Predicting the spatial distribution of ground flora on large domains using a hierarchical Bayesian model

Accomodation of important sources of uncertainty in ecological models is essential to realistically predicting ecological processes. The purpose of this project is to develop a robust methodology for modeling natural processes on a landscape while accounting for the variability in a process by utilizing environmental and spatial random effects. A hierarchical Bayesian framework has allowed the simultaneous integration of these effects. This framework naturally assumes variables to be random and the posterior distribution of the model provides probabilistic information about the process. Two species in the genus Desmodium were used as examples to illustrate the utility of the model in Southeast Missouri, USA. In addition, two validation techniques were applied to evaluate the qualitative and quantitative characteristics of the predictions.This revised version was published online in May 2005 with corrections to the Cover Date.     

基于MCMC模拟的MGPD模型及其在地质灾害风险度量中的应用

基于马尔科夫链蒙特卡洛(简记为MCMC)模拟的参数贝叶斯估计,对改进的广义帕累托分布(简记为MGPD)模型进行了优化,并利用该模型得到了地质灾害损失的在险损失值(简记为VaR)和条件损失值(简记为CVaR).以湖南娄底市地质灾害损失数据进行实证分析及模型适应性检验,结果表明:优化后的模型不仅具有很好的极值数据描述能力,而且具有较强的适用性.     

基于信号博弈的阳澄湖大闸蟹绿色标签市场应用分析

水产品绿色标签对消费者来说是高质量的信号,对卖家来说则是卖出高价的资本.但是欺诈行为的存在损害了消费者和其他卖家的利益,扰乱了市场秩序.以阳澄湖大闸蟹为例,通过信号博彝理论,对螃蟹市场中关卖双方的消费行为进行博彝分析,得到了精炼贝叶斯Nash均衡.     

Contribution of Different Organs to Grain Filling in Durum Wheat under Mediterranean Conditions I. Contribution of Post‐Anthesis Photosynthesis and Remobilization

Under Mediterranean conditions, drought affects cereals production principally through a limitation of grain filling. In this study, the respective role of post‐anthesis photosynthesis and carbon remobilization and the contribution of flag leaf, stem, chaff and awns to grain filling were evaluated under Mediterranean conditions in durum wheat (Triticum turgidum var. durum) cultivars. For the purpose, we examined the effects of shading and excision of different parts of the plant and compared carbon isotope discrimination (Δ) in dry matter of flag leaf, stem, chaff, awns and grain at maturity and in sap of stem, flag leaf, chaff and awns, this last measurement providing information on photosynthesis during a short period preceding sampling. Source–sink manipulations and isotopic imprints of different organs on final isotope composition of the grain confirmed the high contribution of both carbons assimilated by ears and remobilized from stems to grain filling, and the relatively low contribution of leaves to grain filling. Grain Δ was highly and significantly associated with grain yield across treatments, suggesting the utilization of this trait as an indicator of source–sink manipulations effects on grain yield. Chaff and awns Δ were better correlated with grain Δ than stem and leaf Δ, indicating that chaff were more involved in grain filling than other organs. Moreover, in chaff, sap Δ was highly significantly correlated with dry matter Δ. These results suggest the use of Δ for a rapid and non‐destructive estimation of the variation in the contribution of different organs to grain filling.     

Bayesian estimation of diagnostic accuracy of a new bead-based antibody detection test to reveal Toxoplasma gondii infections in pig populations

The success of a Toxoplasma gondii surveillance program in European pig production systems depends partly on the quality of the test to detect infection in the population. The test accuracy of a recently developed serological bead-based assay (BBA) was investigated earlier using sera from experimentally infected animals. In this study, the accuracy of the BBA was determined by the use of sera from animals from two field subpopulations. As no T. gondii infection information of these animals was available, test accuracy was determined through a Bayesian approach allowing for conditional dependency between BBA and an ELISA test. The priors for prevalence were based on available information from literature, whereas for specificity vague non-informative priors were used. Priors for sensitivity were based either on available information or specified as non-informative. Posterior estimates for BBA sensitivity and specificity were (mode) 0.855 (Bayesian 95% credibility interval (bCI) 0.702–0.960) and 0.913 (bCI 0.893–0.931), respectively. Comparing the results of BBA and ELISA, sensitivity was higher for the BBA while specificity was higher for ELISA. Alternative priors for the sensitivity affected posterior estimates for sensitivity of both BBA and ELISA, but not for specificity. Because the difference in prevalence between the two subpopulations is small, and the number of infected animals is small as well, the precision of the posterior estimates for sensitivity may be less accurate in comparison to the estimates for specificity. The estimated value for specificity of BBA is at least optimally defined for testing pigs from conventional and organic Dutch farms.     

金融时间序列指标判别框架：以特质波动率为例

基于拐点集合判别的TBUD方法主要思路是分析拐点集合间的关系，并在高维空间进行划分，从而搭建判别模型，并将分析框架应用在特质波动率等若干指标上，利用实证数据得到结论。应用TBUD判别框架可以发现，特质波动率等指标无法对拐点集合进行清晰划分，因而并不具有预测能力。     

A global sensitivity analysis of cultivar trait parameters in a sugarcane growth model for contrasting production environments in Queensland,Australia

