiments typically lead to multivariate data consisting of small correlated counts, which are overdispersed relative to standard models. In this paper models that account for the overdispersion under different hypotheses on insect behavior are described and illustrated with an example, and a graphical approach to discriminating among them is briefly discussed. Supplementary files giving technical computations, data and code are available online. Content Type Journal Article Pages 157-169 DOI 10.1007/s13253-010-0042-6 Authors A. C. Davison, Institute of Mathematics, Station 8, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland I. Ricard, Clinical Trial Unit, University Hospital Basel, Schanzenstrasse 55, 4031 Basel Switzerland Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 2

Description:    Capture–recapture (CR) models assume marked individuals remain at risk of capture, which may not be true if individuals lose their mark or emigrate definitively from the study area. Using a double-marking protocol, with a main and auxiliary mark, and both live encounters and dead recoveries at a large scale, partially frees CR models from this assumption. However, the auxiliary mark may fall off and its presence is often not mentioned when dead individuals are reported. We propose a new model to deal with heterogeneity of detection and uncertainty of the presence of an auxiliary mark in a multi-event framework. Our general model, based on a double-marking protocol, uses information from physical captures/recaptures, distant observations and main mark recoveries from dead animals. We applied our model to a 13-year data set of a harvested species, the Greater Snow Goose. We obtained seasonal survival estimates for adults of both sexes. Survival estimates differed between models where the presence of the auxiliary mark upon recovery was ignored versus those where the presence was accounted for. In the multi-event framework, seasonal survival estimates are no longer biased because the heterogeneity due to the presence of an auxiliary mark is accounted for in the estimation of recovery rates. Note: An illustration of the implementation of our model in E-SURGE is available online. Content Type Journal Article Pages 88-104 DOI 10.1007/s13253-010-0035-5 Authors C. Juillet, Centre d’Études Nordiques and Département de Biologie, Université Laval, Pavillon A. Vachon, 1045 avenue de la Médecine, Québec, QC G1V 0A6, Canada R. Choquet, Centre d’Écologie Fonctionnelle et Évolutive, Centre National de la Recherche Scientifique, Équipe Biométrie et Biologie des Populations, 1919 Route de Mende, 34293 Montpellier Cedex 5, France G. Gauthier, Centre d’Études Nordiques and Département de Biologie, Université Laval, Pavillon A. Vachon, 1045 avenue de la Médecine, Québec, QC G1V 0A6, Canada R. Pradel, Centre d’Écologie Fonctionnelle et Évolutive, Centre National de la Recherche Scientifique, Équipe Biométrie et Biologie des Populations, 1919 Route de Mende, 34293 Montpellier Cedex 5, France Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 1

Description:    Exact confidence intervals for variance components in linear mixed models rely heavily on normal distribution assumptions. If the random effects in the model are not normally distributed, then the true coverage probabilities of these conventional intervals may be erratic. In this paper we examine the performance of nonparametric bootstrap confidence intervals based on restricted maximum likelihood (REML) estimators. Asymptotic theory suggests that these intervals will achieve the nominal coverage value as the sample size increases. Incorporating a small-sample adjustment term in the bootstrap confidence interval construction process improves the performance of these intervals for small to intermediate sample sizes. Simulation studies suggest that the bootstrap standard method (with a transformation) and the bootstrap bias-corrected and accelerated (BC a ) method produce confidence intervals that have good coverage probabilities under a variety of distribution assumptions. For an interlaboratory comparison of mercury concentration in oyster tissue, a balanced one-way random effects model is used to quantify the proportion of the variation in mercury concentration that can be attributed to the laboratories. In this application the exact confidence interval using normal distribution theory produces misleading results and inferences based on nonparametric bootstrap procedures are more appropriate. Content Type Journal Article Pages 1-18 DOI 10.1007/s13253-012-0087-9 Authors Brent D. Burch, Department of Mathematics and Statistics, Northern Arizona University, Flagstaff, AZ 86011-5717, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description:    The paper provides a systematic approach to designing the laboratory phase of a multiphase experiment, taking into account previous phases. General principles are outlined for experiments in which orthogonal designs can be employed. Multiphase experiments occur widely, although their multiphase nature is often not recognized. The need to randomize the material produced from the first phase in the laboratory phase is emphasized. Factor-allocation diagrams are used to depict the randomizations in a design and the use of skeleton analysis-of-variance (ANOVA) tables to evaluate their properties discussed. The methods are illustrated using a scenario and a case study. A basis for categorizing designs is suggested. This article has supplementary material online. Content Type Journal Article Pages 422-450 DOI 10.1007/s13253-011-0060-z Authors C. J. Brien, School of Mathematics and Statistics, University of South Australia, Mawson Lakes Boulevard, Mawson Lakes, SA 5095, Australia B. D. Harch, Ecosciences Precinct, CSIRO Sustainable Agriculture Flagship, GPO Box 2583, Brisbane, Qld 4001, Australia R. L. Correll, Rho Environmentrics, PO Box 366, Highgate, SA 2063, Australia R. A. Bailey, School of Mathematical Sciences, Queen Mary University of London, Mile End Road, London, E1 4NS UK Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 3

Description:    An objective for applying a Crop Simulation Model (CSM) in precision agriculture is to explain the spatial variability of crop performance and to help guide decisions related to the site-specific management of crop inputs. CSMs require inputs related to soil, climate, management, and crop genetic information to simulate crop yield. In practice, however, measuring these inputs at the desired high spatial resolution is prohibitively expensive. We propose a Bayesian modeling framework that melds a CSM with sparse data from a yield monitoring system to deliver location specific posterior predicted distributions of yield and associated unobserved spatially varying CSM parameter inputs. These products facilitate exploration of process-based explanations for yield variability. The proposed Bayesian melding model consists of a systemic component representing output from the physical model and a residual spatial process that compensates for the bias in the physical model. The spatially varying inputs to the systemic component arise from a multivariate Gaussian process, while the residual component is modeled using a univariate Gaussian process. Due to the large number of observed locations in the motivating dataset, we seek dimension reduction using low-rank predictive processes to ease the computational burden. The proposed model is illustrated using the Crop Environment Resources Synthesis (CERES)-Wheat CSM and wheat yield data collected in Foggia, Italy. Content Type Journal Article Pages 453-474 DOI 10.1007/s13253-011-0070-x Authors Andrew O. Finley, Departments of Forestry and Geography, Michigan State University, East Lansing, MI, USA Sudipto Banerjee, School of Public Health, University of Minnesota, Minneapolis, MN, USA Bruno Basso, Department of Crop Systems, Forestry and Environmental Sciences, University of Basilicata, Potenza, Italy Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 4

Description:    When making predictions with complex simulators it can be important to quantify the various sources of uncertainty. Errors in the structural specification of the simulator, for example due to missing processes or incorrect mathematical specification, can be a major source of uncertainty, but are often ignored. We introduce a methodology for inferring the discrepancy between the simulator and the system in discrete-time dynamical simulators. We assume a structural form for the discrepancy function, and show how to infer the maximum-likelihood parameter estimates using a particle filter embedded within a Monte Carlo expectation maximization (MCEM) algorithm. We illustrate the method on a conceptual rainfall-runoff simulator (logSPM) used to model the Abercrombie catchment in Australia. We assess the simulator and discrepancy model on the basis of their predictive performance using proper scoring rules. This article has supplementary material online. Content Type Journal Article Pages 554-570 DOI 10.1007/s13253-011-0077-3 Authors Richard D. Wilkinson, School of Mathematical Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD UK Michail Vrettas, School of Mathematical Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD UK Dan Cornford, Department of Engineering and Applied Science, Aston University, Aston Triangle, Birmingham, B4 7ET UK Jeremy E. Oakley, School of Mathematics and Statistics, University of Sheffield, Sheffield, S3 7RH UK Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 4

Description:    We consider the problem of forecasting future regional climate. Our method is based on blending different members of an ensemble of regional climate model (RCM) simulations while accounting for the discrepancies between these simulations, under present day conditions, and observational records for the recent past. To this end, we develop Bayesian space-time models that assess the discrepancies between climate model simulations and observational records. Those discrepancies are then propagated into the future to obtain blended forecasts of 21st century climate. The model allows for location-dependent spatial heterogeneities, providing local comparisons between the different simulations. Additionally, we estimate the different modes of spatial variability, and use the climate model-specific coefficients of the spatial factors for comparisons. We focus on regional climate model simulations performed in the context of the North American Regional Climate Change Assessment Program (NARCCAP). We consider, in particular, simulations from RegCM3 using three different forcings: NCEP, GFDL and CGCM3. We use simulations for two time periods: current climate conditions, covering 1971 to 2000, and future climate conditions under the SRES A2 emissions scenario, covering 2041 to 2070. We investigate yearly mean summer temperature for a domain in the South West of the United States. The results indicated the RCM simulations underestimate the mean summer temperature increase for most of the domain compared to our model. Content Type Journal Article Pages 586-605 DOI 10.1007/s13253-011-0074-6 Authors Esther Salazar, Department of Electrical and Computer Engineering, Duke University, Durham, USA Bruno Sansó, Department of Applied Mathematics and Statistics, University of California Santa Cruz, Santa Cruz, USA Andrew O. Finley, Departments of Forestry and Geography, Michigan State University, East Lansing, USA Dorit Hammerling, Department of Environmental Engineering, University of Michigan, Ann Arbor, USA Ingelin Steinsland, Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway Xia Wang, Department of Mathematical Sciences, University of Cincinnati, Cincinnati, USA Paul Delamater, Department of Geography, Michigan State University, East Lansing, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 4

