A hierarchical Bayesian model for forecasting state-level corn yield

Historically, the National Agricultural Statistics Service crop forecasts and estimates have been determined by a group of commodity experts called the Agricultural Statistics Board (ASB). The corn yield forecasts for the “speculative region,” ten states that account for approximately 85 % of corn production, are based on two sets of monthly surveys, a farmer interview survey and a field measurement survey. The members of the ASB subjectively determine a forecast on the basis of a discussion of the survey data and auxiliary information about weather, average planting dates, and crop maturity. The ASB uses an iterative procedure, where initial state estimates are adjusted so that the weighted sum of the final state estimates is equal to a previously-determined estimate for the speculative region. Deficiencies of the highly subjective ASB process are lack of reproducibility and a measure of uncertainty. This paper describes the use of Bayesian methods to model the ASB process in a way that leads to objective forecasts and estimates of the corn yield. First, we use small area estimation techniques to obtain state-level forecasts. Second, we describe a way to adjust the state forecasts so that the weighted sum of the state forecasts is equal to a previously-determined regional forecast. We use several diagnostic techniques to assess the goodness of fit of various models and their competitors. We use Markov chain Monte Carlo methods to fit the models to both historic and current data from the two monthly surveys. Our results show that our methodology can provide reasonable and objective forecasts of corn yields for states in the speculative region.     

A Bayesian hierarchical model for monitoring harbor seal changes in Prince William Sound, Alaska

Bayesian hierarchical models were used to assess trends of harbor seals, Phoca vitulina richardsi, in Prince William Sound, Alaska, following the 1989 Exxon Valdez oil spill. Data consisted of 4–10 replicate observations per year at 25 sites over 10 years. We had multiple objectives, including estimating the effects of covariates on seal counts, and estimating trend and abundance, both per site and overall. We considered a Bayesian hierarchical model to meet our objectives. The model consists of a Poisson regression model for each site. For each observation the logarithm of the mean of the Poisson distribution was a linear model with the following factors: (1) intercept for each site and year, (2) time of year, (3) time of day, (4) time relative to low tide, and (5) tide height. The intercept for each site was then given a linear trend model for year. As part of the hierarchical model, parameters for each site were given a prior distribution to summarize overall effects. Results showed that at most sites, (1) trend is down; counts decreased yearly, (2) counts decrease throughout August, (3) counts decrease throughout the day, (4) counts are at a maximum very near to low tide, and (5) counts decrease as the height of the low tide increases; however, there was considerable variation among sites. To get overall trend we used a weighted average of the trend at each site, where the weights depended on the overall abundance of a site. Results indicate a 3.3% decrease per year over the time period.     

A hierarchical Bayesian non-linear spatio-temporal model for the spread of invasive species with application to the Eurasian Collared-Dove

The spread of invasive species is a long studied subject that garners much interest in the ecological research community. Historically the phenomenon has been approached using a purely deterministic mathematical framework (usually involving differential equations of some form). These methods, while scientifically meaningful, are generally highly simplified and fail to account for uncertainty in the data and process, of which our knowledge could not possibly exist without error. We propose a hierarchical Bayesian model for population spread that accommodates data sources with errors, dependence structures between population dynamics parameters, and takes into account prior scientific understanding via non-linear relationships between model parameters and space-time response variables. We model the process (i.e., the bird population in this case) as a Poisson response with spatially varying diffusion coefficients as well as a logistic population growth term using a common reaction-diffusion equation that realistically mimics the ecological process. We focus the application on the ongoing invasion of the Eurasian Collared-Dove.     

Modelling volatility of growth rate in atmospheric carbon dioxide concentrations in a Bayesian approach

Atmospheric gases, such as carbon dioxide, ozone, methane, nitrous oxide, and etc., create a natural greenhouse effect and cause climate change. Therefore, modelling behavior of these gases could help policy makers to control greenhouse effects. In a Bayesian framework, we analyse and model conditional variance of growth rate in atmospheric carbon dioxide concentrations (ACDC) using monthly data from a subset of the well known Mauna Loa atmosphere carbon dioxide record. The conditional variance of ACDC monthly growth rate is modelled using the autoregressive conditional heteroscedasticity (ARCH), generalized ARCH model (GARCH) and a few variants of stochastic volatility (SV) models. Smooth transition ARCH and GARCH models are shown to be able to capture the dynamics in the conditional variance in ACDC level growth rate and to improve the forecast performance of ACDC growth rate.     

