Evaluation of the Accuracy of Diagnostic Tests From Repeated Measurements Without a Gold Standard

A model is presented to evaluate the accuracy of diagnostic tests from data from individuals that are repeatedly tested in time. Repeated measurements from three diagnostic tests for foot-and-mouth disease, applied to vaccinated and experimentally infected cattle, were analyzed. At any time the true disease status of the individuals was unknown, i.e., no gold standard was available. The model allows for correlation between repeated test results, in consequence of the underlying structure for the unknown true disease status, but also by the distribution of the test results conditional upon true disease status. The model also allows for dependence between the different diagnostic tests conditional upon true disease status. Prior information about the structure of the prevalence and the specificity of the tests was incorporated in a Bayesian analysis. Posterior inference was carried out with Markov chain Monte Carlo. Simulated data were analyzed to gain insight into the performance of the posterior Bayesian inference. The simulated data are typical for the expensive and, therefore, modestly sized infection experiments that are conducted under controlled conditions.     

Hypothesis Tests on Mixture Model Components with Applications in Ecology and Agriculture

Multiple comparisons are widely used to compare gross features of distributions across populations. However, often a scientific hypothesis is more easily couched in terms of more focused null and alternative statistical hypotheses. For example, among distributions exhibiting clusters of continuous measurements across strata, are there clusters of measurements similar in terms of location, spread, or weight? We propose testing such hypotheses using a sequence of nested finite mixture models. Reasonable, data-driven priors are suggested based on estimates of the sample spreads and midpoints. Formal hypothesis testing is carried out through the computation of Bayes factors. The method is illustrated on Holling’s (Ecological Monographs 62:447–502, 1992) forest and prairie bird body mass data, and data on the time-to-abortion in dairy cows. Supplemental simulations 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.     

Independent Component Analysis for Three-Way Data With an Application From Atmospheric Science

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.     

EFFECTS OF ELEMENTAL SULFUR AND MYCORRHIZAE ON THE YIELD OF WHEAT IN DIFFERENT SOILS

Yield responses of wheat (Triticum aestivum L.) to elemental sulfur (S), mycorrhizae (mycorrhizal fungus, Glomus mosseae), and phosphorus (P) alone and collectively in two different soils were investigated. Plants were grown on calcareous sterilized Karaburun and Menek?e soils (sub-group of Typic Xerorthent). In the all treatments mycorrhizae inoculation alone compared to the control treatment increased shoot and root yields. The yield responses to S alone, P alone, and combined application of S and P in mycorrhizal and non-mycorrhizal treatments in two different soils were interestingly fluctuating. The findings obtained in this study indicated that S tended to increase the efficiency of mycorrhizae in the well aerated Karaburun soil in comparison to a less aerated (heavier textured) Menek?e soil. This potential to increase the efficiency of mycorrhizae on the root growth indicate that yield increase of shoot and grain can occur being an important finding for ecological agriculture.     

Nonparametric Threshold Model of Zero-Inflated Spatio-Temporal Data with Application to Shifts in Jellyfish Distribution

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.     

区域农田有机物质投入量的估算方法 —— 以陕西长武县为例

以黄土高原典型沟壑区长武县为研究区域,进行区域农田有机物质投入量估算方法的研究。将农田有机物质投入分为秸秆还田、秸秆根茬残留、作物地下生物量和有机肥4个部分分别进行估算。基于LANDSAT 5卫星遥感资料,利用CASA模型估算植物第一净生产力,获得作物整株重,结合作物根冠比估算作物地上生物量和地下生物量,以地上生物量和实地秸秆调查来估算秸秆根茬量和秸秆还田量。估算结果显示,长武县农田年总有机物质投入量为42 700 t。     

Estimating Population Growth Rate From Capture–Recapture Data in Presence of Capture Heterogeneity

The direct estimation and modeling of population growth rate from capture–recapture data has now seen a number of applications. However, the original model cannot accommodate heterogeneous capture probabilities. While studying a population of small mammals Peromyscus maniculatus, we became concerned that the peak of population size may be estimated too late in the year because of heterogeneous catchability. Hence, we developed a variation of the original model with a finite number of catchability classes. The results obtained with the new model are more in agreement with the known biology of this population. A bibliographic appendix and computer code are available online.     

Greenhouse gas fluxes during rewetting of peatlands by use of effluents – a lysimeter study

Abstract

The present study aims at assessing the effect of using the effluent of a wastewater treatment plant as an alternative measure for rewetting nutrient-rich fen soils over the growing season on the emission of greenhouse gas (GHG) and at discussing possible changes in the greenhouse potential as a result of this practice. In order to allow a discussion on GHG based on integrated CH₄, N₂O, and CO₂ flux rates, fluxes were measured in our lysimeter study using the chamber methodology from May to December in 2003 and 2004. The study compares the gaseous fluxes of fen soils in lysimeters treated with the effluent and/or freshwater for rewetting. Only freshwater was applied to the control lysimeter. The source of water hardly had any statistically significant effect on trace gas fluxes. However, there was a trend towards higher CH₄ emissions at the effluent lysimeters compared to the control lysimeter. Effluent usage did not decrease the greenhouse effect at the same rate, which could be observed at the control. Nevertheless, regarding gaseous emissions the use of effluents could prove to be a solution to the current problem of today's major peat oxidation and fen soil loss by drainage.     