Description:    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. Content Type Journal Article Pages 389-408 DOI 10.1007/s13253-011-0059-5 Authors J. L. Laake, National Marine Mammal Laboratory, Alaska Fisheries Sciences Center, NMFS, Seattle, WA 98115, USA B. A. Collier, Institute of Renewable Natural Resources, Texas A&M University, College Station, TX 77843, USA M. L. Morrison, Department of Wildlife and Fisheries Sciences, Texas A&M University, College Station, TX 77843, USA R. N. Wilkins, Institute of Renewable Natural Resources, Texas A&M University, College Station, TX 77843, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 3

Description:    A statistically efficient approach is adopted for modeling spatial time-series of large data sets. The estimation of the main diagnostic tool such as the likelihood function in Gaussian spatiotemporal models is a cumbersome task when using extended spatial time-series such as air pollution. Here, using the Innovation Algorithm, we manage to compute it for many spatiotemporal specifications. These specifications refer to the spatial periodic-trend, the spatial autoregressive moving average, the spatial autoregressive integrated and fractionally integrated moving average Gaussian models. Our method is applied to daily pollutants over a large metropolitan area like Athens. In the applied part of our paper, we first diagnose temporal and spatial structures of data using non-likelihood based criteria, such as the empirical autocorrelation and covariance functions. Second, we use likelihood and non-likelihood based criteria to select a spatiotemporal model among various specifications. Finally, using kriging we regionalize the resulting parameter estimates of the best-fitted model in space at any unmonitored location in the Athens region. The results show that a specific autoregressive integrated moving average spatiotemporal model can optimally perform in within and out of spatial sample estimation. Supplemental materials for this article are available from the journal website. Content Type Journal Article Pages 371-388 DOI 10.1007/s13253-011-0057-7 Authors Georgios Tsiotas, Department of Economics, University of Crete, Panepistimioupolis, Rethymnon, 74100 Greece Athanassios A. Argiriou, Department of Physics, Section of Applied Physics, University of Patras, 265 00 Patras, Greece Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 3

Description:    In studies about the potential distribution of ecological niches, only the presence of the species of interest is usually recorded. Pseudo-absences are sampled from the study area in order to avoid biased estimates and predictions. For cases in which, instead of the mere presence, a continuous abundance index is recorded, we derive a two-part model for semicontinuous (i.e., positive with excess zeros) data which explicitly takes into account uncertainty about the sampled zeros. Our model is a direct extension of the one of Ward et al. (Biometrics 65, 554–563, 2009 ). It is fit in a Bayesian framework, which has many advantages over the maximum likelihood approach of Ward et al. ( 2009 ), the most important of which is that the prevalence of the species does not need to be known in advance. We illustrate our approach with real data arising from an original study aiming at the prediction of the potential distribution of the Taxus baccata in two central Italian regions. Supplemental materials giving detailed proofs of propositions, tables and code are available online. Content Type Journal Article Pages 339-356 DOI 10.1007/s13253-011-0054-x Authors B. Di Lorenzo, Department of Statistic, Probability and Applied Statistics, Sapienza – University of Rome, Rome, 00185 Italy A. Farcomeni, Department of Infectious Diseases and Public Health, Sapienza – University of Rome, Rome, 00185 Italy N. Golini, Department of Statistic, Probability and Applied Statistics, Sapienza – University of Rome, Rome, 00185 Italy Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 3

Description:    Functional regression is a natural tool for exploring the potential impact of the physical environment (continuously monitored) on biological processes (often only assessed annually). This paper explores the potential use of functional regression analysis and the closely related functional principal component analysis for studying the relationship between river flow (continuously monitored) and salmon abundance (measured annually). The specific example involves a depressed sockeye salmon population in Rivers Inlet, BC. Particular attention is given to (i) the role of subject matter expertise and cross-validation techniques in guiding decisions on basis functions and smoothing parameters, and (ii) the importance of restricting the time domain for the continuously monitored variable to a scientifically meaningful period of time. In addition, we derive a joint confidence region for the functional regression coefficient function and discuss its use relative to the more commonly used pointwise confidence intervals. The analysis points to a substantial negative correlation between early spring river flow and marine survival of the sockeye salmon that subsequently migrate down the inlet. Content Type Journal Article Pages 282-300 DOI 10.1007/s13253-010-0049-z Authors L. M. Ainsworth, Department of Statistics & Actuarial Science, Simon Fraser University, Burnaby, BC, Canada V5A 1S6 R. Routledge, Department of Statistics & Actuarial Science, Simon Fraser University, Burnaby, BC, Canada V5A 1S6 J. Cao, Department of Statistics & Actuarial Science, Simon Fraser University, Burnaby, BC, Canada V5A 1S6 Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 2

Description:    Trials in the early stages of selection are often subject to variation arising from spatial variability and interplot competition, which can seriously bias the assessment of varietal performance and reduce genetic progress. An approach to jointly model both sources of bias is presented. It models genotypic and residual competition and also global and extraneous spatial variation. Variety effects were considered random and residual maximum likelihood was used for parameter estimation. Competition at the residual level was examined using two special simultaneous autoregressive models. An equal-roots second-order autoregressive (EAR(2)) model is proposed for trials where competition is dominant. An equal-roots third-order autoregressive (EAR(3)) model allows for competition and spatial variability. These models are applied to two yield data sets from an Australian sugarcane selection program. One data set is in the paper and the other is in supplementary material available online. To determine the effect of simultaneously adjusting for spatial variability and interplot competition on selection, the percentages of superior varieties in common in the top 15% for the joint model and classical approaches were compared. Agreement between the two approaches was 45 and 84%. Hence, for some trials there are large differences in varieties advanced to the next stage of selection. Content Type Journal Article Pages 269-281 DOI 10.1007/s13253-010-0051-5 Authors Joanne K. Stringer, BSES Limited, P.O. Box 86, Indooroopilly, Qld 4068, Australia Brian R. Cullis, Faculty of Informatics, School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, NSW 2500, Australia Robin Thompson, IACR Rothamsted, Harpenden, AL5 2JQ UK Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 2

Description:    Traditional analyses of capture–recapture data are based on likelihood functions that explicitly integrate out all missing data. We use a complete data likelihood (CDL) to show how a wide range of capture–recapture models can be easily fitted using readily available software JAGS/BUGS even when there are individual-specific time-varying covariates. The models we describe extend those that condition on first capture to include abundance parameters, or parameters related to abundance, such as population size, birth rates or lifetime. The use of a CDL means that any missing data, including uncertain individual covariates, can be included in models without the need for customized likelihood functions. This approach also facilitates modeling processes of demographic interest rather than the complexities caused by non-ignorable missing data. We illustrate using two examples, (i) open population modeling in the presence of a censored time-varying individual covariate in a full robust design, and (ii) full open population multi-state modeling in the presence of a partially observed categorical variable. Supplemental materials for this article are available online. Content Type Journal Article Pages 253-268 DOI 10.1007/s13253-010-0052-4 Authors Matthew R. Schofield, Department of Statistics, Columbia University, New York, NY, USA Richard J. Barker, Department of Mathematics and Statistics, University of Otago, P.O. Box 56, Dunedin, New Zealand Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 2

Description:    In this paper we describe novel methodology for evaluating competition among strains of Rhizobium bacteria which can be found naturally occurring in or can be introduced into soil. Rhizobia can occupy nodules on the roots of legume plants allowing the plant to ‘fix’ atmospheric nitrogen. Our model defines competitive outcomes for a community (the multinomial count of nodules occupied by each strain at the end of a time period) relative to the past state of the community (the proportion of each strain present at the beginning of the time period) and incorporates this prior information in the analysis. Our approach for assessing competition provides an analogy to multivariate methods for continuous responses in competition studies and an alternative to univariate methods for discrete responses that respects the multivariate nature of the data. It can also handle zero values in the multinomial response providing an alternative to compositional data analysis methods, which traditionally have not been able to facilitate zero values. The proposed experimental design is based on the simplex design and the model is an extension of multinomial baseline category logit models that includes multiple offsets and random terms to allow for correlation among clustered responses. Supplemental materials for this article are available from the journal website. Content Type Journal Article Pages 409-421 DOI 10.1007/s13253-011-0058-6 Authors C. Brophy, Department of Mathematics & Statistics, National University of Ireland Maynooth, Maynooth, Co. Kildare, Ireland J. Connolly, UCD School of Mathematical Sciences, Environmental & Ecological Modelling Group, University College Dublin, Belfield, Dublin 4, Ireland I. L. Fagerli, Department of Arctic and Marine Biology, University of Tromsø, 9037 Tromsø, Norway S. Duodu, National Veterinary Institute, P.O. Box 750, Sentrum, 0106 Oslo, Norway M. M. Svenning, Department of Arctic and Marine Biology, University of Tromsø, 9037 Tromsø, Norway Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 3