A Bayesian hierarchical model of Antarctic fur seal foraging and pup growth related to sea ice and prey abundance

We created a Bayesian hierarchical model (BHM) to investigate ecosystem relationships between the physical ecosystem (sea ice extent), a prey measure (krill density), predator behaviors (diving and foraging effort of female Antarctic fur seals, Arctocephalus gazella, with pups) and predator characteristics (mass of maternal fur seals and pups). We collected data on Antarctic fur seals from 1987/1988 to 1994/1995 at Seal Island, Antarctica. The BHM allowed us to link together predators and prey into a model that uses all the data efficiently and accounts for major sources of uncertainty. Based on the literature, we made hypotheses about the relationships in the model, which we compared with the model outcome after fitting the BHM. For each BHM parameter, we calculated the mean of the posterior density and the 95% credible interval. Our model confirmed others' findings that increased sea ice was related to increased krill density. Higher krill density led to reduced dive intensity of maternal fur seals, as measured by dive depth and duration, and to less time spent foraging by maternal fur seals. Heavier maternal fur seals and lower maternal foraging effort resulted in heavier pups at 22 d. No relationship was found between krill density and maternal mass, or between maternal mass and foraging effort on pup growth rates between 22 and 85 days of age. Maternal mass may have reflected environmental conditions prior to the pup provisioning season, rather than summer prey densities. Maternal mass and foraging effort were not related to pup growth rates between 22 and 85 d, possibly indicating that food was not limiting, food sources other than krill were being used, or differences occurred before pups reached age 22 d.     

Spatiotemporal calibration of atmospheric nitrogen dioxide concentration estimates from an air quality model for Connecticut

Environmental and Ecological Statistics - A spatiotemporal calibration and resolution refinement model was fitted to calibrate nitrogen dioxide ($$\hbox {NO}_2$$) concentration estimates from the...     

A Bayesian hierarchical approach to quantifying stakeholder attitudes toward conservation in the presence of reporting error

Stakeholder support is vital for achieving conservation success, yet there are few reliable mechanisms to monitor stakeholder attitudes toward conservation. Approaches used to assess attitudes rarely account for bias arising from reporting error, which can lead to falsely reporting a positive attitude toward conservation (false-positive error) or not reporting a positive attitude when the respondent has a positive attitude toward conservation (false-negative error). Borrowing from developments in applied conservation science, we used a Bayesian hierarchical model to quantify stakeholder attitudes as the probability of having a positive attitude toward wildlife notionally (or in abstract terms) and at localized scales while accounting for reporting error. We compared estimates from our model, Likert scores, and naïve estimates (i.e., proportion of respondents reporting a positive attitude in at least 1 question that was only susceptible to false-negative error) with true stakeholder attitudes through simulations. We then applied the model in a survey of tea estate staff on their attitudes toward Asian elephants (Elephas maximus) in the Kaziranga–Karbi Anglong landscape of northeast India. In simulations, Bayesian model estimates of stakeholder attitudes toward wildlife were less biased than naïve estimates or Likert scores. After accounting for reporting errors, we estimated the probability of having a positive attitude toward elephants notionally as 0.85 in the Kaziranga landscape, whereas the proportion of respondents who had positive attitudes toward elephants at a localized scale was 0.50. In comparison, without accounting for reporting errors, naïve estimates of proportions of respondents with positive attitudes toward elephants were 0.69 and 0.23 notionally and at local scales, respectively. False (positive and negative) reporting probabilities were consistently not 0 (0.22–0.68). Regular and reliable assessment of stakeholder attitudes–combined with inference on drivers of positive attitudes–can help assess the success of initiatives aimed at facilitating human behavioral change and inform conservation decision making.     

