A review of data needed to parameterize a dynamic model of measles in developing countries

Background

Dynamic models of infection transmission can project future disease burden within a population. Few dynamic measles models have been developed for low-income countries, where measles disease burden is highest. Our objective was to review the literature on measles epidemiology in low-income countries, with a particular focus on data that are needed to parameterize dynamic models.

Methods

We included age-stratified case reporting and seroprevalence studies with fair to good sample sizes for mostly urban African and Indian populations. We emphasized studies conducted before widespread immunization. We summarized age-stratified attack rates and seroprevalence profiles across these populations. Using the study data, we fitted a "representative" seroprevalence profile for African and Indian settings. We also used a catalytic model to estimate the age-dependent force of infection for individual African and Indian studies where seroprevalence was surveyed. We used these data to quantify the effects of population density on the basic reproductive number R ₀.

Results

The peak attack rate usually occurred at age 1 year in Africa, and 1 to 2 years in India, which is earlier than in developed countries before mass vaccination. Approximately 60% of children were seropositive for measles antibody by age 2 in Africa and India, according to the representative seroprevalence profiles. A statistically significant decline in the force of infection with age was found in 4 of 6 Indian seroprevalence studies, but not in 2 African studies. This implies that the classic threshold result describing the critical proportion immune (p _c) required to eradicate an infectious disease, p _c = 1-1/R ₀, may overestimate the required proportion immune to eradicate measles in some developing country populations. A possible, though not statistically significant, positive relation between population density and R ₀ for various Indian and African populations was also found. These populations also showed a similar pattern of waning of maternal antibodies. Attack rates in rural Indian populations show little dependence on vaccine coverage or population density compared to urban Indian populations. Estimated R ₀ values varied widely across populations which has further implications for measles elimination.

Conclusions

It is possible to develop a broadly informative dynamic model of measles transmission in low-income country settings based on existing literature, though it may be difficult to develop a model that is closely tailored to any given country. Greater efforts to collect data specific to low-income countries would aid in control efforts by allowing highly population-specific models to be developed.     

Balancing Evidence and Uncertainty when Considering Rubella Vaccine Introduction

Background

Despite a safe and effective vaccine, rubella vaccination programs with inadequate coverage can raise the average age of rubella infection; thereby increasing rubella cases among pregnant women and the resulting congenital rubella syndrome (CRS) in their newborns. The vaccination coverage necessary to reduce CRS depends on the birthrate in a country and the reproductive number, R₀, a measure of how efficiently a disease transmits. While the birthrate within a country can be known with some accuracy, R₀ varies between settings and can be difficult to measure. Here we aim to provide guidance on the safe introduction of rubella vaccine into countries in the face of substantial uncertainty in R₀.

Methods

We estimated the distribution of R₀ in African countries based on the age distribution of rubella infection using Bayesian hierarchical models. We developed an age specific model of rubella transmission to predict the level of R₀ that would result in an increase in CRS burden for specific birth rates and coverage levels. Combining these results, we summarize the safety of introducing rubella vaccine across demographic and coverage contexts.

Findings

The median R₀ of rubella in the African region is 5.2, with 90% of countries expected to have an R₀ between 4.0 and 6.7. Overall, we predict that countries maintaining routine vaccination coverage of 80% or higher are can be confident in seeing a reduction in CRS over a 30 year time horizon.

Conclusions

Under realistic assumptions about human contact, our results suggest that even in low birth rate settings high vaccine coverage must be maintained to avoid an increase in CRS. These results lend further support to the WHO recommendation that countries reach 80% coverage for measles vaccine before introducing rubella vaccination, and highlight the importance of maintaining high levels of vaccination coverage once the vaccine is introduced.     

Estimates of subtropical forest biomass based on airborne LiDAR and Landsat 8 OLI data

 快速、定量、精确地估算区域森林生物量一直是森林生态功能评价以及碳储量研究的重要问题。该研究基于机载激光雷达(LiDAR)点云与Landsat 8 OLI多光谱数据, 借助江苏省常熟市虞山地区55块调查样地数据, 首先提取并分析了87个特征变量(53个OLI特征变量, 34个LiDAR特征变量)与森林地上、地下生物量的Pearson’s相关系数以进行变量优选, 然后利用多元逐步回归法建立森林生物量估算模型(OLI生物量估算模型和LiDAR生物量估算模型), 并与基于两种数据建立的综合生物量估算模型的结果进行比较, 讨论预测结果及其精确性。结果表明: 3种模型(OLI模型、LiDAR模型和综合模型)在所有样地无区分分析时, 地上和地下生物量的估算精度均达到0.4以上, 基于不同森林类型(针叶林、阔叶林、混交林)分析时地上和地下生物量的估算精度均有明显提高, 达到0.67及以上。利用分森林类型模型估算生物量, 综合生物量估算模型精度(地上生物量: R²为0.88; 地下生物量: R²为0.92)优于OLI生物量估算模型(地上生物量: R²为0.73; 地下生物量: R²为0.81)和LiDAR生物量估算模型(地上生物量: R²为0.86; 地下生物量: R²为0.83)。     