Description:    In this paper, a new approach to independent component analysis (ICA) for three-way data is considered. The rotational freedom of the three-mode component analysis (Tucker3) model is exploited to implement ICA in one mode of the data. The performance of the proposed approach is evaluated by means of numerical experiments. An illustration with real data from atmospheric science is presented, where the first mode is spatial location, the second is time and the third is a set of different meteorological variables representing geopotential heights at various vertical pressure levels. The results show that the three-mode decomposition finds spatial patterns of climate anomalies which can be interpreted in a meteorological sense and as such gives an insightful low-dimensional representation of the data. Content Type Journal Article Pages 319-338 DOI 10.1007/s13253-011-0055-9 Authors Steffen Unkel, Department of Mathematics and Statistics, Faculty of Mathematics, Computing & Technology, The Open University, Walton Hall, Milton Keynes, MK7 6AA UK Abdel Hannachi, Department of Meteorology, Stockholm University, Stockholm, Sweden Nickolay T. Trendafilov, Department of Mathematics and Statistics, Faculty of Mathematics, Computing & Technology, The Open University, Walton Hall, Milton Keynes, MK7 6AA UK Ian T. Jolliffe, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 3

Description:    The proportion of sampling sites occupied by a species is a concept of interest in ecology and biodiversity conservation. Occupancy surveys based on collecting detection data along transects have become increasingly popular to monitor some species. To date, the analysis of such data has been carried out by discretizing the data, dividing the transects into discrete segments. Here we propose alternative occupancy models which describe the detection process as a continuous point process. These models provide a more natural description of the data and eliminate the need to divide transects into segments, which can be arbitrary and may lead to increased bias in the estimator of occupancy or increased chances of obtaining estimates on the boundary of the parameter space. We present a model that assumes independence between detections and an alternative model that describes the detection process as a Markov modulated Poisson process to account for potential clustering in the detections. The utility of these models is illustrated with the analysis of data from a recent survey of the Sumatran tiger Panthera tigris sumatrae . The models can also be applied to surveys that collect continuous data in time, such as those based on the use of camera-trap devices. Supplementary materials for this article are available online. Content Type Journal Article Pages 301-317 DOI 10.1007/s13253-010-0053-3 Authors Gurutzeta Guillera-Arroita, School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, CT2 7NZ UK Byron J. T. Morgan, School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, CT2 7NZ UK Martin S. Ridout, School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, CT2 7NZ UK Matthew Linkie, Fauna & Flora International (FFI) in Aceh, Jln Tgk. Chik Dipasi No. 50, Desa Limpok, Darussalam, Aceh Besar, Nanggroe Aceh Darussalam, 23373 Indonesia Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 3

Description: Editorial Collaborators Content Type Journal Article Pages 578-579 DOI 10.1007/s13253-010-0048-0 Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 15 Journal Issue Volume 15, Number 4

Description: Guest Editors’ Introduction to the Special Issue on “Computer Models and Spatial Statistics for Environmental Science” Content Type Journal Article Pages 451-452 DOI 10.1007/s13253-011-0071-9 Authors Brian J. Reich, Department of Statistics, North Carolina State University, 2311 Stinson Drive 4264 SAS Hall, Box 8203, Raleigh, NC 27695, USA Murali Haran, Department of Statistics, Penn State, 326 Thomas Building, University Park, PA 16802, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 4

Description:    Traditional spatial linear regression models assume that the mean of the response is a linear combination of predictors measured at the same location as the response. In spatial applications, however, it seems plausible that neighboring predictors can also inform about the response. This article proposes using unobserved kernel averaged predictors in such regressions. The kernels are parametric introducing additional parameters that are estimated with the data. Properties and challenges of using kernel averaged predictors within a regression model are detailed in the simple case of a univariate response and a single predictor. Additionally, extensions to multiple predictors and generalized linear models are discussed. The methods are demonstrated using a data set of dew duration and shrub density. Supplemental materials are available online. Content Type Journal Article Pages 233-252 DOI 10.1007/s13253-010-0050-6 Authors Matthew J. Heaton, Department of Statistical Science, Duke University, Box 90251, Durham, NC 27708-0251, USA Alan E. Gelfand, Department of Statistical Science, Duke University, Box 90251, Durham, NC 27708-0251, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 2

Description:    The analysis of animal movement and resource use has become a standard tool in the study of animal ecology. Telemetry devices have become quite sophisticated in terms of overall size and data collecting capacity. Statistical methods to analyze movement have responded, becoming ever more complex, often relying on state-space modeling. Estimation of movement metrics such as utilization distributions have not followed suit, relying primarily on kernel density estimation. Here we consider a method for making inference about space use that is free of all of the major problems associated with kernel density estimation of utilization distributions such as autocorrelation, irregular time gaps, and error in observed locations. Our proposed method is based on a data augmentation approach that defines use as a summary of the complete path of the animal which is only partially observed. We use a sample from the posterior distribution of the complete path to construct a posterior sample for the metric of interest. Three basic importance sampling based methods for sampling from the posterior distribution of the path are proposed and compared. We demonstrate the augmentation approach by estimating a spatial map of diving intensity for female northern fur seals in the Pribilof Islands, Alaska. Content Type Journal Article Pages 357-370 DOI 10.1007/s13253-011-0056-8 Authors Devin S. Johnson, National Marine Mammal Laboratory, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA, 7600 Sand Point Way NE, Seattle, WA 98115, USA Josh M. London, National Marine Mammal Laboratory, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA, 7600 Sand Point Way NE, Seattle, WA 98115, USA Carey E. Kuhn, National Marine Mammal Laboratory, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA, 7600 Sand Point Way NE, Seattle, WA 98115, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 3

Description:    This article is motivated by the need of biological and environmental scientists to fit a popular nonlinear model to binary dose-response data. The four-parameter logistic model, also known as the Hill model, generalizes the usual logistic regression model to allow the lower and upper response asymptotes to be greater than zero and less than one, respectively. This article develops an EM algorithm, which is naturally suited for maximum likelihood estimation under the Hill model after conceptualizing the problem as a mixture of subpopulations in which some subjects respond regardless of dose, some fail to respond regardless of dose, and some respond with a probability that depends on dose. The EM algorithm leads to a pair of functionally independent two-parameter optimizations and is easy to program. Not only can this approach be computationally appealing compared to simultaneous optimization with respect to all four parameters, but it also facilitates estimating covariances, incorporating predictors, and imposing constraints. This article is motivated by, and the EM algorithm is illustrated with, data from a toxicology study of the dose effects of selenium on the death rates of flies. Other biological and environmental applications, as well as medical and agricultural applications, are also described briefly. Computer code for implementing the EM algorithm is available as supplemental material online. Content Type Journal Article Pages 221-232 DOI 10.1007/s13253-010-0045-3 Authors Gregg E. Dinse, Biostatistics Branch, National Institute of Environmental Health Sciences, Mail Drop A3-03, P.O. Box 12233, Research Triangle Park, NC 27709, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 2

Description:    In the design of clinical trials involving fish observed over time in tanks, there may be advantages in housing several treatment groups within the same tank. In particular, such “within-tank” designs will be more efficient than designs with treatment groups in separate tanks when substantial between-tank variability is expected. One potential problem with within-tank designs is that it may not be possible to include all treatments in one tank; in statistical terms this means that the blocks (tanks) are incomplete. In incomplete block designs, there may be a concern that the treatments present in the same tank (denoted here as “neighbors”) affect each other in their performance; thus the need for an assessment of neighbor effects. In this paper, we propose two statistical approaches to assess and account for neighbor effects. The first approach is based on a non-linear mixed model and the second involves cross-classified and multiple membership models. Both approaches are illustrated on simulated data as well as data from a clinical ISAV (Infectious Salmon Anaemia Virus) trial; corresponding computer code is available online. The simulation studies demonstrated that both models show promise in capturing neighbor treatment effects of the type assumed for the models, whenever such neighbor effects are of at least moderate magnitude. In the absence of or with low magnitudes of neighbor effects, the non-linear mixed model faced numerical challenges and produced noisy results. One version of the cross-classified and multiple membership model was shown to depend strongly on prior information about variance-covariance parameters for datasets similar to the ISAV data. Analyses of the ISAV trial data by both models did not provide any evidence of substantial neighbor effects. Content Type Journal Article Pages 202-220 DOI 10.1007/s13253-010-0043-5 Authors Elmabrok Masaoud, Centre for Veterinary Epidemiological Research, University of Prince Edward Island, 550 University Avenue, Charlottetown, PE C1A 4P3, Canada Henrik Stryhn, Centre for Veterinary Epidemiological Research, University of Prince Edward Island, 550 University Avenue, Charlottetown, PE C1A 4P3, Canada Shona Whyte, Centre for Veterinary Epidemiological Research, University of Prince Edward Island, 550 University Avenue, Charlottetown, PE C1A 4P3, Canada William J. Browne, School of Clinical Veterinary Sciences, University of Bristol, Langford, BS40 5DU UK Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 2

