Climate Projections Using Bayesian Model Averaging and Space–Time Dependence

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.     

A Note on the Royle–Nichols Model for Repeated Detection–Nondetection Data

In this note, it is shown that the integrated likelihood for the Royle–Nichols model with a Poisson mixing distribution can be expressed as a finite rather than an infinite sum of terms. The advantages which so accrue are discussed and explored by means of two examples. The finite sum formulation of the likelihood is also shown to hold for negative binomial and zero-inflated mixing distributions. Results based on these two mixing distributions proved disappointing however and their use is not recommended unless extensive data are available.     

A General Bayesian Estimation Method of Linear–Bilinear Models Applied to Plant Breeding Trials With Genotype × Environment Interaction

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.     

A Model for Anion Electric Adsorption on Variable Charge Soils

A model was established for anion electric adsorption on variable charge surfaces and an equation to describe the relationship of adsorption amount with equalibrium concentration and suspension pH was derived according to the principles of surface reaction between variable charge surface and anions.The adsorption behaviours of nitrate and chloride on variable charge soils were modelled and the results showed that the model could describe the adsorption of nitrate very well.     

A Test of Positive Association for Detecting Heterogeneity in Capture for Capture–Recapture Data

The Cormack–Jolly–Seber (CJS) model assumes that all marked animals have equal recapture probabilities at each sampling occasion, but heterogeneity in capture often occurs and should be taken into account to avoid biases in parameter estimates. Although diagnostic tests are generally used to detect trap-dependence or transience and assess the overall fit of the model, heterogeneity in capture is not routinely tested for. In order to detect and identify this phenomenon in a CJS framework, we propose a test of positive association between previous and future encounters using Goodman–Kruskal’s gamma. This test is based solely on the raw capture histories and makes no assumption on model structure. The development of the test is motivated by a dataset of Sandwich terns (Thalasseus sandvicensis), and we use the test to formally show that they exhibit heterogeneity in capture. We use simulation to assess the performance of the test in the detection of heterogeneity in capture, compared to existing and corrected diagnostic goodness-of-fit tests, Leslie’s test of equal catchability and Carothers’ extension of the Leslie test. The test of positive association is easy to use and produces good results, demonstrating high power to detect heterogeneity in capture. We recommend using this new test prior to model fitting as the outcome will guide the model-building process and help draw more accurate biological conclusions. Supplementary materials accompanying this paper appear online.     

A Bayesian Model for Presence-Only Semicontinuous Data,With Application to Prediction of Abundance of <Emphasis Type="Italic">Taxus Baccata</Emphasis> in Two Italian Regions

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.     

A Measurement Error Model for Heterogeneous Capture Probabilities in Mark-Recapture Experiments: An Estimating Equation Approach

Logistic models for capture probabilities that depend on covariates are effective if the covariates can be measured exactly. If there is measurement error so that a surrogate for the covariate is observed rather than the covariate itself, simple adjustments may be made if the parameters of joint distribution of the covariate and the surrogate are known. Here we consider the case when a surrogate is observed whenever an individual is captured and the parameters must also be estimated from the data. An estimating equation regression calibration approach is developed and it is illustrated on a real dataset where the surrogate is an individual bird’s wing-length, which varies from occasion to occasion.     

A computer program to determine the soil textural class in 1–2–3 for windows and excel

Abstract

Spreadsheets are now widely used for data entry and analysis. Therefore, Texture AutoLookup (TAL) is a computer program designed to work within 1–2–3 for Windows and EXCEL to determine the USDA soil textural class. TAL determines the textural class without having to repeat data entry because data is taken directly from the spreadsheet itself. Moreover, TAL works even with two particle size data or with imperfect data (that is, the sum of the three particle sizes being unequal to 100%). TAL is independent of the particle‐size analysis method, and TAL allows textural class names to be modified or be translated into another language.     

Assessing the Efficiency of Humic Substances as Washing Agents for Oil–Contaminated Soils and Peats under Model Experimental Conditions

Two humic preparations of different origins have been compared as washing agents for oil-contaminated soils and peat under model experimental conditions using a sample from the plow horizon of soddypodzolic soil artificially contaminated with oil or diesel fuel and a sample of high-moor peat contaminated with crude oil because of a spill occurred 15 years ago. Soil and peat were washed by shaking with solutions of the humic preparations Gumat Sakhalinskii and Lignogumat in a 1: 10 (m/v) ratio. Control samples were washed with distilled water. Washing with a synthetic surfactant (sodium dodecyl sulfate) was also added to the experimental design. After washing, soil and peat samples were air-dried and used for the determination of the total content of petroleum hydrocarbons; the characterization of their hydrocarbon composition; and the assessment of hydrophobicity from the contact angle and the efficiency of colonization by oil-destructing microorganisms Rhodococcus sp. and Candida sp., which are components of the preparation Bioros recommended for oil contaminations. It has been shown that the extraction efficiency of petroleum hydrocarbons by humic preparations did not differ from the extraction efficiency by water and was less than that by sodium dodecyl sulfate in all cases. No appreciable changes in the contact angles of soil and peat have been observed at the use of water and humic preparations as washing agents, while the contact angle decreased to less than 90o after washing with sodium dodecyl sulfate, which indicated the hydrophobicity of the surface of substrate particles. It has been found that humic preparations favor the colonization of soil and peat by oil-destructing microorganisms Rhodococcus sp. and Candida sp. Based on the obtained results, humic preparations have been recommended for further study as preparations favoring the ability of oil-destructing microorganisms to colonize oil-contaminated substrates.     

A Capture–Recapture Model with Double-Marking,Live and Dead Encounters,and Heterogeneity of Reporting Due to Auxiliary Mark Loss

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.     

Razorbacks: Feral Pigs as Agricultural Pests,Disruptors of Ecosystems,Reservoirs of Contagion,and Favored Game for Sport and Subsistence Hunters: A Review of the Literature, 2005–2011

Whether they are called “razorbacks,” as is common in the southern United States (and where they are notably the official sports mascots of the University of Arkansas), or “wild boar,” in much of the rest of the world, feral pigs are interbreeding subtypes of Sus scrofa, the same pig domesticated over 5,000 years ago. Once they have become established in the wild, these formerly manageable animals rapidly undergo a dramatic reversion to a wild appearance, may display aggression toward humans, engage in agriculturally and ecologically damaging behaviors, and spread diseases to both livestock and humans. They are the targets of many ongoing, increasingly sophisticated, science-based, eradication efforts.