The Role of Weather in Meningitis Outbreaks in Navrongo, Ghana: A Generalized Additive Modeling Approach

Bacterial (meningococcal) meningitis is a devastating infectious disease with outbreaks occurring annually during the dry season in locations within the ??Meningitis Belt??, a region in sub-Saharan Africa stretching from Ethiopia to Senegal. Meningococcal meningitis occurs from December to May in the Sahel with large epidemics every 5?C10?years and attack rates of up to 1000 infections per 100,000 people. High temperatures coupled with low humidity may favor the conversion of carriage to disease as the meningococcal bacteria in the nose and throat are better able to cross the mucosal membranes into the blood stream. Similarly, respiratory diseases such as influenza and pneumonia might weaken the immune defenses and add to the mucosa damage. Although the transmission dynamics are poorly understood, outbreaks regularly end with the onset of the rainy season and may begin anew with the following dry season. In this paper, we employ a generalized additive modeling approach to assess the association between number of reported meningitis cases and a set of weather variables (relative humidity, rain, wind, sunshine, maximum and minimum temperature). The association is adjusted for air quality (dust, carbon monoxide), as well as varying degrees of unobserved time-varying confounding processes that co-vary with both the disease incidence and weather. We present the analysis of monthly reported meningitis counts in Navrongo, Ghana, from 1998?C2008.     

Comparison of Models Analyzing a Small Number of Observed Meningitis Cases in Navrongo,Ghana

The “meningitis belt” is a region in sub-Saharan Africa where annual outbreaks of meningitis occur, with epidemics observed cyclically. While we know that meningitis is heavily dependent on seasonal trends, the exact pathways for contracting the disease are not fully understood and warrant further investigation. Most previous approaches have used large sample inference to assess impacts of weather on meningitis rates. However, in the case of rare events, the validity of such assumptions is uncertain. This work examines the meningitis trends in the context of rare events, with the specific objective of quantifying the underlying seasonal patterns in meningitis rates. We compare three main classes of models: the Poisson generalized linear model, the Poisson generalized additive model, and a Bayesian hazard model extended to accommodate count data and a changing at-risk population. We compare the accuracy and robustness of the models through the bias, RMSE, and standard deviation of the estimators, and also provide a detailed case study of meningitis patterns for data collected in Navrongo, Ghana.Supplementary materials accompanying this paper appear online.     

Development of vaccination strategies for the management of rabies in African wild dogs

The standard objective of a vaccination strategy is to reduce the reproductive ratio, R₀, defined as the number of secondary host infections arising directly from introduction of a single infected individual into an otherwise fully susceptible population, to below 1. This ensures that only very small outbreaks are likely to arise. However, this objective usually requires a high level of vaccination coverage that is often expensive and logistically difficult to achieve. For the purposes of conserving rare species that are threatened by outbreaks of infectious disease, population persistence may be assured by a vaccination strategy designed to suppress only the largest outbreaks of disease that reduce the population to below a minimum viable population size. Such strategies targeting only a viable minimal ‘core’ of the population are likely to be logistically less demanding. Here we explore how these core vaccination strategies might be designed for African wild dogs (Lycaon pictus), an endangered canid species whose remaining populations are threatened by rabies outbreaks. We develop and analyse a population viability model with an explicit epidemiological model embedded within it. The model predicts that core vaccination campaigns, using vaccines that provide two years of immunity, and targets 30-40% of individuals within a population every 1-2 years would be successful in ensuring persistence of small populations, and required coverage levels can be even lower in larger populations. This strategy appears robust to synchronized introduction of rabies into packs, possible Allee effects, and the use of vaccines providing only one year of immunity.     