Description:    Motivated by a problem in describing forest nitrogen cycling, in this paper we explore regression models for spatial images. Specifically, we present a functional concurrent linear model with varying coefficients for two-dimensional spatial images. To address overparameterization issues, the parameter surfaces in this model are transformed into the wavelet domain and a sparse representation is found by using a large-scale l 1 constrained least squares algorithm. Once the sparse representation is identified, an inverse wavelet transform is applied to obtain the estimated parameter surfaces. The optimal penalization term in the objective function is determined using the Bayesian Information Criterion (BIC) and we introduce measures of model quality. Our model is versatile and can be applied to both single and multiple replicate cases. Content Type Journal Article Pages 105-130 DOI 10.1007/s13253-010-0047-1 Authors Jun Zhang, Statistical and Applied Mathematical Sciences Institute, 19 T.W. Alexander Drive, Research Triangle Park, NC 27709-4006, USA Murray K. Clayton, Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, USA Philip A. Townsend, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 1

Description:    We suggest a simple isobole analysis for binary mixture toxicity experiments. The analysis is based on estimated logarithmic effect concentrations and their corresponding standard errors. The suggested model allows for synergism/antagonism and incorporates within-mixture variation as well as between-mixture variation in a random effects model. The likelihood ratio test for the hypothesis of concentration addition (CA) is examined, in particular its small sample properties. We study two datasets on the joint effect of acifluorfen versus diquat and glyphosate versus mechlorprop, respectively, on the growth of the aquatic macrophyte Lemna minor . Content Type Journal Article Pages 562-577 DOI 10.1007/s13253-010-0041-7 Authors Helle Sørensen, Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark Nina Cedergreen, Department of Basic Sciences and Environment, University of Copenhagen (LIFE), Copenhagen, Denmark Jens C. Streibig, Department of Agriculture and Ecology, University of Copenhagen (LIFE), Copenhagen, Denmark Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 15 Journal Issue Volume 15, Number 4

Description:    There is increasing scientific interest in studying the spatial distribution of species abundance in relation to environmental variability. Jellyfish in particular have received considerable attention in the literature and media due to regional population increases and abrupt changes in distribution. Jellyfish distribution and abundance data, like many biological datasets, are characterized by an excess of zero counts or nonstationary processes, which hampers their analyses by standard statistical methods. Here we further develop a recently proposed statistical framework, the constrained zero-inflated generalized additive model (COZIGAM), and apply it to a spatio-temporal dataset of jellyfish biomass in the Bering Sea. Our analyses indicate systematic spatial variation in the process that causes the zero inflation. Moreover, we show strong evidence of a range expansion of jellyfish from the southeastern to the northwestern portion of the survey area beginning in 1991. The proposed methodologies could be readily applied to ecological data in which zero inflation and spatio-temporal nonstationarity are suspected, such as data describing species distribution in relation to changes of climate-driven environmental variables. Some supplemental materials including an animation of jellyfish annual biomass and web appendices are available online. Content Type Journal Article Pages 185-201 DOI 10.1007/s13253-010-0044-4 Authors Hai Liu, Division of Biostatistics, Indiana University School of Medicine, Indianapolis, IN 46202, USA Lorenzo Ciannelli, College of Oceanic and Atmospheric Sciences, Oregon State University, Corvallis, OR 97331, USA Mary Beth Decker, Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA Carol Ladd, Pacific Marine Environmental Laboratory, NOAA, Seattle, WA 98115, USA Kung-Sik Chan, Department of Statistics and Actuarial Science, University of Iowa, Iowa City, IA 52242, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 2

Description:    Association analysis in important crop species has generated heightened interest for its potential in dissecting complex traits by utilizing diverse mapping populations. However, the mixed linear model approach is currently limited to single marker analysis, which is not suitable for studying multiple QTL effects, epistasis and gene by environment interactions. In this paper, we propose the adaptive mixed LASSO method that can incorporate a large number of predictors (genetic markers, epistatic effects, environmental covariates, and gene by environment interactions) while simultaneously accounting for the population structure. We show that the adaptive mixed LASSO estimator possesses the oracle property of adaptive LASSO. Algorithms are developed to iteratively estimate the regression coefficients and variance components. Our results demonstrate that the adaptive mixed LASSO method is very promising in modeling multiple genetic effects when a large number of markers are available and the population structure cannot be ignored. It is expected to be a powerful tool for studying the architecture of complex traits in important plant species. Supplemental materials for this article are available from the journal website. Content Type Journal Article Pages 170-184 DOI 10.1007/s13253-010-0046-2 Authors Dong Wang, Department of Statistics, University of Nebraska, Lincoln, NE 68583, USA Kent M. Eskridge, Department of Statistics, University of Nebraska, Lincoln, NE 68583, USA Jose Crossa, International Maize and Wheat Improvement Center, 06600 Mexico, DF Mexico Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 2

Description:    We explore the ability of a process-based space–time model to decompose 8-hour ozone on a given day and site into parts attributable to local emissions and regional transport, to provide space–time predictions, and to assess the efficacy of past and future emission controls. We model ozone as created plus transported plus an error with seasonally varying spatial covariance parameters. Created ozone is a function of the observed NO x concentration, the latent VOC concentration, and solar radiation surrogates. Transported ozone is a weighted average of the ozone observed at all sites on the previous day, where the weights are a function of wind speed and direction. The latent VOC process mean includes emissions, temperature, and a workday indicator, and the error has seasonally varying spatial covariance parameters. Using likelihood methods, we fit the model and obtain one set of predictions appropriate for prediction backward in time, and another appropriate for predicting under hypothetical emission scenarios. The first set of predictions has a lower root-mean-squared error (RMSE) when compared to point observations than do the 36 km gridcell averages from the Community Mesoscale Air Quality Model (CMAQ) used by the EPA; the second set has the same RMSE as CMAQ, but under-predicts high ozone values. Content Type Journal Article Pages 17-44 DOI 10.1007/s13253-010-0028-4 Authors A. J. Nail, Department of Statistics, North Carolina State University, Raleigh, NC USA J. M. Hughes-Oliver, Department of Statistics, North Carolina State University, Raleigh, NC USA J. F. Monahan, Department of Statistics, North Carolina State University, Raleigh, NC USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 1

Description:    We present an approach for estimating physical parameters in nonlinear models that relies on an approximation to the mechanistic model itself for computational efficiency. The proposed methodology is validated and applied in two different modeling scenarios: (a) Simulation and (b) lower trophic level ocean ecosystem model. The approach we develop relies on the ability to predict right singular vectors (resulting from a decomposition of computer model experimental output) based on the computer model input and an experimental set of parameters. Critically, we model the right singular vectors in terms of the model parameters via a nonlinear statistical model. Specifically, we focus our attention on first-order models of these right singular vectors rather than the second-order (covariance) structure. Content Type Journal Article Pages 475-494 DOI 10.1007/s13253-011-0073-7 Authors Mevin B. Hooten, Colorado Cooperative Fish and Wildlife Research Unit, U.S. Geological Survey, Fort Collins, CO, USA William B. Leeds, Department of Statistics, University of Missouri, Columbia, MO, USA Jerome Fiechter, Ocean Sciences Department, University of California – Santa Cruz, Santa Cruz, CA, USA Christopher K. Wikle, Department of Statistics, University of Missouri, Columbia, MO, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 4

Description: Erratum to: Species Occupancy Modeling for Detection Data Collected Along a Transect Content Type Journal Article Category Erratum Pages 318-318 DOI 10.1007/s13253-011-0061-y Authors Gurutzeta Guillera-Arroita, School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, CT2 7NZ UK Byron J. T. Morgan, School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, CT2 7NZ UK Martin S. Ridout, School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, CT2 7NZ UK Matthew Linkie, Fauna & Flora International (FFI) in Aceh, Jln Tgk. Chik Dipasi No. 50, Desa Limpok, Darussalam, Aceh Besar, Nanggroe Aceh Darussalam, 23373 Indonesia Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117 Journal Volume Volume 16 Journal Issue Volume 16, Number 3

