Bayesian analysis of temperature sensitivity of plant phenology in Germany

The temperature sensitivity of 43 phenological phases was analysed in Germany within the period 1951–2006 with the help of a Bayesian approach. First a Bayesian model comparison of monthly temperatures and phenological phases throughout the year was conducted. We analysed the data as constant (mean onset date), as linear (constant trend over time) and as change point model (time varying change). The change point model involves the selection of two linear segments which match at a particular time. The matching point is estimated by an examination of all possible breaks weighted by their respective change point probability. Secondly a Bayesian coherence analysis was applied to investigate the relationship between phenological onset dates and an effective temperature generated as a weighted average of monthly means. Temperature weight coefficients were obtained from an optimization of a coherence factor by simulated annealing.Results reveal that late spring, summer and early autumn temperature months exhibit a clear preference for the change point model (>50%) indicating nonlinear change. The temperature development of April and August shows exceptionally high nonlinearities compared to the other months with change point model probabilities of 78% and 81% over the last five decades.For all phenophases a strong dependence of phenology on temperature is determined. We can classify two main temperature response patterns of the studied phenological phases: on the one hand spring phenophases are particularly sensitive to temperatures in April, exhibiting a prompt response. On the other hand summer phenophases are less influenced by temperature during or right before the month of the onset. They reveal a delayed response to nonlinear temperature changes mainly of April. Especially abrupt changes during the temperature sensitive stage of species cause a pronounced change in plant phenology regardless of the time of onset.     

Estimating the Health Impact of Climate Change With Calibrated Climate Model Output

Studies on the health impacts of climate change routinely use climate model output as future exposure projection. Uncertainty quantification, usually in the form of sensitivity analysis, has focused predominantly on the variability arise from different emission scenarios or multi-model ensembles. This paper describes a Bayesian spatial quantile regression approach to calibrate climate model output for examining to the risks of future temperature on adverse health outcomes. Specifically, we first estimate the spatial quantile process for climate model output using non-linear monotonic regression during a historical period. The quantile process is then calibrated using the quantile functions estimated from the observed monitoring data. Our model also down-scales the gridded climate model output to the point-level for projecting future exposure over a specific geographical region. The quantile regression approach is motivated by the need to better characterize the tails of future temperature distribution where the greatest health impacts are likely to occur. We applied the methodology to calibrate temperature projections from a regional climate model for the period 2041 to 2050. Accounting for calibration uncertainty, we calculated the number of excess deaths attributed to future temperature for three cities in the US state of Alabama.     

Flexible Distributed Lag Models Using Random Functions With Application to Estimating Mortality Displacement From Heat-Related Deaths

Changes in the distribution of ambient temperature, due to climate change or otherwise, will likely have a negative effect on public health. Characterizing the relationship between temperature and mortality is a key aspect of the larger problem of understanding the health effect of climate change. In this article, a flexible class of distributed lag models are used to analyze the effects of heat on mortality in four major metropolitan areas in the U.S. (Chicago, Dallas, Los Angeles, and New York). Specifically, the proposed methodology uses Gaussian processes to construct a prior model for the distributed lag function. Gaussian processes are adequately flexible to capture a wide variety of distributed lag functions while ensuring smoothness properties of process realizations. The proposed framework also allows for probabilistic inference of the maximum lag. Applying the proposed methodology revealed that mortality displacement (or, harvesting) was present for most age groups and cities analyzed suggesting that heat advanced death in some individuals. Additionally, the estimated shape of the DL functions gave evidence that highly variable temperatures pose a threat to public health. This article has supplementary material online.     

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.     

Spatial Hierarchical Modeling of Precipitation Extremes From a Regional Climate Model

The goal of this work is to characterize the extreme precipitation simulated by a regional climate model (RCM) over its spatial domain. For this purpose, we develop a Bayesian hierarchical model. Since extreme value analyses typically only use data considered to be extreme, the hierarchical approach is particularly useful as it sensibly pools the limited data from neighboring locations. We simultaneously model the data from both a control and future run of the RCM which allows for easy inference about projected change. Additionally, this hierarchical model is the first to spatially model the shape parameter which characterizes the nature of the distribution’s tail. Our hierarchical model shows that for the winter season, the RCM indicates a general increase in 100-year precipitation return levels for most of the study region. For the summer season, the RCM surprisingly indicates a significant decrease in the 100-year precipitation return level.     