Multimethod,multistate Bayesian hierarchical modeling approach for use in regional monitoring of wolves

In many cases, the first step in large‐carnivore management is to obtain objective, reliable, and cost‐effective estimates of population parameters through procedures that are reproducible over time. However, monitoring predators over large areas is difficult, and the data have a high level of uncertainty. We devised a practical multimethod and multistate modeling approach based on Bayesian hierarchical‐site‐occupancy models that combined multiple survey methods to estimate different population states for use in monitoring large predators at a regional scale. We used wolves (Canis lupus) as our model species and generated reliable estimates of the number of sites with wolf reproduction (presence of pups). We used 2 wolf data sets from Spain (Western Galicia in 2013 and Asturias in 2004) to test the approach. Based on howling surveys, the naïve estimation (i.e., estimate based only on observations) of the number of sites with reproduction was 9 and 25 sites in Western Galicia and Asturias, respectively. Our model showed 33.4 (SD 9.6) and 34.4 (3.9) sites with wolf reproduction, respectively. The number of occupied sites with wolf reproduction was 0.67 (SD 0.19) and 0.76 (0.11), respectively. This approach can be used to design more cost‐effective monitoring programs (i.e., to define the sampling effort needed per site). Our approach should inspire well‐coordinated surveys across multiple administrative borders and populations and lead to improved decision making for management of large carnivores on a landscape level. The use of this Bayesian framework provides a simple way to visualize the degree of uncertainty around population‐parameter estimates and thus provides managers and stakeholders an intuitive approach to interpreting monitoring results. Our approach can be widely applied to large spatial scales in wildlife monitoring where detection probabilities differ between population states and where several methods are being used to estimate different population parameters.     

大连市区大气氮湿沉降研究

雨水的主要成分是水并含有低浓度的含氮化合物,降落在春夏秋3季的雨水对植物生长有一定的影响.雨水除满足植物生理生态需水外,也为植物提供了一定的氮营养.确定雨水中的含氮量多少时空分布状况对植物生长状况的影响不论从环境科学还是植物生长科学都有一定的意义.通过2009—2010年对位于我国辽东半岛最南端的大连市区的雨水连续收集,利用流动分析仪测定雨水中的活性氮化合物含量及变化趋势,可能对生态环境有一定的影响.结果表明:两年的降雨次数分别为34和51次,降雨量分别为687.55和630.22 mm,介于常年年平均降雨量(550~950 mm)的范围内.雨水的总含氮量分别为22.94和41.65 kg·hm-2,总无机含氮量分别为17.67和18.67 kg·hm-2,其中,前1年铵态氮和硝态氮的通量分别为7.99和9.68 kg·hm-2;后1年铵态氮和硝态氮的通量分别为7.72和10.95 kg·hm-2,降雨量大的7和8月份氮沉降通量大,氮沉降通量的变化趋势与降雨量一致.降氮强度各月分布不均,2年的月平均值分别为0.42和0.55 mg·L-1·h-1,总氮的沉降强度按季节排序为冬季>秋季>春季>夏季.随降雨沉降的活性氮化合物可以作为植物生长的氮源,每年平均沉降的总氮相当于使用69.15 kg·hm-2的尿素.一般来说雨水中的无机氮化合物能被植物直接利用,含量多少对不同生态系统产生不同的影响;在农田生态系统有利于作物和杂草的生长,在江河湖海边缘有利于植被的生长,产生富营养化.大连市区2年的月平均降氮质量浓度为4.90 mg·L-1,已远远超出富营养水体中氮质量浓度的阈值,周边水体存在富营养化的隐患.     