Description: Erratum to: The Role of Weather in Meningitis Outbreaks in Navrongo, Ghana: A Generalized Additive Modeling Approach Content Type Journal Article Category Erratum Pages 1-1 DOI 10.1007/s13253-012-0109-7 Authors Vanja Dukić, Department of Applied Mathematics, University of Colorado, Boulder, CO, USA Mary Hayden, National Center of Atmospheric Research, Boulder, CO, USA Abudulai Adams Forgor, War Memorial Hospital, Ghana Health Service, Navrongo, Ghana Tom Hopson, National Center of Atmospheric Research, Boulder, CO, USA Patricia Akweongo, Department of Health Policy, Planning & Management, School of Public Health, University of Ghana, Legon, Accra, Ghana Abraham Hodgson, Ghana Health Service, Navrongo, Ghana Andrew Monaghan, National Center of Atmospheric Research, Boulder, CO, USA Christine Wiedinmyer, National Center of Atmospheric Research, Boulder, CO, USA Tom Yoksas, University Corporation for Atmospheric Research, Boulder, CO, USA Madeleine C. Thomson, International Research Institute for Climate and Society, and Mailman School of Public Health, Columbia University, New York City, NY, USA Sylwia Trzaska, Columbia University, New York City, NY, USA Raj Pandya, University Corporation for Atmospheric Research, Boulder, CO, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description: Guest Editors’ Introduction to the Special Issue on Climate Change and Human Health Content Type Journal Article Pages 1-2 DOI 10.1007/s13253-012-0106-x Authors Roger D. Peng, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe St, Baltimore, MD 21205, USA Bo Li, Department of Statistics, Purdue University, 250 N. University St, West Lafayette, IN 47907, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description:    One challenge for statisticians and epidemiologists in projecting the future health risks of climate change is how to estimate exposure-response relationships when temperatures are higher than at present. Low dose extrapolation has been an area of rich study, resulting in well-defined methods and best practices. A primary difference between high dose and low dose extrapolation of exposure-response relationships is that low dose extrapolation is bounded at no exposure and no (or a baseline) response. With climate change altering weather variables and their variability beyond historical values, the highest future exposures in a region are projected to be higher than current experience. Modelers of the health risks of high temperatures are making assumptions about human responses associated with exposures outside the range of their data; these assumptions significantly affect the magnitude of projected future risks. Further, projections are affected by adaptation assumptions; we explore no adaptation (extrapolated response); individual (physiological) adaptation; and community adaptation. We present an example suggesting that linear models can make poor predictions of observations when no adaptation is assumed. Assumptions of the effects of weather above what has been observed needs to be more transparent in future studies. Statistical simulation studies could guide public health researchers in identifying best practices and reducing bias in projecting risks associated with extreme temperatures. Epidemiological studies should evaluate the extent and time required for adaptation, as well as the benefits of public health interventions. Content Type Journal Article Pages 1-15 DOI 10.1007/s13253-012-0104-z Authors Joacim Rocklöv, Department of Public Health and Clinical Medicine, Epidemiology and Global Health, Umeå University, Umeå, Sweden Kristie L. Ebi, Department of Medicine, Stanford University, Stanford, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description:    The timing and size of many infectious disease outbreaks depend on climatic influences. Meningitis is an example of such a disease. Every year countries in the so-called African meningitis belt are afflicted with meningococcal meningitis disease outbreaks. The timing of these outbreaks coincide with the dry season that starts in February and ends in late May. There are two main hypotheses about this strong seasonal effect. The first hypothesis assumes that during the dry season there is an increase in the risk that an individual will transition from being an asymptomatic carrier to having invasive disease. The second hypothesis states that the incidence of meningitis increases due to higher transmission of the infection during the dry season. These two biological hypotheses suggest dynamics that would necessitate different public health responses: the first would result in broadly correlated outbreak dynamics, and thus a regional vaccination response; the second would result in locally correlated outbreaks, spreading from location to location, for which a localized response may be effective in containing regional spread. In this paper, we develop a statistical model to investigate these hypotheses. Easily interpretable parameters of the model allow us to study and compare differences in the attack rates, rates of transmission and the possible underlying environmental effect during the dry and non-dry seasons. Standard maximum likelihood or Bayesian inference for this model is infeasible as there are potentially tens of thousands of latent variables in the model and each evaluation of the likelihood is expensive. We therefore propose an approximate Bayesian computation (ABC) approach to infer the unknown parameters. Using simulated data examples, we demonstrate that it is possible to learn about some of the important parameters of our model using our methodology. We apply our modeling and inferential approach to data on cases of meningitis for 34 communities in Nigeria from Medecins Sans Frontières (MSF) and World Health Organization (WHO) for 2009. For this particular data set we are able to find weak evidence in favor of the first hypothesis, suggesting a regional vaccination response. Content Type Journal Article Pages 1-22 DOI 10.1007/s13253-012-0101-2 Authors Roman Jandarov, Department of Statistics, Pennsylvania State University, University Park, PA 16802, USA Murali Haran, Department of Statistics, Pennsylvania State University, University Park, PA 16802, USA Matthew Ferrari, Department of Entomology and Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA 16802, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description:    We consider spatial point pattern data that have been observed repeatedly over a period of time in an inhomogeneous environment. Each spatial point pattern can be regarded as a “snapshot” of the underlying point process at a series of times. Thus, the number of points and corresponding locations of points differ for each snapshot. Each snapshot can be analyzed independently, but in many cases there may be little information in the data relating to model parameters, particularly parameters relating to the interaction between points. Thus, we develop an integrated approach, simultaneously analyzing all snapshots within a single robust and consistent analysis. We assume that sufficient time has passed between observation dates so that the spatial point patterns can be regarded as independent replicates, given spatial covariates. We develop a joint mixed effects Gibbs point process model for the replicates of spatial point patterns by considering environmental covariates in the analysis as fixed effects, to model the heterogeneous environment, with a random effects (or hierarchical) component to account for the different observation days for the intensity function. We demonstrate how the model can be fitted within a Bayesian framework using an auxiliary variable approach to deal with the issue of the random effects component. We apply the methods to a data set of musk oxen herds and demonstrate the increased precision of the parameter estimates when considering all available data within a single integrated analysis. Content Type Journal Article Pages 1-22 DOI 10.1007/s13253-012-0111-0 Authors Ruth King, School of Mathematics and Statistics, University of St Andrews, St Andrews, Fife, Scotland, UK Janine B. Illian, School of Mathematics and Statistics, University of St Andrews, St Andrews, Fife, Scotland, UK Stuart E. King, School of Mathematics and Statistics, University of St Andrews, St Andrews, Fife, Scotland, UK Glenna F. Nightingale, School of Mathematics and Statistics, University of St Andrews, St Andrews, Fife, Scotland, UK Ditte K. Hendrichsen, Norwegian Institute For Nature Research, 7047 Trondheim, Norway Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description:    Insects are among the most significant indicators of a changing climate. Here we evaluate the impact of temperature, precipitation, and elevation on the tree-killing ability of an eruptive species of bark beetle in pine forests of British Columbia, Canada. We consider a spatial-temporal linear regression model and in particular, a new statistical method that simultaneously performs model selection and parameter estimation. This approach is penalized maximum likelihood estimation under a spatial-temporal adaptive Lasso penalty, paired with a computationally efficient algorithm to obtain approximate penalized maximum likelihood estimates. A simulation study shows that finite-sample properties of these estimates are sound. In a case study, we apply this approach to identify the appropriate components of a general class of landscape models which features the factors that propagate an outbreak. We interpret the results from ecological perspectives and compare our method with alternative model selection procedures. Content Type Journal Article Pages 1-18 DOI 10.1007/s13253-012-0103-0 Authors Perla E. Reyes, Department of Statistics, Kansas State University, Manhattan, KS, USA Jun Zhu, Department of Statistics and Department of Entomology, University of Wisconsin, Madison, WI, USA Brian H. Aukema, Department of Entomology, University of Minnesota, St. Paul, MN, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description:    This paper uses high-order categorical non-stationary Markov chains to model the occurrence of extreme temperature events, in particular frost days. These models can be applied to estimate: the probability that a given day in the future is a frost day (below zero); the probability that a given period is frost-free; the distribution of the length of the frost-free period. These quantities then can be used for pricing of weather derivatives. Several stationary and non-stationary high-order (yet parsimonious) Markov models are proposed and compared using AIC and BIC . Partial likelihood theory is used to estimate the parameters of these models. We show that optimal (in terms of AIC / BIC ) non-stationary Markov models that have constant “Markov coefficients” (across the year) are not adequate to estimate the aforementioned probabilities. Therefore this paper develops Markov models with a time-varying periodic structure across the year. A challenge in fitting these models (by maximizing the partial likelihood) is the large number of parameters. The paper presents a method for overcoming this challenge; one that uses parametric fits to the logit of the nonparametric estimates of the seasonal transition probability curves to initialize the optim function in the R package. Satisfactory results are shown to obtain from this approach. The work is applied to temperature records for the Province of Alberta, Canada. Content Type Journal Article Pages 1-23 DOI 10.1007/s13253-012-0090-1 Authors Reza Hosseini, University of Southern California, Los Angeles, USA Nhu D. Le, BC Cancer Agency, Vancouver, Canada James V. Zidek, University of British Columbia, Vancouver, Canada Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description:    Co-clustering has been broadly applied to many domains such as bioinformatics and text mining. However, model-based spatial co-clustering has not been studied. In this paper, we develop a co-clustering method using a generalized linear mixed model for spatial data. To avoid the high computational demands associated with global optimization, we propose a heuristic optimization algorithm to search for a near optimal co-clustering. For an application pertinent to Integrated Pest Management, we combine the spatial co-clustering technique with a statistical inference method to make assessment of pest densities more accurate. We demonstrate the utility and power of our proposed pest assessment procedure through simulation studies and apply the procedure to studies of the persea mite ( Oligonychus perseae ), a pest of avocado trees, and the citricola scale ( Coccus pseudomagnoliarum ), a pest of citrus trees. Content Type Journal Article Pages 1-18 DOI 10.1007/s13253-012-0089-7 Authors Zhanpan Zhang, One Research Circle, GE Global Research, Niskayuna, NY 12309, USA Daniel R. Jeske, Department of Statistics, University of California, 900 University Avenue, Riverside, CA 92521, USA Xinping Cui, Department of Statistics, University of California, 900 University Avenue, Riverside, CA 92521, USA Mark Hoddle, Department of Entomology, University of California, 900 University Avenue, Riverside, CA 92521, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description: Andrew P. Robinson, Jeff D. Hamann: Forest Analytics With R: An Introduction Content Type Journal Article Category Book Review Pages 1-2 DOI 10.1007/s13253-012-0093-y Authors Luis A. Apiolaza, School of Forestry, University of Canterbury, Private Bag 4800, Christchurch, New Zealand Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description:    Bayesian MCMC calibration and uncertainty analysis for computationally expensive models is implemented using the SOARS (Statistical and Optimization Analysis using Response Surfaces) methodology. SOARS uses a radial basis function interpolator as a surrogate, also known as an emulator or meta-model, for the logarithm of the posterior density. To prevent wasteful evaluations of the expensive model, the emulator is built only on a high posterior density region (HPDR), which is located by a global optimization algorithm. The set of points in the HPDR where the expensive model is evaluated is determined sequentially by the GRIMA algorithm described in detail in another paper but outlined here. Enhancements of the GRIMA algorithm were introduced to improve efficiency. A case study uses an eight-parameter SWAT2005 (Soil and Water Assessment Tool) model where daily stream flows and phosphorus concentrations are modeled for the Town Brook watershed which is part of the New York City water supply. A Supplemental Material file available online contains additional technical details and additional analysis of the Town Brook application. Content Type Journal Article Pages 1-18 DOI 10.1007/s13253-012-0091-0 Authors David Ruppert, School of Operations Research and Information Engineering and Department of Statistical Science, Cornell University, Comstock Hall, Ithaca, NY 14853, USA Christine A. Shoemaker, School of Civil and Environmental Engineering and School of Operations Research and Information Engineering, Cornell University, Hollister Hall, Ithaca, NY 14853, USA Yilun Wang, School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China Yingxing Li, Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, China Nikolay Bliznyuk, Department of Statistics (IFAS), University of Florida, Gainesville, FL 32611, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description:    Understanding the relationship between air temperature and water temperature is a critical component in the management of aquatic resources. One important application is for stream fisheries that have temperature-sensitive fish. Co-located devices were used to obtain air and water temperature for summer periods from 100 locations in Virginia that have native brook trout populations. We develop a dynamic spatiotemporal model that accounts for the relationship between air and water temperature, and the spatial and temporal correlation in the data. Our model allows for the inclusion of land use, solar gain and other site level characteristics that might influence the relationship. Our model also allows for predictive forecasts of the risk to fish at individual sites and one can track how the risk changes over time. The model may be used to rank sites with regard to risk, which aids management in prioritizing decisions about restoration and preservation. Content Type Journal Article Pages 1-19 DOI 10.1007/s13253-012-0088-8 Authors Ciro Velasco-Cruz, Department of Statistics, Virginia Tech, Blacksburg, VA, USA Scotland C. Leman, Department of Statistics, Virginia Tech, Blacksburg, VA, USA Mark Hudy, U.S. Forest Service, Fish and Aquatic Ecology Unit, James Madison University, Harrsonburg, VA, USA Eric P. Smith, Department of Statistics, Virginia Tech, Blacksburg, VA, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description: 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.