Coupling of dry matter and nitrogen accumulation in millet and ryegrass

Abstract

The logistic model has proven very useful in relating dry matter production of warm‐season and cool‐season forage grasses to applied nitrogen (N). A recent extension of the model coupled dry matter and plant N accumulation through a common response coefficient c. The objective of this analysis was to apply the extended model to both warm season pearl millet [Pennisetum typhoides (Burm.) Staph and C. E. Hubb.] and cool season ryegrass [Lolium multiflorum Lam.], and to establish a common response coefficient, c, between accumulation of dry matter and plant N for the two grasses in rotation. Analysis of variance established the validity of this hypothesis. The model accurately described response of dry matter, plant N removal, and plant N concentration to applied N, with an overall correlation coefficient of 0.9954. Furthermore, the model closely described the relationship between yield and plant N removal. The logistic equation is well behaved and simple to use on a pocket calculator. It can be used to estimate yield and plant N removal in evaluation of agricultural practices and the influences on environmental quality.     

Autologistic regression analysis of spatial-temporal binary data via Monte Carlo maximum likelihood

This article considers logistic regression analysis of binary data that are measured on a spatial lattice and repeatedly over discrete time points. We propose a spatial-temporal autologistic regression model and draw statistical inference via maximum likelihood. Due to an unknown normalizing constant in the likelihood function, we use Monte Carlo to obtain maximum likelihood estimates of the model parameters and predictive distributions at future time points. We also use path sampling to estimate the unknown normalizing constant and approximate an information criterion for model assessment. The methodology is illustrated by the analysis of a dataset of mountain pine beetle outbreaks in western Canada.     

A Bayesian multinomial model for analyzing categorical habitat selection data

Modeling the number of uses of discrete habitat types by animals with a multinomial distribution, we illustrate the use of Bayesian methods to estimate selection. An advantage of this approach in assessing selection is the construction of credibility intervals that do not rely on large sample normal theory. In addition, credibility intervals for ranked selection of habitats are easily obtained. Bayes factors and Bayesian p values (posterior predictive values) are used to test the hypothesis of selection for each animal, test selection across all animals and for multiple comparisons among habitats. We compare our method to alternative methods for a real dataset. Freely available WinBUGS software is used to fit the model and test hypotheses.     

Bayesian Methods for Hierarchical Distance Sampling Models

The few distance sampling studies that use Bayesian methods typically consider only line transect sampling with a half-normal detection function. We present a Bayesian approach to analyse distance sampling data applicable to line and point transects, exact and interval distance data and any detection function possibly including covariates affecting detection probabilities. We use an integrated likelihood which combines the detection and density models. For the latter, densities are related to covariates in a log-linear mixed effect Poisson model which accommodates correlated counts. We use a Metropolis-Hastings algorithm for updating parameters and a reversible jump algorithm to include model selection for both the detection function and density models. The approach is applied to a large-scale experimental design study of northern bobwhite coveys where the interest was to assess the effect of establishing herbaceous buffers around agricultural fields in several states in the US on bird densities. Results were compared with those from an existing maximum likelihood approach that analyses the detection and density models in two stages. Both methods revealed an increase of covey densities on buffered fields. Our approach gave estimates with higher precision even though it does not condition on a known detection function for the density model.     

Bayesian multivariate process modeling for prediction of forest attributes

This article investigates multivariate spatial process models suitable for predicting multiple forest attributes using a multisource forest inventory approach. Such data settings involve several spatially dependent response variables arising in each location. Not only does each variable vary across space, they are likely to be correlated among themselves. Traditional approaches have attempted to model such data using simplifying assumptions, such as a common rate of decay in the spatial correlation or simplified cross-covariance structures among the response variables. Our current focus is to produce spatially explicit, tree species specific, prediction of forest biomass per hectare over a region of interest. Modeling such associations presents challenges in terms of validity of probability distributions as well as issues concerning identifiability and estimability of parameters. Our template encompasses several models with different correlation structures. These models represent different hypotheses whose tenability are assessed using formal model comparisons. We adopt a Bayesian hierarchical approach offering a sampling-based inferential framework using efficient Markov chain Monte Carlo methods for estimating model parameters.     

Likelihood-based inference for clustered line transect data

The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference is implemented using Markov chain Monte Carlo (MCMC) methods to obtain efficient estimates of spatial clustering parameters. Uncertainty is addressed using parametric bootstrap or by consideration of posterior distributions in a Bayesian setting. Maximum likelihood estimation and Bayesian inference are compared in an example concerning minke whales in the northeast Atlantic.     