广州市园林绿化植物苗木对二氧化硫和二氧化氮吸收能力分析

采用人工模拟熏气法,研究了36种广州市园林绿化植物对SO2和NO2气体吸收净化能力,以系统聚类分析方法为依据,将参试植物的吸收净化能力划分为强性、较强、中等、较弱及弱5个等级。结果显示,在不同SO2质量浓度（0.259和0.448 mg.m-3）环境下,黄槐、鸡冠刺桐、红花银桦、木棉、红千层、大花紫薇、复羽叶栾树吸收SO2能力具有强或较强的能力,而罗汉松、竹柏、深山含笑、乐昌含笑、观光木、樟树、阴香、双翼豆、印度紫檀、大花五桠果、长芒杜英、五月茶、海南蒲桃、芒果、海南红豆、糖胶树和幌伞枫吸收SO2能力表现为较弱或弱;在NO2质量浓度（0.149和0.428 mg.m-3）环境下,黄槐、黄葛榕、红花银桦、红千层、麻楝、复羽叶栾树、大花紫薇和小叶榄仁吸收净化NO2能力为较强或强,而深山含笑、五月茶、芒果、海南红豆、糖胶树和桂花叶片对NO2吸收能力表现为较弱或弱。在不同SO2和NO2浓度环境下,黄槐、红花银桦、红千层、复羽叶栾树和大花紫薇叶片对SO2和NO2吸收净化能力表现为强或较强,而深山含笑、五月茶、芒果、海南红豆、糖胶树叶片对SO2和NO2吸收净化能力为较弱或弱。研究结果为珠三角城市功能型园林植物选择和广东生态景观林带建设提供科学依据。     

A Bayesian mixture model for missing data in marine mammal growth analysis

Much of what is known about bottle nose dolphin (Tursiops truncatus) anatomy and physiology is based on necropsies from stranding events. Measurements of total body length, total body mass, and age are used to estimate growth. It is more feasible to retrieve and transport smaller animals for total body mass measurement than larger animals, introducing a systematic bias in sampling. Adverse weather events, volunteer availability, and other unforeseen circumstances also contribute to incomplete measurement. We have developed a Bayesian mixture model to describe growth in detected stranded animals using data from both those that are fully measured and those not fully measured. Our approach uses a shared random effect to link the missingness mechanism (i.e. full/partial measurement) to distinct growth curves in the fully and partially measured populations, thereby enabling drawing of strength for estimation. We use simulation to compare our model to complete case analysis and two common multiple imputation methods according to model mean square error. Results indicate that our mixture model provides better fit both when the two populations are present and when they are not. The feasibility and utility of our new method is demonstrated by application to South Carolina strandings data.     

Bayesian nonparametric inference for the overlap of daily animal activity patterns

The study of the interaction among species is an active area of research in Ecology. In particular, it is of interest to evaluate the overlap of their ecological niches. Temporal activity is one of the niche’s axes most commonly used to explore ecological segregation among animal species, and many contributions focus on the overlap of this variable. Once the information of the temporal activity is obtained in the wild, the data is treated as a random sample. There exist different methods to estimate the overlap. Specifically, in the case of two species, one possibility is to estimate the density of the temporal activity of each species and then evaluate the overlap between these density functions. This leads naturally to the analysis of circular data. Most of the procedures currently in use impose some rather restrictive assumptions on the probabilistic models used to describe the phenomena, and only provide approximate measures of the uncertainty involved in the process. In this article, we propose a Bayesian nonparametric approach which incorporates a well-defined noninformative prior. We take advantage of the data structure to define such a prior in terms of the predictive distribution. To the best of our knowledge, this is a novel approach. Our procedure is compared with a well-known method using simulated data, and applied to the analysis of real camera-trap data concerning two mammalian species from the El Triunfo biosphere reserve (Chiapas, Mexico).     

A simulation model for the effects of burning on the phosphorus and nitrogen cycle of a heathland ecosystem

In this paper, a simulation model is presented for nutrient cycling in heathland ecosystems. The results of simulations are compared with field data of phosphorus and nitrogen accumulation in different compartments of the system in the years after burning, given in the literature on this subject (Chapman, 1967; 1970). It is shown that for the cycling of P the system returns slowly to equilibrium after an initial disturbance. The time span of about 30 years that is required to reach equilibrium is in agreement with the field data. However, P accumulation in living above-ground biomass and litter is overestimated by the model in the first years after burning. Nitrogen accumulation in biomass and litter is systematically underestimated by the model.The results are discussed with reference to the processes in nutrient cycling that are insufficiently understood, and with reference to the management of heathlands.     