New sugarcane cultivars are continuously developed to improve sugar industry productivity. Despite this sugarcane crop models such as the ‘Sugar’ module in the Agricultural Productions System sIMulator (APSIM-Sugar) have not been updated to reflect the most recent cultivars. The implications of misrepresenting cultivar parameters in APSIM-Sugar is difficult to judge as little research has been published on the likely values of these parameters and how uncertainty in parameter values may affect model outputs. A global sensitivity analysis can be used to better understand how cultivar parameters influence simulated yields. A Gaussian emulator was used to perform a global sensitivity analysis on simulated biomass and sucrose yield at harvest for two contrasting sugarcane-growing regions in Queensland, Australia. Biomass and sucrose yields were simulated for 42 years to identify inter-annual variability in output sensitivities to 10 parameters that represent physiological traits and can be used to simulated differences between sugarcane cultivars. Parameter main effect (S_i) and total effect (ST_i) sensitivity indices and emulator accuracy were calculated for all year-region-output combinations. When both regions were considered together parameters representing radiation use efficiency (rue), number of green leaves (green_leaf_no) and a conductance surrogate parameter (k_L) were the most influential parameters for simulated biomass in APSIM-Sugar. Simulated sucrose yield was most sensitive to rue, sucrose_fraction (representing the fraction of biomass partitioned as sucrose in the stem) and green_leaf_no. However, climate and soil differences between regions changed the level of influence cultivar parameters had on simulation outputs. Specifically, model outputs were more sensitive to changes in the transp_eff_cf and k_L parameters in the Burdekin region due to lower rainfall and poor simulated soil conditions. Collecting data on influential traits that are relatively simple to measure (e.g. number of green leaves) during cultivar development would greatly contribute to the simulation of new cultivars in crop models. Influential parameters that are difficult to measure directly such as transp_eff_cf and sucrose_fraction are ideal candidates for statistical calibration. Calibrating crop models either through direct observation or statistical calibration would allow crop modellers to better test how new cultivars will perform in a range of production environments.     

头骨形态指标鉴别水貂来源有效性的评估

人工驯养野生动物是获取野生动物产品的重要手段,但是非法盗猎的野生动物产品以人工饲养的名义进入市场,就会对野生动物的保护形成挑战。因此,准确鉴别市场上的动物产品是来自养殖场还是野外,是保护野外种群,维持养殖业正常秩序的关键。本研究以北美水貂(Neovison vison)为例,基于颧骨内侧空间(ZAS)、颧宽(ZAA和ZBA)、颅基长(BSL)、下颌长(MDL)、眶后收缩(POC)和最大面部宽度(GFB)等量度,建立了9个头骨形态计量学指标:POC/GFB、POC/BSL、POC/MDL、ZAS SQRT/BSL、ZA SSQRT/MDL、ZBA×BSL/2ZAS、ZBA×MDL/2ZAS、ZBB×BSL/2ZAS和ZBB×MDL/2ZAS。野生组(n=32,9雄23雌)和饲养组(n=45,35雄10雌)的比较表明,除POC/GFB外,所有指标对于在两组样品之间均有显著差异,整体判别正确率为73.2%—85.5%。在性别已知的情况下,雄性的整体正确率为82.4%—93.8%,雌性为41.7%—85.7%。为此,应针对不同性别、不同来源的动物建立相应的参考数据,利用似然比进行鉴别。本研究的结果不仅可用于北美水貂的鉴别,也为其他小型食肉动物的鉴别提供经验。     

Discrimination of “grazing milk” using milk fatty acid profile in the grassland dairy area in Hokkaido

Milk produced by the grazing system, referred to as “grazing milk” contains many components required for human health. The milk fatty acid (FA) profile is strongly associated with the diet on the farms. In the present study, based on the FA profile of farmer's bulk milk, we determined how to discriminate between milk produced on grazing and on a confinement system. A field survey was conducted four times (grazing and confinement season) in the Konsen (29 farms) and Okhotsk (25 farms) area in Hokkaido. Farmer's bulk milk samples and details of feeding management were collected and the FA profile of milk was measured. Milk produced during the grazing season contained less C16:0 and cis‐9 C16:0, and more C18:0, cis‐9 C18:1, trans‐11 C18:1, cis‐9,12 C18:2, cis‐9,trans‐11 C18:2 and cis‐9,12,15 C18:3 than milk produced during the confinement season. Discrimination analysis using 16 FA revealed that almost all milk samples were discriminated correctly (confinement season: 90% correct and 10% borderline, grazing season: 88% correct, 9% borderline and 3% incorrect). For farmers that were categorized incorrectly and were considered borderline in the grazing season, the dependency on pasture was low compared with that for farmers correctly discriminated. Therefore, to claim “grazing milk”, a high dependency on pasture is required for grazing dairy farmers.     

Genomic prediction for meat and carcass traits in Nellore cattle using a Markov blanket algorithm

This study was carried out to evaluate the advantage of preselecting SNP markers using Markov blanket algorithm regarding the accuracy of genomic prediction for carcass and meat quality traits in Nellore cattle. This study considered 3675, 3680, 3660 and 524 records of rib eye area (REA), back fat thickness (BF), rump fat (RF), and Warner–Bratzler shear force (WBSF), respectively, from the Nellore Brazil Breeding Program. The animals have been genotyped using low-density SNP panel (30 k), and subsequently imputed for arrays with 777 k SNPs. Four Bayesian specifications of genomic regression models, namely Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression methods were compared in terms of prediction accuracy using a five folds cross-validation. Prediction accuracy for REA, BF and RF was all similar using the Bayesian Alphabet models, ranging from 0.75 to 0.95. For WBSF, the predictive ability was higher using Bayes B (0.47) than other methods (0.39 to 0.42). Although the prediction accuracies using Markov blanket of SNP markers were lower than those using all SNPs, for WBSF the relative gain was lower than 13%. With a subset of informative SNPs markers, identified using Markov blanket, probably, is possible to capture a large proportion of the genetic variance for WBSF. The development of low-density and customized arrays using Markov blanket might be cost-effective to perform a genomic selection for this trait, increasing the number of evaluated animals, improving the management decisions based on genomic information and applying genomic selection on a large scale.