内蒙古东部区粮食产量对气候变化的响应

利用内蒙古东部产粮区25个气象站建站至2005年气象资料与粮豆和主要作物产量资料,分析了作物生长季水热演变规律,及区域作物产量对气候变化的响应。结果表明:(1)近50a该区域水热匹配格局发生变化,特别是近20a暖干化趋势明显;(2)降水是影响该地区粮食生产的关键气象因子,春、夏季降水量的匮乏和生长季高温是粮食生产的主要限制因素;(3)各作物的第一敏感因子不同。小麦和谷子为降水,玉米为夏季高温,马铃薯产量对7-8月温差和4-5月降水反应敏感。(4)据模型推算,当生长季平均最高气温或温差增加1℃时,有可能使玉米气象产量减少102～192kg/hm2,大豆减产87kg/hm2,马铃薯增产55.5kg/hm2。因此,气候暖干化倾向有可能造成该区域农业产量呈下降趋势。研究结果可为有关部门制定应对气候变化策略提供参考。     

近40年鄱阳湖枯水期水体面积变化特征及驱动因素分析

湖泊是维持生态平衡的重要因素,鄱阳湖作为我国最大的淡水湖,由于枯水期提前且持续时间延长导致水体面积发生变化。研究鄱阳湖枯水期水体面积的变化特征及其驱动因素对区域生态环境的稳定及经济发展具有重要意义。利用1973-2018年鄱阳湖枯水期遥感影像数据,采用Mann-Kendall趋势检验法、Mann-Kendall突变分析法及Pettitt突变检验对湖泊水体面积变化特征进行分析,并利用双累积曲线法量化出入湖流量之差和人类活动导致的土地利用的变化对鄱阳湖枯水期水体面积的影响。结果表明:(1)在研究时段内,鄱阳湖枯水期水体面积呈极显著下降趋势(P0.01),突变点年份为2002年(P0.01);(2)出入湖径流量之差多年来呈现不显著的增加趋势;1980-2015年鄱阳湖流域的土地利用情况表现为城乡工建用地增加,其余用地类型均呈现减小趋势;(3)以1977-2002年作为基准期,2003-2016年人类活动导致的土地利用的变化对鄱阳湖枯水期水体面积变化的贡献率为87.48%,是鄱阳湖枯水期水体面积显著减少的主要原因。研究成果为鄱阳湖水资源的合理利用及维持区域生态系统的平衡提供了科学依据。     

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.     

不同气候模式对密云水库流域非点源污染负荷的影响

以密云水库流域内4个气象站1961-2000年40 a的气象特征分析结果为基础,采用统计分析和线性回归的方法,预测流域气候变化趋势,采用任意情景设置法设定25种气候情景(5种温度变化和5种降雨变化的组合情景)和3个水文情景年(丰、平、枯水年)。利用HSPF(hydrologic simulation program-fortran)模型模拟密云水库流域不同气候变化情景下径流量和非点源污染物负荷量的变化情况。结果表明:1)增加20%降雨,能增加73.4%的径流量,而减少20%降雨会减少56.3%的径流,而气温变化对径流和水质负荷影响不是很明显;2)总氮和总磷负荷随径流增加而增大,总磷负荷对径流变化更加敏感,降雨增加20%,总氮和总磷负荷分别增加约70.8%和78.3%;而减少20%降雨,会使得总氮和总磷负荷分别减少约55.3%和57.2%;3)从水文年对比来看,潮河流域丰水年径流量是枯水年的3.1倍,总氮、总磷负荷则分别是枯水年的2.9倍、3.5倍,白河流域丰水年径流量是枯水年的4.6倍,总氮、总磷负荷则分别是枯水年的5.6倍、8.5倍,且年内非点源污染负荷主要集中在汛期,高风险区主要分布在怀柔区、延庆县、滦平县以及密云县,需要对其采取对应的措施来控制非点源污染的影响。     

A landscape model for quantifying the trade-off between conservation needs and economic constraints in the management of a semi-natural grassland community