A roadmap for the conservation of freshwater mussels in Europe

Europe has a long history of human pressure on freshwater ecosystems. As pressure continues to grow and new threats emerge, there is an urgent need for conservation of freshwater biodiversity and its ecosystem services. However, whilst some taxonomic groups, mainly vertebrates, have received a disproportionate amount of attention and funds, other groups remain largely off the public and scientific radar. Freshwater mussels (Bivalvia, Unionida) are an alarming example of this conservation bias and here we point out six conceptual areas that need immediate and long-term attention: knowledge, threats, socioeconomics, conservation, governance and education. The proposed roadmap aims to advance research, policy and education by identifying the most pressing priorities for the short- and long-term conservation of freshwater mussels across Europe.     

曝气复氧对底泥氮素生物地球化学循环影响的作用机制研究

通过分析底泥氮污染物释放规律和转化过程,以及底泥生境、氮形态变化和氮循环功能微生物群落结构变化的规律,探讨了不同曝气复氧条件影响底泥氮生物地球化学循环的生物代谢、物理化学联合作用的机制。结果表明：曝气复氧对底泥中氮的生物地球化学循环影响是一个包括微生物代谢作用和物理化学作用的复杂联合作用过程。水体好氧环境的改变主要引起参与底泥氮循环的硝化、亚硝化和反硝化功能菌群群落结构的演变,对异养菌和氨化菌的影响不大,证明环境好氧条件的改变对底泥有机质生物分解产生氨氮的微生物代谢过程影响不大,主要对底泥释放的氨氮硝化、反硝化等生物转化过程产生大的影响。不同溶解氧条件下,底泥释放的氮素在微生物作用下主要以NH4＋-N和NO3--N的形式进入试验体系,并在特定的氧化还原电位（临界值-200 mV）和pH（临界值6.70）条件下通过物理化学作用在底泥中以离子交换态氮（IEF-N）、碳酸盐结合态氮（CF-N）、铁锰氧化态氮（IMOF-N）及有机态和硫化物结合态氮（OSF-N）等不同形态氮相互转化,同时,在氮的转化和循环过程中部分输入上覆水体。在低溶解氧组实验条件下[ρ（DO）〈0.5 mg.L-1],底泥向水体输出氮总量为底泥可转化态氮的19.7%,主要为氨氮,最大释放速率达到289.13 mg.m-2.d-1,释放的质量浓度可达到18.8 mg.L-1;好氧条件下（DO饱和）,底泥向水体输出氮总量为底泥可转化态氮的1.8%;好氧-缺氧条件下为11.7%,主要以N2的形式释出系统。     

A simulation model for projecting changes in salinity concentrations and species dominance in the coastal margin habitats of the Everglades

Sharp boundaries typically separate the salinity tolerant mangroves from the salinity intolerant hardwood hammock species, which occupy the similar geographical areas of southern Florida. Evidence of strong feedback between tree community-type and the salinity of the unsaturated (vadose) zone of the soil suggests that a severe disturbance that significantly tilts the salinity in the vadose zone might cause a shift from one vegetation type to the other. In this study, a model based upon the feedback dynamics between vegetation and salinity of the vadose zone of the soil was used to take account of storm surge events to investigate the mechanisms that by which this large-scale disturbance could affect the spatial pattern of hardwood hammocks and mangroves. Model simulation results indicated that a heavy storm surge that completely saturated the vadose zone at 30 ppt for 1 day could lead to a regime shift in which there is domination by mangroves of areas previously dominated by hardwood hammocks. Lighter storm surges that saturated the vadose zone at less than 7 ppt did not cause vegetation shifts. Investigations of model sensitivity analysis indicated that the thickness of the vadose zone, coupled with precipitation, influenced the residence time of high salinity in the vadose zone and therefore determined the rate of mangrove domination. The model was developed for a southern Florida coastal ecosystem, but its applicability may be much broader.     