Spatial Inference of Nitrate Concentrations in Groundwater

We develop a method for multiscale estimation of pollutant concentrations, based on a nonparametric spatial statistical model. We apply this method to estimate nitrate concentrations in groundwater over the mid-Atlantic states, using measurements gathered during a period of 10 years. A map of the fine-scale estimated nitrate concentration is obtained, as well as maps of the estimated county-level average nitrate concentration and similar maps at the level of watersheds and other geographic regions. The fine-scale and coarse-scale estimates arise naturally from a single model, without refitting or ad hoc aggregation. As a result, the uncertainty associated with each estimate is available, without approximations relying on high spatial density of measurements or parametric distributional assumptions.     

Microbial processes and community composition in the rhizosphere of European beech - The influence of plant C exudates

Plant roots strongly influence C and N availability in the rhizosphere via rhizodeposition and uptake of nutrients. This study aimed at investigating the effect of resource availability on microbial processes and community structure in the rhizosphere. We analyzed C and N availability, as well as microbial processes and microbial community composition in rhizosphere soil of European beech and compared it to the bulk soil. Additionally, we performed a girdling experiment in order to disrupt root exudation into the soil. By this novel approach we were able to demonstrate that enhanced resource availability positively affected N mineralization and hydrolytic enzyme activities in the rhizosphere, but negatively affected nitrification rates and oxidative enzyme activities, which are involved in the degradation of soil organic matter. Both rhizosphere effects on N mineralization and oxidative enzyme activities disappeared in the girdling treatment. Microbial community structure in the rhizosphere, assessed by phospholipid fatty acid analysis, differed only slightly from bulk soil but was markedly altered by the girdling treatment, indicating additional effects of the girdling treatment beyond the reduction of root exudation. Differences in oxidative enzyme activities and nitrification rates between rhizosphere soil and bulk soil, however, suggest considerable differences in the (functional) microbial community composition.     

Comparative Study of Different Soil Amendments and Microbes for Integrated Nutrient Management and Growth Promotion of Jatropha Curcas

Increased alkalinity caused reduction in growth of Jatropha curcas plant. This experiment was designed to test the efficacy of beneficial microbes and vermicompost individually and in combinations to alleviate the stressful effect of alkaline stress on growth of Jatropha. Plants inoculated with bacterial consortia had significantly increase in vegetative growth parameters. Soil analysis showed increase in total organic carbon (TOC) by 68% compared to soil before plantation treated with Bacterial consortia + vermicompost. Nitrogen (N) and phosphorus (P) content in the soil was increased by 25% and 84% before plantation treated with Bacterial consortia. Maximum uptake of potassium (K) and sodium (Na) content was observed in consortia. Plant analysis showed that consortia could increase uptake of micronutrients compared to control and other treatments. It can be concluded that multispecies bacterial consortia alone or in combination with mycorrhiza and vermicompost can reduce alkaline stress and enhances the seed germination potential, vegetative growth, and nutrient status of soil.     

Bayesian Spatial Point Process Modeling of Line Transect Data

This paper develops a Bayesian approach for spatial inference on animal density from line transect survey data. We model the spatial distribution of animals within a geographical area of interest by an inhomogeneous Poisson process whose intensity function incorporates both covariate effects and spatial smoothing of residual variation. Independently thinning the animal locations according to their estimated detection probabilities results into another spatial Poisson process for the sightings (the observations). Prior distributions are elicited for all unknown model parameters. Due to the sparsity of data in the application we consider, eliciting sensible prior distributions is important in order to get meaningful estimation results. A reversible jump Markov Chain Monte Carlo (MCMC) algorithm for simulation of the posterior distribution is developed. We present results for simulated data and a real data set of minke whale pods from Antarctic waters. The main advantages of our method compared to design-based analyses are that it can use data arising from sources other than specifically designed surveys and its ability to link covariate effects to variation of animal density. The Bayesian paradigm provides a coherent framework for quantifying uncertainty in estimation results.     