We present a landscape model to investigate the ecological consequences and costs of different management regimes in semi-natural grasslands. The model integrates dynamic abiotic conditions, management (i.e. disturbance) regime and response of more than 50 characteristic plant and insect species by modelling the dynamics of relevant niche parameters as predictors for species distribution models. We compare our results for exemplary scenarios differing in spatial and temporal scales and exemplary species belonging to different functional groups through several steps of aggregation.Our analysis aims at the question whether an infrequent massive disturbance by rototilling can serve as a less expensive alternative to annual mowing for preserving the characteristic species composition of open dry grasslands in Southern Germany. Rototilling results in a shifting mosaic determining the habitat quality for plant and animal species that may reduce the survival of local or regional populations.For some meadow species as well as the encroaching shrub species, rototilling has a detrimental effect on regional habitat quality. Other species, e.g. weeds and annual pioneers, strongly benefit or show only negligible reaction. Since this is a multi-objective problem, there is a no magic bullet in selecting an optimum scenario of measures. But by visualising the trade-off between ecological consequences and costs, our model is a valuable tool for conservation managers providing a sound scientific basis for management decisions relying on available ecological knowledge. It is also an interesting example for a model describing complex communities in a relatively simple way, simultaneously considering the main driving factors.     

基于相对湿润度指数的云南干旱气候变化特征

基于云南15个代表站1961-2010年气候资料,使用相对湿润度（M）指数和Morlet小波变换方法分析云南干旱气候的时空变化规律和特征.结果表明：雨季M指数主要反映降水对干旱的影响,干季M指数对气温、日照等共同引发的蒸散量变化有相应的响应.云南气候干湿年际波动大、年代际变化明显;雨季M指数主要表现为10～ 16a、6～8a和2～4a的周期性变化,干季M指数的变化周期以8a和4～6a为主;雨季M指数的地区性差别比干季大.云南的严重干旱均为上年雨季（或其末期）M指数偏小、随后的干季M指数典型偏低和当年雨季开始偏晚相叠加的结果.在全球变暖背景下,云南雨季有气候变干的趋势,干季大多区域呈干旱略加强趋势.近年云南多数区域M指数的主要变化周期相继进入谷值期,并与降水偏少同步出现,导致严重干旱发生频率加大.研究结果对云南干旱预测、评估及其风险管理和应用决策具有指导性和实用性.     

近20年塔里木河干流区地下水埋深变化特征及其生态效应研究

基于塔里木河干流区近20年水文生态变化监测资料,研究了其地下水埋深时空变化特征与河道来水的相互关系及其生态效应。结果表明:塔河干流上中游段地下水埋深年度变化呈枯水期3—4月变幅在0.42～0.92 m,汛期7—9月变幅在1.06～3.67 m。经近20年的生态输水,2009—2017年9年间下游段地下水埋深平均抬升了3.75 m,地下水埋深随输水量的变化明显,总体上在输水停止后1月内达到峰值,而后逐渐降低,直至下一次输水才会明显回升。2009年之后的生态输水对下游植被恢复效果明显,下游NDVI平均值由0.05提升至0.15。本研究系统分析了塔里木河干流区生态输水以来区域生态环境对地下水的综合响应,可为区域水资源调控和进一步量化输水效益提供理论依据。     

Conservation of European wild rabbit populations when hunting is age and sex selective