Estimating an homogeneous series of a population abundance indicator despite changes in data collection procedure: A hierarchical Bayesian modelling approach

Abundance indicators are required both to assess and to manage wild populations. As new techniques are developed and teams in charge of gathering the data change, data collection procedures (DCPs) can evolve in space and time. How to estimate an homogeneous series of abundance indicator despite changes in DCP? To tackle this question a hierarchical Bayesian modelling (HBM) approach is proposed. It integrates multiple DCPs in order to derive a single abundance indicator that can be compared over space and time irrespective of the DCP used. Compared to single DCP models, it takes further advantage for abundance estimation of the joint treatment of a larger set of spatio-temporal units. After presenting the general formulation of our HBM approach, it is applied to the juvenile Atlantic salmon (Salmo salar L.) population of the River Nivelle (France). Posterior model checking, using χ² discrepancy measure, do not reveal any inadequacy between the model and the data. Despite a change in the DCP used (successive removals to catch-per-unit of effort), a unique abundance indicator for the 425 spatio-temporal units (site × year) sampled over twenty-four years (1985-2008) is estimated. The HBM approach allows the assessment of precision of the abundance estimates and shows variation between DCPs: a reduction in precision is observed during the most recent years (2005-2008) when only the catch-per-unit of effort DCP was used. The merits and generality of our HBM approach are discussed. We contend it extends previous single DCP models or inter-calibration of two DCPs, and it could be applied to a wide range of specific situations (taxon and DCPs).     

A proposed methodology for assessing the quality of wildlife habitat

Measurement of the quality and quantity of wildlife habitat is a necessity in planning and analyzing water resource projects. A methodology that provides a means of examining the following major components of habitat quality is proposed: (1) the quantity of land uses, (2) the degree of interspersion of land use, aand (3) the stage of land management and vegetative types. The model can be calibrated to a specific region by experienced wildlife biologists and applied by field technicians using aerial photographs and field evaluation of randomly located points. Biologists familiar with a region develop transformation curves relating variables that represent the above three major components to standardizing factors that range from 0 to 1; these transformation curves reflect the sensitivity of wildlife populations to determinants of habitat quality. The methodology also provides the means for biologists to weigh each components as to its relative importance to the wildlife group or species. A weighted geometric mean of the components provides an indication of the overall quality of the habitat. Changes in the habitat components that may occur because of alternative project actions can be used to evaluate the effectiveness of the alternatives. The change in the index of habitat quality will indicate the need for mitigation in water resource projects. The system then can be used to identify areas for mitigation and the effects of mitigating measures.     

不同施氮量对饲料稻糙米蛋白质含量的影响及其机理

采用田间小区试验探讨了"氮中量施肥法"(N,P2O5,K2O施用量为190,90,100 kg·hm-2)、"氮高量施肥法"(N,P2O5,K2O施用量为210,90,100 kg-hm-2)、"氮低量施肥法"(N,P2O5,K2O施用量为170,90,100kg·hm-2)对饲料稻威优198糙米蛋白质质量分数的影响及其机理.结果表明,与氮高量施肥法和氮低量施肥法相比,氮中量施肥法能明显促进孕穗期、乳熟期水稻旗叶中硝酸还原酶,乳熟期旗叶中蛋白水解酶活性,齐穗期旗叶谷氨酰胺合成酶以及齐穗期和乳熟期籽粒中谷氮酰胺合成酶活性,明显促进水稻对氮素营养的吸收与转运,显著提高糙米中蛋白质质量分数和蛋白质质量分数.氮中量施肥法处理糙米蛋白质质量分数分别比氮高量施肥法处理和氮低量施肥法处理提高了 1.39%和17.93%,籽粒产量分别提高了 12.02%和8.47%,糙米蛋白质产量分别提高了12.54%和18.32%.研究表明,氮中量施肥法能满足饲料稻高蛋白高产栽培的需要.