A paradox of cooling winter soil surface temperatures in a warming northeastern United States

Climate model projections indicate that wintertime temperatures will warm and change snowfall patterns in the northeastern U.S. Snow provides insulation for soils from air temperature fluctuations. Therefore, these snowfall changes will have consequences, particularly for soil surface temperatures. Changes in minimum soil surface temperatures were investigated using a heat flow soil temperature model, driven using daily average air temperature and snow depth data. Two scenarios of three coupled atmosphere-ocean circulation models were used to run the soil temperature model. Modeled projections of minimum soil surface temperatures in the northeastern U.S. indicate that warming will occur over the majority of the region over the 2000-2099 period. In contrast, soil surface temperature projections in the northernmost and snowiest regions of the Northeast indicate minimum soil surface temperatures will be colder. In these northern regions, the coldest soil surface temperatures are also projected to occur later in winter, but show little change in other regions. Most trends throughout the Northeast are enhanced using the higher emission scenario A1fi. Changes to snow depth drive the changes in the minimum soil surface temperatures where snow persists during winter, whereas average air temperatures drive changes in the rest of the northeastern U.S.     

Decoupling of air flow above and in plant canopies and gravity waves affect micrometeorological estimates of net scalar exchange

Turbulence within open canopies is shown to undergo a dramatic change in character during the transition from convective to stable conditions. In convective conditions the flow within the canopy is coupled through turbulent exchange to the flow aloft. As the transition proceeds, the within- and above-canopy flows decouple and vertically coherent waves form within the canopy. The intensity of above-canopy turbulence is not a good indicator of flow decoupling. Within-canopy waves can lead to large random error in the measurement of the change of storage and the advection terms in the mass balance equation. More importantly, errors associated with sampling over incomplete wave cycles will inevitably be combined with true advective flux divergences at non-ideal sites. Quantitative estimates of likely errors on storage of heat and CO₂ within the canopy are presented.     

A first-order analysis of the heat wave in the soil

On the basis of a realistic distribution of the net radiative flux density (composed of a half sinusoid for the shortwave contribution plus a term dependent on the soil surface temperature for the longwave contribution), the solutions regarding the propagation of both the diurnal thermal wave and the heat flux density in the soil are analyzed. The more relevant differences from the analytical solutions obtained under the classical hypothesis of pure sinusoidal forcing waves on the boundary are therefore pointed out.     

用计算机对沿岸海浪现场观测的统计特征研究

为了分析广西北海市沿岸的波浪特征,采用计算机对SZF型波浪浮标观测一个月（2008年6月份）的风浪过程资料,并对其进行了统计分析和谱分析,给出了波高和周期分布,以及特征波要素和波谱关系。结果表明,观测期间风向很稳定,以SW和S为主,风速主要在1~3级,2级风的出现频率为40%,1级风为25%,3级风的出现频率为24.2%;波高以3级为主,出现率达66%;主波向出现的频率主要在S向,占35.4%。从风向和浪向来看,S、SW向的波主要是风或外海海浪共同作用所致,但波向与风向一般都有一定的差别,综其原因是风速和风向的变化较快,而波浪的变化有一定的延迟所致,实测波高分布与瑞利分布基本相符。     

Assessing the Impacts of Time-to-Detection Distribution Assumptions on Detection Probability Estimation

Abundance estimates from animal point-count surveys require accurate estimates of detection probabilities. The standard model for estimating detection from removal-sampled point-count surveys assumes that organisms at a survey site are detected at a constant rate; however, this assumption can often lead to biased estimates. We consider a class of N-mixture models that allows for detection heterogeneity over time through a flexibly defined time-to-detection distribution (TTDD) and allows for fixed and random effects for both abundance and detection. Our model is thus a combination of survival time-to-event analysis with unknown-N, unknown-p abundance estimation. We specifically explore two-parameter families of TTDDs, e.g., gamma, that can additionally include a mixture component to model increased probability of detection in the initial observation period. Based on simulation analyses, we find that modeling a TTDD by using a two-parameter family is necessary when data have a chance of arising from a distribution of this nature. In addition, models with a mixture component can outperform non-mixture models even when the truth is non-mixture. Finally, we analyze an Ovenbird data set from the Chippewa National Forest using mixed effect models for both abundance and detection. We demonstrate that the effects of explanatory variables on abundance and detection are consistent across mixture TTDDs but that flexible TTDDs result in lower estimated probabilities of detection and therefore higher estimates of abundance.Supplementary materials accompanying this paper appear on-line.     