Description: Heterosis, also known as the hybrid vigor, occurs when the mean phenotype of hybrid offspring is superior to that of its two inbred parents. The heterosis phenomenon is extensively utilized in agriculture though the molecular basis is still unknown. In an effort to understand phenotypic heterosis at the molecular level, researchers have begun to compare expression levels of thousands of genes between parental inbred lines and their hybrid offspring to search for evidence of gene expression heterosis. Standard statistical approaches for separately analyzing expression data for each gene can produce biased and highly variable estimates and unreliable tests of heterosis. To address these shortcomings, we develop a hierarchical model to borrow information across genes. Using our modeling framework, we derive empirical Bayes estimators and an inference strategy to identify gene expression heterosis. Simulation results show that our proposed method outperforms the more traditional strategy used to detect gene expression heterosis. This article has supplementary material online.

Description: In its simplest case, ANOVA can be seen as a generalization of the t-test for comparing the means of a continuous variable in more than two groups defined by the levels of a discrete covariate, a so-called factor. Testing is then typically done by using the standard F-test. Here, we consider the special but frequent case of factor levels that are ordered. We propose an alternative test using mixed models methodology. The new test often outperforms the standard F-test when factor levels are ordered. We illustrate the proposed testing procedure in simulation studies and three typical applications: nonparametric dose response analysis in agriculture, associations between rating scales and a continuous outcome, and testing differentially expressed genes with ordinal phenotypes.

Description: A problem of interest for ecology and conservation is that of determining the best allocation of survey effort in studies aimed at estimating the proportion of sites occupied by a species. Many species are difficult to detect and often remain undetected during surveys at sites where they are present. Hence, for the estimator of species occupancy to be unbiased, detectability needs to be taken into account. In such studies there is a trade-off between sampling more sites and expending more survey effort within each site. This design problem has not been addressed to date with an explicit consideration of the uncertainty in assumed parameter values. In this article we apply sequential and Bayesian design techniques and show how a simple two-stage design can significantly improve the efficiency of the study. We further investigate the optimal allocation of survey effort between the two study stages, given a prior distribution for the parameter values. We address this problem using asymptotic approximations and then explore how the results change when the sample size is small, considering second-order approximations and highlighting the value of simulations as a tool for study design. Given the efficiency gain, we recommend following the sequential design approach for species occupancy estimation. This article has supplementary material online.