Several predator species at risk of extinction in Southwestern Europe are dependent on the population density of European wild rabbits Oryctolagus cuniculus. Rabbit populations in the region, however, have recently undergone dramatic decreases in population density, which may be exacerbated by hunting. Current hunting policies set the autumn-winter season, just before the start of rabbit reproduction, as the main hunting season, and previous theoretical models have estimated that the current hunting season may have the greatest negative impact on rabbit abundance and should be changed. We utilised a model for rabbit population dynamics to determine the effects of the timing of hunting during two seasons, summer and autumn, on the tendency of rabbit populations to be over-harvested and on the number of rabbits hunted. This model included field estimates of age- and sex-selection biases of hunting by shotgun. Scenarios with different hunting rates and sex- and age-selection probabilities of hunting were simulated for populations with different turnover levels and with and without compensatory mortality mechanisms. Field estimations showed that hunting in summer was juvenile-biased whereas autumn hunting was juvenile- and male-biased. In contrast to previous findings, our modelling results suggested that hunting in autumn may be the most conservative option for harvesting of rabbit populations, since these populations were more prone to be over-harvested during the summer. The differences between the two seasons in number of rabbits hunted were dependent on population dynamics and hunting sex- and age-selection probabilities. Our findings suggest that altering of current hunting policies would not optimise the exploitation or conservation of wild rabbit populations, but that the latter may be improved by some changes in the timing of hunting.     

Seasonal effects on microorganisms in the rhizosphere of two tropical plants in a polyculture agroforestry system in Central Amazonia,Brazil

Rhizosphere and bulk soil samples were taken from two perennial plant species (the palm Bactris gasipaes and the dicot Theobroma grandiflorum) growing in an experimental polyculture agroforestry system in Central Amazonia in the wet and the dry season. In the dry season, the soil water potential in the rhizosphere and the bulk soil was below pF 4.2. Microbial biomass and basal respiration were lower in the dry season than in the wet season. However, the lower metabolic quotient in the dry season indicated a higher metabolic efficiency. While the number of oligotrophic microorganisms was not affected by the season, the number of eutrophic microorganisms in the rhizosphere was lower in the dry season than in the wet season. The substrate utilisation pattern of the bacterial community was also affected by the season. Despite marked differences in root morphology there were no significant differences in any of the microbiological parameters between the two plant species. It is concluded that during the dry season, a large portion of the microbial biomass dies off or enters a dormant stage resulting in a change in community composition.     

A bayesian approach to crop Model calibration under unknown error covariance

Bayesian methods seem well adapted to dynamic system models in general and to crop models in particular, because there is in general prior information about parameter values. The usefulness of a Bayesian approach has often been pointed out, but actual applications are rather rare. A major difficulty is including the elements of the covariance matrix of model errors in the treatment. We treat the specific case of balanced data and an unstructured covariance matrix. In our particular case this is a 3 × 3 matrix. We illustrate two methods for deriving a sample from the joint posterior density for the crop model parameters and the error covariance matrix parameters. The first method is based on importance sampling, the second on Metropolis within Gibbs sampling. We derive an instrumental density for the former and a proposal density for the latter which are adapted to this type of model and data. Both algorithms work well and they give very similar results. The example concerns a model for sunflowers during rapid leaf growth. The ultimate goal is to use the model as a decision aid in predicting disease risk.     

Feral pig predation threatens the indigenous harvest and local persistence of snake-necked turtles in northern Australia

Northern snake-necked turtles (Chelodina rugosa) traditionally provided an important seasonal source of protein for indigenous communities in Arnhem Land, northern Australia. Harvest techniques today differ little from those used historically, harvesting being applied in the late dry season when ephemeral waters have drawn down and turtles are aestivating. Radio-telemetry was used to quantify survival rates of C. rugosa at a traditional turtle harvest site and relate them to harvest, predation by feral pigs (Sus scrofa) and environmental factors. Although turtle survival was positively correlated with body size, the survival of turtles of all sizes and stages of maturity was compromised by pig predation. Seasonal variation in the onset, duration and severity of rainfall and associated influences on periodic drying, are important for C. rugosa survival because such variation influences the timing and intensity of both Aboriginal harvest and pig predation. Contemporary harvest rates of C. rugosa in Arnhem Land by Aboriginal people are very low because pig predation depletes available stocks immediately before Aboriginal harvesting. Aboriginal harvest rates are regulated also by the frequency and timing of ceremonies and other cultural activities that interfere with harvests. Before the arrival of pigs, such relaxation of harvest pressure in years when harvest would otherwise be possible would have contributed to the local abundance and persistence of C. rugosa. In contrast, pig predation is unrelenting, and years of high turtle survival are now restricted only to years of high wet season rainfall.