Yield and Price Forecasting for Stochastic Crop Decision Planning

The primary objective of this paper is to develop yield and price forecasting models employed in informed crop decision planning—a key aspect of effective farm management. For yearly yield prediction, we introduce a weather-based regression model with time-dependent varying coefficients. In order to allow for within-year climate variations, we predict yearly crop yield using weekly temperature and rainfall summaries resulting in a large number of correlated predictors. To overcome this difficulty, we reduce the space of predictors to a small number of uncorrelated predictors using Functional Principal Component Analysis (FPCA). For detailed price forecasting, we develop a futures-based model for long-range cash price prediction. In this model, the cash price is predicted as a sum of the nearby settlement futures price and the predicted commodity basis. We predict the one-year commodity basis as a mixture of historical basis data using a functional model-based approach. In both forecasting models, we estimate approximate prediction confidence intervals that are further integrated in a decision planning model. We applied our methods to corn yield and price forecasting for Hancock County in Illinois. Our forecasting results are more accurate in comparison to predictions based on existing methods. The methods introduced in this paper generally apply to other locations in the US and other crop types. The supplemental materials for this article are available online.     

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.     

Bayesian Inference for Animal Space Use and Other Movement Metrics

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.     

Effects of Biosolids-Derived Organomineral Fertilizers,Urea, and Biosolids Granules on Crop and Soil Established with Ryegrass (Lolium perenne L.)

A pot scale trial investigated the agronomic performance of two organomineral fertilizers (OMF₁₅—15:4:4 and OMF₁₀—10:4:4) in comparison with urea and biosolids granules to establish ryegrass (Lolium perenne L.). Two soils of contrasting characteristics and nitrogen (N) application rates in the range of 0–300 kg ha^?1 were used over a period of 3 years. Fertilizer effects were determined on: (1) dry matter yield (DMY) and crop responses, (2) nitrogen use efficiency (NUE), and (3) selected soil chemical properties. Ryegrass responded linearly (R² ≥ 0.75; P < 0.001) to organomineral fertilizers (OMF) application increasing DMY by 2–27% compared with biosolids but to a lesser extent than urea (range: 17–55%). NUE was related to concentration of readily available N in the fertilizer: urea and OMF showed significantly (P < 0.05) greater N recoveries than biosolids. Total N in soil and soil organic matter showed increments (P < 0.05), which depended on the organic-N content in the fertilizer applied. Soil extractable P levels remained close to constant after 3 years of continuous OMF application but increased with biosolids and decreased with urea, respectively (P < 0.05). The application of biosolids changed soil P Index from 5 to 6; hence, there is a need to monitor soil P status. Both OMF₁₀ and OMF₁₅ formulations are suitable for application in ryegrass.     

High proportion of diploid hybrids produced by interspecific diploid × tetraploid <Emphasis Type="Italic">Sorghum</Emphasis> hybridization

A perennial version of grain sorghum [S. bicolor (L.) Moench] would create opportunities for greatly reducing tillage and preventing soil degradation. Efforts to select for perenniality and grain production among progeny of hybrids between S. bicolor (2n = 20) and the weedy tetraploid perennial S. halepense (L.) Pers. (2n = 40) are complicated in that F₁ hybrids produced by diploid × tetraploid sorghum crosses are usually tetraploid. In 2013, a set of random pollinations between 19 diploid cytoplasmic male-sterile inbred lines and 43 tetraploid perennial plants produced 165 F₁ hybrid plants, more than 75% of which had highly atypical plant, panicle, and seed phenotypes. Phenotypic segregation in F₂ populations derived from atypical hybrids was also anomalous. Examination of mitotic metaphase cells in F₁ or F₂ root tips revealed that 129 of the 165 hybrids were diploid. Parentage of the diploid progenies was confirmed using simple-sequence repeat analysis. The mechanism by which diploid hybrids arise from diploid × tetraploid crosses is unknown, but it may involve either production of monohaploid (n = 10) pollen by the tetraploid parent or chromosome elimination during early cell divisions following formation of the triploid zygote. The ability to produce diploid germplasm segregating for S. bicolor and S. halepense alleles could have great utility, both for the development of perennial sorghum and for the improvement of conventional grain sorghum.     

Estimating Distance Sampling Detection Functions When Distances Are Measured With Errors

Distance sampling methods assume that distances are known but in practice there are often errors in measuring them. These can have substantial impact on the bias and precision of distance sampling estimators. In this paper we develop methods that accommodate both systematic and stochastic measurement errors. We use the methods to estimate detection probability in two surveys with substantial measurement error. The first is a shipboard line transect survey in the North Sea in which information on measurement error comes from photographically measured distances to a subset of detections. The second is an aerial cue-counting survey off Iceland in which information on measurement error comes from pairs of independently estimated distances to a subset of detections. Different methods are required for measurement error estimation in the two cases. We investigate by simulation the properties of the new estimators and compare them to conventional estimators. They are found to perform better than conventional estimators in the presence of measurement error, more so in the case of cue-counting and point transect estimators than line transect estimators. An appendix on the asymptotic distributions of conditional and full likelihood estimators is available online.