Description:    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. Content Type Journal Article Pages 1-19 DOI 10.1007/s13253-012-0086-x Authors Stefano Castruccio, Department of Statistics, The University of Chicago, 5734 S. University Avenue, 60637 Chicago, IL, USA Luca Bonaventura, MOX—Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo Da Vinci 32, 20133 Milano, Italy Laura M. Sangalli, MOX—Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo Da Vinci 32, 20133 Milano, Italy Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description:    Multivariate hierarchical Bayesian models provide a flexible framework for comprehensive study of biological systems with more than one outcome. Recent methodological developments facilitate modeling of heterogeneous associations between outcomes by specifying a linear mixed model on (co)variances at different levels of the data structure. Motivated by previous evidence for heterogeneous correlations in animal agriculture, we apply the proposed hierarchical Bayesian models to study the nature of the correlations between key performance outcomes in dairy cattle production systems, namely milk yield and reproduction. That is, the association between these outcomes might depend upon various fixed and random effect sources of heterogeneity both at the individual cow (residual) level as well as the herd (cluster) level. We thus propose a sequential modeling approach based on the deviance information criterion to select relevant explanatory variables on both types of associations. Furthermore, we extend the proposed methodology to accommodate right-censored outcomes, as common for dairy reproduction data, and use it to analyze field data from the Michigan dairy industry. The nature of the associations between milk production and reproduction in dairy cattle was inferred to be strongly heterogeneous and driven by multiple farm management practices and herd attributes, as well as by random clustering effects, at both cow and herd levels, thereby suggesting potential between-herd and within-herd intervention strategies to optimize performance of dairy production systems. Supplementary materials are available online. Content Type Journal Article Pages 1-20 DOI 10.1007/s13253-012-0084-z Authors Nora M. Bello, Department of Statistics, Kansas State University, Manhattan, KS 66506, USA Juan P. Steibel, Department of Animal Science, Michigan State University, East Lansing, MI 48824, USA Ronald J. Erskine, Department of Large Animal Clinical Sciences, College of Veterinary Medicine, Michigan State University, East Lansing, MI 48824, USA Robert J. Tempelman, Department of Animal Science, Michigan State University, East Lansing, MI 48824, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description:    Genebank managers conduct viability tests on stored seeds so they can replace lots that have viability near a critical threshold, such as 50 or 85 % germination. Currently, these tests are typically scheduled at uniform intervals; testing every 5 years is common. A manager needs to balance the cost of an additional test against the possibility of losing a seed lot due to late retesting. We developed a data-informed method to schedule viability tests for a collection of 2,833 maize seed lots with 3 to 7 completed viability tests per lot. Given these historical data reporting on seed viability at arbitrary times, we fit a hierarchical Bayesian seed-viability model with random seed lot specific coefficients. The posterior distribution of the predicted time to cross below a critical threshold was estimated for each seed lot. We recommend a predicted quantile as a retest time, chosen to balance the importance of catching quickly decaying lots against the cost of premature tests. The method can be used with any seed-viability model; we focused on two, the Avrami viability curve and a quadratic curve that accounts for seed after-ripening. After fitting both models, we found that the quadratic curve gave more plausible predictions than did the Avrami curve. Also, a receiver operating characteristic (ROC) curve analysis and a follow-up test demonstrated that a 0.05 quantile yields reasonable predictions. Content Type Journal Article Pages 1-17 DOI 10.1007/s13253-012-0085-y Authors Allan Trapp, Department of Statistics & Statistical Laboratory, Iowa State University, Snedecor Hall, Ames, IA 50011-1210, USA Philip Dixon, Department of Statistics & Statistical Laboratory, Iowa State University, Snedecor Hall, Ames, IA 50011-1210, USA Mark P. Widrlechner, North Central Regional Plant Introduction Station, Iowa State University, Ames, IA 50011-1170, USA David A. Kovach, North Central Regional Plant Introduction Station, Iowa State University, Ames, IA 50011-1170, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description:    We demonstrate the potential of conditionally Gaussian state-space models in integrated population modeling, when certain model parameters may be functions of previous observations. The approach is applied to a heron census, and the data are best described by a model with three population-size thresholds which determine the population productivity. The model provides an explanation of how the population rebounds rapidly after major falls in size, which are characteristic of the data. By contrast, a simple logarithmic regression of productivity on population size was not significant. The results are of ecological interest, and suggest hypotheses for further investigation. Supplementary figures are available online. Content Type Journal Article Pages 1-14 DOI 10.1007/s13253-011-0080-8 Authors Panagiotis Besbeas, Department of Statistics, Athens University of Economics and Business, Athens, Greece Byron J. T. Morgan, School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, Kent CT2 7NF, England, UK Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description: If the full capture histories of captured individuals are available, inferences on multistate open population models may be conducted using the well known Arnason–Schwarz model. However, data of this detail is not always available. It is well known that inference on the transition probabilities of a Markov chain may be conducted using aggregate data and we extend this approach to aggregate data on multistate open population models. We show that for parameters to be identifiable we need to augment the aggregate data and we achieve this by batch marking a cohort of individuals according to their initial state, so that the batch marking augments the aggregate data. Model performance is examined by conducting several simulation studies and the model is applied to a real data set where full capture histories are available so it may be compared with the Arnason–Schwarz estimates. This article has supplementary material online. Content Type Journal Article Pages 1-16 DOI 10.1007/s13253-011-0065-7 Authors Jakub Stoklosa, Department of Mathematics and Statistics, The University of Melbourne, Victoria, 3010 Australia Peter Dann, Research Group, Phillip Island Nature Park, P.O. Box 97, Cowes, Phillip Island, Victoria 3922, Australia Richard Huggins, Department of Mathematics and Statistics, The University of Melbourne, Victoria, 3010 Australia Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description:    We consider a continuous-time proportional hazards model for the analysis of ecological monitoring data where subjects are monitored at discrete times and fixed sites across space. Since the exact time of event occurrence is not directly observed, we rely on dichotomous event indicators observed at monitoring times to make inference about the model parameters. We use autoregression on the response at neighboring sites from a previous time point to take into account spatial dependence. The interesting fact is utilized that the probability of observing an event at a monitoring time when the underlying hazards is proportional falls under the class of generalized linear models with binary responses and complementary log-log link functions. Thus, a maximum likelihood approach can be taken for inference and the computation can be carried out using standard statistical software packages. This approach has significant computational advantages over some of the existing methods that rely on Monte Carlo simulations. Simulation experiments are conducted and demonstrate that our method has sound finite-sample properties. A real dataset from an ecological study that monitored bark beetle colonization of red pines in Wisconsin is analyzed using the proposed models and inference. Supplementary materials that contain technical details are available online. Content Type Journal Article Pages 1-13 DOI 10.1007/s13253-011-0081-7 Authors Feng-Chang Lin, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA Jun Zhu, Department of Statistics and Department of Entomology, University of Wisconsin at Madison, Madison, WI 53706, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description: Model-averaging is commonly used as a means of allowing for model uncertainty in parameter estimation. In the frequentist framework, a model-averaged estimate of a parameter is the weighted mean of the estimates from each of the candidate models, the weights typically being chosen using an information criterion. Current methods for calculating a model-averaged confidence interval assume approximate normality of the model-averaged estimate, i.e., they are Wald intervals. As in the single-model setting, we might improve the coverage performance of this interval by a one-to-one transformation of the parameter, obtaining a Wald interval, and then back-transforming the endpoints. However, a transformation that works in the single-model setting may not when model-averaging, due to the weighting and the need to estimate the weights. In the single-model setting, a natural alternative is to use a profile likelihood interval, which generally provides better coverage than a Wald interval. We propose a method for model-averaging a set of single-model profile likelihood intervals, making use of the link between profile likelihood intervals and Bayesian credible intervals. We illustrate its use in an example involving negative binomial regression, and perform two simulation studies to compare its coverage properties with the existing Wald intervals. Content Type Journal Article Pages 1-14 DOI 10.1007/s13253-011-0064-8 Authors David Fletcher, Department of Mathematics and Statistics, University of Otago, PO Box 56, Dunedin, New Zealand Daniel Turek, Department of Mathematics and Statistics, University of Otago, PO Box 56, Dunedin, New Zealand Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description: Statistical analyses of two-way tables with interaction arise in many different fields of research. This study proposes the von Mises–Fisher distribution as a prior on the set of orthogonal matrices in a linear–bilinear model for studying and interpreting interaction in a two-way table. Simulated and empirical plant breeding data were used for illustration; the empirical data consist of a multi-environment trial established in two consecutive years. For the simulated data, vague but proper prior distributions were used, and for the real plant breeding data, observations from the first year were used to elicit a prior for parameters of the model for data of the second year trial. Bivariate Highest Posterior Density (HPD) regions for the posterior scores are shown in the biplots, and the significance of the bilinear terms was tested using the Bayes factor. Results of the plant breeding trials show the usefulness of this general Bayesian approach for breeding trials and for detecting groups of genotypes and environments that cause significant genotype × environment interaction. The present Bayes inference methodology is general and may be extended to other linear–bilinear models by fixing certain parameters equal to zero and relaxing some model constraints. Content Type Journal Article Pages 1-23 DOI 10.1007/s13253-011-0063-9 Authors Sergio Perez-Elizalde, Programa de Posgrado en Socieconomía, Estadística e Informática, Colegio de Postgraduados, Montecillos, Edo. de Mexico, Mexico Diego Jarquin, Programa de Posgrado en Socieconomía, Estadística e Informática, Colegio de Postgraduados, Montecillos, Edo. de Mexico, Mexico Jose Crossa, Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600 Mexico DF, Mexico Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description: Delay differential equations (DDEs) are widely used in ecology, physiology and many other areas of applied science. Although the form of the DDE model is usually proposed based on scientific understanding of the dynamic system, parameters in the DDE model are often unknown. Thus it is of great interest to estimate DDE parameters from noisy data. Since the DDE model does not usually have an analytic solution, and the numeric solution requires knowing the history of the dynamic process, the traditional likelihood method cannot be directly applied. We propose a semiparametric method to estimate DDE parameters. The key feature of the semiparametric method is the use of a flexible nonparametric function to represent the dynamic process. The nonparametric function is estimated by maximizing the DDE-defined penalized likelihood function. Simulation studies show that the semiparametric method gives satisfactory estimates of DDE parameters. The semiparametric method is demonstrated by estimating a DDE model from Nicholson’s blowfly population data. Content Type Journal Article Pages 1-16 DOI 10.1007/s13253-011-0066-6 Authors Liangliang Wang, Department of Statistics, University of British Columbia, 6356 Agricultural Road, Vancouver, BC V6T1Z2, Canada Jiguo Cao, Department of Statistics and Actuarial Science, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A1S6, Canada Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description:    A species accumulation curve is a useful tool in ecology. We construct a simultaneous confidence band for a single species accumulation curve, as an improvement over the existing point-wise confidence band. We also construct a simultaneous confidence band for the difference between two species accumulation curves, as a graphical procedure used to compare two species assemblages. A bootstrap-based calibration algorithm is adopted to tune the coverage probability of a confidence band toward its desired confidence level. The performance of the proposed procedures is demonstrated using both simulated data and real data. Content Type Journal Article Pages 1-14 DOI 10.1007/s13253-011-0062-x Authors Jun Li, Department of Statistics, University of California, Riverside, Riverside, CA 92521, USA Chang Xuan Mao, School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description:    Agricultural experiments are often laid out in a rectangle in 3–5 replicates. Is it better to use a standard randomized complete-block design in rows, or a complete-block design in rows but with restricted randomization, or an efficient row-column design? These approaches differ in the variance of the estimator of a difference between two treatments, and in the bias of the estimator of that variance, as well as in the mechanics of constructing the design and analyzing the data. I conclude that when intra-column correlations are high then the row-column design is best but that when they are moderate the best procedure is to use an improved version of restricted randomization, which gives an unbiased estimator of the average variance in the single experiment performed. Content Type Journal Article Pages 1-16 DOI 10.1007/s13253-011-0082-6 Authors R. A. Bailey, School of Mathematical Sciences, Queen Mary, University of London, Mile End Road, London, E1 4NS UK Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description:    We propose a method for introducing dependence in the dose-response modeling of multiple dichotomous endpoints. The method uses a copula to define a joint multivariate distribution that is consistent with predetermined marginal distributions representing the individual dose-response functions for each endpoint. Use of copulas allows the marginal dose-response functions for each dose-endpoint combination to be unrestricted in form. An application of particular relevance to risk assessment is the dose-response modeling of multiple types of tumors in test animals exposed to a carcinogen, allowing for tumors at different sites in the same animal to be statistically dependent. In addition, the method can be used to address the possibility that different tissues/organs are subject to different internal doses and possibly different active moieties. These applications are illustrated with rodent cancer bioassay data from two example compounds. Content Type Journal Article Pages 1-21 DOI 10.1007/s13253-011-0078-2 Authors Weihsueh A. Chiu, National Center for Environmental Assessment, Office of Research and Development, United States Environmental Protection Agency, Washington, DC 20460, USA Kenny S. Crump, Department of Mathematics and Statistics, Louisiana Tech University, Ruston, LA 71270, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description:    Forecasting the end-of-year crop yield is critical for agricultural decision-making and inherently difficult. Historically, a panel of commodity specialists known as the Agricultural Statistics Board convene regularly to set estimates based on expert review of a combination of survey data and administrative/auxiliary information. To make this process less subjective and more repeatable, we develop a Bayesian hierarchical model that produces superior yield forecasts/estimates, while quantifying different sources of uncertainty. The proposed hierarchical model naturally combines information from multiple monthly surveys measured on different temporal supports, including a field measurement survey and two farmer interview surveys. The dependence between the monthly updated surveys and the serial dependence of the annual yield are incorporated at different levels of the hierarchy. The effectiveness of our approach is demonstrated through an application from the US Department of Agriculture. Empirical results indicate that the hierarchical model produces superior forecasts to both the panel of experts and the composite estimator developed by Keller and Olkin (Technical Report, National Agricultural Statistics Service, 2002 ), while providing an accurate measure of uncertainty. Content Type Journal Article Pages 1-23 DOI 10.1007/s13253-011-0067-5 Authors Jianqiang C. Wang, Hewlett-Packard Labs, Palo Alto, CA 94304, USA Scott H. Holan, Department of Statistics, University of Missouri, Columbia, MO 65211-6100, USA Balgobin Nandram, Department of Mathematical Science, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, USA Wendy Barboza, National Agricultural Statistics Service, Fairfax, VA 22030-1504, USA Criselda Toto, National Institute of Statistical Sciences, Durham, NC 27709, USA Edwin Anderson, National Agricultural Statistics Service, Fairfax, VA 22030-1504, USA Journal Journal of Agricultural, Biological, and Environmental Statistics Online ISSN 1537-2693 Print ISSN 1085-7117