Spatially varying temperature trends in a Central California Estuary

We consider monthly temperature data collected over a period of 16 years at 24 stations in the estuarine wetlands of the Elkhorn Slough watershed, located in the Monterey Bay area in Central California, USA. Our goal is to develop a statistical model in order to separate the seasonal cycle, short-term fluctuations, and long-term trends, while accounting for the spatial variability of these features. In the model, each station has a specific, time-invariant mixture of two seasonal patterns, to encompass the spatial variability of oceanic influence. Likewise, trends are modeled as local mixtures of two patterns, to include the spatial variability of long-term temperature change. Finally, all stations share a common baseline, whose temporal variability is linearly dependent on a variable that summarizes several atmospheric measurements. We use a Bayesian approach with a purposely developed Markov chain Monte Carlo method to explore the posterior distribution of the parameters. We find that the seasonal cycles have changed in time, that neighboring stations can have substantially different behaviors, and that most stations show significant warming trends.     

Predominant bacterial and fungal assemblages in agricultural soils during a record drought/heat wave and linkages to enzyme activities of biogeochemical cycling

Identification of microbial assemblages predominant under natural extreme climatic events will aid in our understanding of the resilience and resistance of microbial communities to climate change. From November 2010 to August 2011, the Southern High Plains (SHP) of Texas, USA, received only 39.6 mm of precipitation (vs. the historical average of 373 mm) and experienced the three hottest months (June–August 2011) since record keeping began in 1911. The objective of this study was to characterize soil bacterial (16 S rRNA gene) and fungal (internal transcribed spacer 1–4, ITS1-ITS4) species distribution and diversity via pyrosequencing during the peak of the drought/heat wave in July 2011 and when the Drought Index and temperatures were lower in March 2012. Samples were collected from two different soil types (loam and sandy loam) under two different dryland cropping histories (monoculture vs. rotation). Fungal Diversity Indexes were significantly higher after the drought/heat wave while Bacterial Indexes were similar. Bacterial phyla distribution in July 2011 was characterized by lower relative abundance of Acidobacteriaand Verrucomicrobia, and greater relative abundance of Proteobacteria, Chloroflexi, Actinobacteria and Nitrospirae than March 2012 samples. Further grouping of pyrosequencing data revealed approximately equal relative proportions of Gram positive (G+) and Gram negative (G−) bacteria in July 2011, while G− bacteria predominated in March 2012. Fungal class Dothideomycetes was approximately two times greater in July 2011 than in March 2012, while the class Sordariomycetes and a group of unidentified OTUs from Ascomycota increased from July 2011 to March 2012. Microbial community composition was less influenced by management history than by the difference in climatic conditions between the sampling times. Correspondence analysis identified assemblages of fungal and bacterial taxa associated with greater enzyme activities (EAs) of C, N, or P cycling found during the drought/heat wave. Microbial assemblages associated with arylsulfatase activity (key to S cycling), which increased after the drought/heat wave, were identified (Streptomyces parvisporogenes, Terrimonas ferruginea and Syntrophobacter sp.) regardless of the soil and management history. The distinct microbial composition found in July 2011 may represent assemblages essential to maintaining ecosystem function during extreme drought and intense heat waves in semiarid agroecosystems.     

基于贝叶斯网络及时序模拟的配电系统可靠性评估

针对适合配电系统可靠性评估的贝叶斯网络模型还不够完善,研究了贝叶斯网络模型的组成,建立了“联合”关系模型和“因果”关系模型;针对贝叶斯网络精确推理难以计算大规模配电系统的可靠性指标,将贝叶斯网络和时序模拟技术相结合,提出了一种时序模拟推理算法。该算法能产生元件的状态与随机时间段,实时进行系统状态的推理与时间、停电用户次数和停电量的累计,利用这些累计量不但可以计算系统的可靠性指标,还可进行诊断推理和因果推理,从而既能实现对系统的总体评价,又能找出钳制系统的薄弱环节。通过与配电系统可靠性测试系统数据及贝叶斯网络精确推理数据相比较,验证了该算法的合理性和有效性。     

A Bayesian Approach to Estimating Agricultural Yield Based on Multiple Repeated Surveys

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.     

甘肃黄土高原地区土壤水热特征分析研究

利用甘肃黄土高原代表站地温、土壤含水量及降水资料,运用统计模拟方法,分析了土壤温度的日变化及土中热交换特性,评述了水热耦合效应。地温的日变化特性用谐波分析方法描述,各季典型天气下24小时热通量值由热平衡台站规范方法计算。结果表明,各层次地温的日变化基本表现为一阶谐波,这种正弦的波形尤以晴天最为明显。不同季节典型天气下土中热通量的变化由正值转为负值的时间基本一致出现在16时,阴天提前1时,由负值转为正值的时间基本一致出现在7时,冬季阴天出现在9时。水热交互作用与土壤含水量的变化有显著相关关系。     

Dynamic Models of Animal Movement with Spatial Point Process Interactions

When analyzing animal movement, it is important to account for interactions between individuals. However, statistical models for incorporating interaction behavior in movement models are limited. We propose an approach that models dependent movement by augmenting a dynamic marginal movement model with a spatial point process interaction function within a weighted distribution framework. The approach is flexible, as marginal movement behavior and interaction behavior can be modeled independently. Inference for model parameters is complicated by intractable normalizing constants. We develop a double Metropolis–Hastings algorithm to perform Bayesian inference. We illustrate our approach through the analysis of movement tracks of guppies (Poecilia reticulata).     

Bayesian 2-Stage Space-Time Mixture Modeling With Spatial Misalignment of the Exposure in Small Area Health Data

We develop a new Bayesian two-stage space-time mixture model to investigate the effects of air pollution on asthma. The two-stage mixture model proposed allows for the identification of temporal latent structure as well as the estimation of the effects of covariates on health outcomes. In the paper, we also consider spatial misalignment of exposure and health data. A simulation study is conducted to assess the performance of the 2-stage mixture model. We apply our statistical framework to a county-level ambulatory care asthma data set in the US state of Georgia for the years 1999?C2008.     

Modelling soil temperature in forest clearcuts using climate station data

A physically based numerical model was developed to estimate the time courses of soil temperature in forest clearcuts from measured solar irradiance, air temperature and wind speed. The model is based upon a finite difference solution of the one-dimensional soil heat flow equation with a surface boundary condition determined from aerodynamic heat transfer and energy balance theory.Modelled soil temperatures were compared with data from two other studies reported in the literature and with temperatures measured in a forest clearcut during 6 and 16 day periods in the summer. The model calculated surface and subsurface soil temperatures accurately over these long intervals. Modelled soil temperatures were relatively insensitive to air temperature, soil thermal properties and the lower boundary soil temperature but quite sensitive to solar irradiance, wind speed and surface roughness.     

A Zero-Inflated Spatial Gamma Process Model With Applications to Disease Mapping

In this paper, we introduce a novel discrete Gamma Markov random field (MRF) prior for modeling spatial relations among regions in geo-referenced health data. Our proposition is incorporated into a generalized linear mixed model zero-inflated (ZI) framework that accounts for excess zeroes not explained by usual parametric (Poisson or Negative Binomial) assumptions. The ZI framework categorizes subjects into low-risk and high-risk groups. Zeroes arising from the low-risk group contributes to structural zeroes, while the high-risk members contributes to random zeroes. We aim to identify explanatory covariates that might have significant effect on (i) the probability of subjects in low-risk group, and (ii) intensity of the high risk group, after controlling for spatial association and subject-specific heterogeneity. Model fitting and parameter estimation are carried out under a Bayesian paradigm through relevant Markov chain Monte Carlo (MCMC) schemes. Simulation studies and application to a real data on hypertensive disorder of pregnancy confirms that our model provides superior fit over the widely used conditionally auto-regressive proposition.     

Estimating the Risk of a Crop Epidemic From Coincident Spatio-temporal Processes

Fusarium Head Blight (FHB), or “scab,” is a very destructive disease that affects wheat crops. Recent research has resulted in accurate weather-driven models that estimate the probability of an FHB epidemic based on experiments. However, these predictions ignore two crucial aspects of FHB epidemics: (1) An epidemic is very unlikely to occur unless the plants are flowering, and (2) FHB spreads by its spores, resulting in spatial and temporal dependence in risk. We develop a new approach that combines existing weather-based probabilities with information on flowering dates from survey data, while simultaneously accounting for spatial and temporal dependence. Our model combines two space-time processes, one associated with pure weather-based FHB risks and the other associated with flowering date probabilities. To allow for scalability, we model spatiotemporal dependence via a process convolutions approach. Our sample-based approach produces a realistic assessment of areas that are persistently at high risk (where the probability of an epidemic is elevated for extended time periods), along with associated estimates of uncertainty. We conclude with the application of our approach to a case study from North Dakota.