Description: The codling moth, the major insect pest in apple and pear orchards, is responsible for most insecticide treatments in European and North American orchards. Despite the intensive insecticide pressure, the codling moth remains a concern for fruit growers and can have a locally dramatic impact on production. To reduce pesticide use, there is a need to better determine the factors that affect the level of damage in orchards. The number of damaged fruits at the end of the codling moth’s first flight in pomefruit commercial orchards quantifies the severity of the attack. However, this variable sums the unobserved damages that occurred throughout this period, depending on a damage risk process that is likely driven by time-dependent and observable covariates. Moreover, as in most ecological/epidemiological studies, the data sets are incomplete and heterogeneous. The statistical challenge here is to build a sensible stochastic model to handle grouped and various data sets that are both right- and left-censored. In this paper, we model the temporal damage risk by combining survival methods with generalised linear mixed models to account for a time varying regression analysis. The results indicate that adult trapping provides useful information on the local level of fruit damages. Unexpectedly, the number of insecticide treatments did not appear to be a significant covariate for fruit damage modelling. The significance of the random component of the model indicates that a substantial portion of the variation in fruit damage remains, demanding further investigation concerning other relevant agronomic and environmental covariates.

Description: Large-scale field evaluation of genetic material forms an important part of the selection process in the early stages of plant breeding programs. These experiments are typically designed ignoring information on genetic relatedness, often available in the form of crossing history, or plant pedigree records. This paper considers the design of plant breeding experiments where the residuals may be correlated with an assumed autoregressive process, and there is a known genetic covariance structure among genotype effects. This structure is frequently more complex than simple nested family models, arising more generally from the pedigree, or possibly identity in state measures. It is widely accepted that the analysis of these data is improved using information on related individuals. The design of these experiments exploiting known genetic relatedness is considered using three case studies from industry that differ in selection goals, genetic complexity and scale.

Description: Spatially structured discrete data arise in diverse areas of application, such as forestry, epidemiology, or soil sciences. Data from several binary variables are often collected at each location. Variation in distributional properties across the spatial domain is of interest. The specific application that motivates our work involves characterizing historical distributions of two species of Oak in the Driftless Area in the Midwestern United States. Scientists are interested in understanding the patterns of interaction between species, as well as their relationships to spatial covariates. Accounting for spatial dependence is not only of inherent interest but also reduces prediction mean squared error, and is necessary for obtaining appropriate measures of uncertainty (i.e., standard errors and confidence intervals). To address the needs of the application, we introduce a centered bivariate autologistic model, which accounts for the statistical dependence in two response variables simultaneously, for the association between them and for the effect of spatial covariates. The model proposed here offers a relatively stable large-scale model structure, with model parameters which can be interpreted in the usual sense across levels of dependence. Since the model allows for separate dependence parameters for each variable, it offers, in essence, the equivalent of a model with a non-separable covariance function. The flexible model framework permits straightforward generalizations to structures with more than two variables, a temporal component, or an irregular lattice domain. Supplementary materials accompanying this paper appear on-line.

Description: We address the question of identifying the relative importance of covariates for model response, a form of sensitivity analysis. Relative importance is typically implemented as part of the model building procedure, e.g., forward variable selection or backward elimination. Here, we take a different perspective. We assume a model with multiple covariates and multivariate response has been selected and formulate criteria to assess covariate importance. Hence, with regard to covariates, our approach is joint , post model fitting, rather than conditional or sequential model creation. The noteworthy challenge we accommodate is the handling of multivariate response where individual regressions may give differing, perhaps conflicting, relative importances. In addition, we recognize that, according to the model specification, importance/sensitivity to covariates may be a global or a local issue. For models with multivariate response, we provide a criterion that (i) produces one sensitivity coefficient for each covariate, (ii) takes into account the model specification of uncertainty, and (iii) is based only on the model parameters but does not require a distribution on the covariates. However, with a prior on the covariates, in special cases, we show that comparison of covariates using this criterion gives the same results as comparison of marginal variances of the inverse predictive distributions of the covariates. We illustrate with an application examining sensitivity of tree abundance to climate. Supplementary materials accompanying this paper appear on-line.

Description: Mark-recapture estimators assume no loss of marks to provide unbiased estimates of population parameters. We describe a hidden Markov model (HMM) framework that integrates a mark loss model with a Cormack–Jolly–Seber model for survival estimation. Mark loss can be estimated with single-marked animals as long as a sub-sample of animals has a permanent mark. Double-marking provides an estimate of mark loss assuming independence but dependence can be modeled with a permanently marked sub-sample. We use a log-linear approach to include covariates for mark loss and dependence which is more flexible than existing published methods for integrated models. The HMM approach is demonstrated with a dataset of black bears ( Ursus americanus ) with two ear tags and a subset of which were permanently marked with tattoos. The data were analyzed with and without the tattoo. Dropping the tattoos resulted in estimates of survival that were reduced by 0.005–0.035 due to tag loss dependence that could not be modeled. We also analyzed the data with and without the tattoo using a single tag. By not using. Supplementary materials accompanying this paper appear on-line.

Description: This work presents a Bayesian hierarchical model with the dual objective to analyze stratified survival data and to automatically classify each stratum into a finite number of groups. This is achieved by specifying parametric as well as piecewise stratum-specific baseline hazards and a finite mixture distribution for the stratum-specific shape parameters. A proportional hazards or accelerated failure time regression component allows to identify the influence of covariates on the survival distribution. We illustrate the model using a dataset of Atlantic salmon, stratified by families, that have been challenged with infectious pancreatic necrosis virus (IPNV). The main objectives are to model the survival time in terms of certain covariates as well as to classify the salmon families into either an IPNV susceptible or resistant group with the ultimate goal of improving resistance to IPNV through a selective breeding program. We compare the fit of different models that include stratum-specific baselines and covariate effects. The classifications show a certain degree of robustness with respect to model choice.