Using empirical social contact data to model person to person infectious disease transmission: An illustration for varicella

With the aim to improve dynamic models for infections transmitted predominantly through non-sexual social contacts, we compared three popular model estimation methods in how well they fitted seroprevalence data and produced estimates for the basic reproduction number R₀ and the effective vaccination level required for elimination of varicella. For two of these methods, interactions between age groups were parameterized using empirical social contact data whereas for the third method we used the current standard approach of imposing a simplifying structure on the ‘Who Acquires Infection From Whom’ (WAIFW) matrix. The first method was based on solving a set of differential equations to obtain an equilibrium value of the proportion of susceptibles. The second method was based on finding a solution for the age-specific force of infection using the formula of the mass action principle by means of iteration. Both solutions were contrasted with observed age-specific seroprevalence data. The best fit of the WAIFW matrix was obtained with contacts involving touching, and lasting longer than 15 min per day. Plausible values for R₀ for varicella in Belgium ranged from 7.66 to 13.44. Both approaches based on empirical social contact data provided a better fit to seroprevalence data than the current standard approach.     

Estimating Dengue Transmission Intensity from Sero-Prevalence Surveys in Multiple Countries

Background

Estimates of dengue transmission intensity remain ambiguous. Since the majority of infections are asymptomatic, surveillance systems substantially underestimate true rates of infection. With advances in the development of novel control measures, obtaining robust estimates of average dengue transmission intensity is key for assessing both the burden of disease from dengue and the likely impact of interventions.

Methodology/Principal Findings

The force of infection (λ) and corresponding basic reproduction numbers (R₀) for dengue were estimated from non-serotype (IgG) and serotype-specific (PRNT) age-stratified seroprevalence surveys identified from the literature. The majority of R₀ estimates ranged from 1–4. Assuming that two heterologous infections result in complete immunity produced up to two-fold higher estimates of R₀ than when tertiary and quaternary infections were included. λ estimated from IgG data were comparable to the sum of serotype-specific forces of infection derived from PRNT data, particularly when inter-serotype interactions were allowed for.

Conclusions/Significance

Our analysis highlights the highly heterogeneous nature of dengue transmission. How underlying assumptions about serotype interactions and immunity affect the relationship between the force of infection and R₀ will have implications for control planning. While PRNT data provides the maximum information, our study shows that even the much cheaper ELISA-based assays would provide comparable baseline estimates of overall transmission intensity which will be an important consideration in resource-constrained settings.     

Isometric scaling of above- and below-ground biomass at the individual and community levels in the understorey of a sub-tropical forest

Background and Aims Empirical studies and allometric partitioning (AP) theory indicate that plant above-ground biomass (M_A) scales, on average, one-to-one (isometrically) with below-ground biomass (M_R) at the level of individual trees and at the level of entire forest communities. However, the ability of the AP theory to predict the biomass allocation patterns of understorey plants has not been established because most previous empirical tests have focused on canopy tree species or very large shrubs.Methods In order to test the AP theory further, 1586 understorey sub-tropical forest plants from 30 sites in south-east China were harvested and examined. The numerical values of the scaling exponents and normalization constants (i.e. slopes and y-intercepts, respectively) of log–log linear M_A vs. M_R relationships were determined for all individual plants, for each site, across the entire data set, and for data sorted into a total of 19 sub-sets of forest types and successional stages. Similar comparisons of M_A/M_R were also made.Key Results The data revealed that the mean M_A/M_R of understorey plants was 2·44 and 1·57 across all 1586 plants and for all communities, respectively, and M_A scaled nearly isometrically with respect to M_R, with scaling exponents of 1·01 for all individual plants and 0·99 for all communities. The scaling exponents did not differ significantly among different forest types or successional stages, but the normalization constants did, and were positively correlated with M_A/M_R and negatively correlated with scaling exponents across all 1586 plants.Conclusions The results support the AP theory’s prediction that M_A scales nearly one-to-one with M_R (i.e. M_A ∝ M_R ^≈1·0) and that plant biomass partitioning for individual plants and at the community level share a strikingly similar pattern, at least for the understorey plants examined in this study. Furthermore, variation in environmental conditions appears to affect the numerical values of normalization constants, but not the scaling exponents of the M_A vs. M_R relationship. This feature of the results suggests that plant size is the primary driver of the M_A vs. M_R biomass allocation pattern for understorey plants in sub-tropical forests.     

Estimating aboveground forest biomass carbon and fire consumption in the U.S. Utah High Plateaus using data from the Forest Inventory and Analysis Program,Landsat, and LANDFIRE

The concentrations of CO₂ and other greenhouse gases in the atmosphere have been increasing and greatly affecting global climate and socio-economic systems. Actively growing forests are generally considered to be a major carbon sink, but forest wildfires lead to large releases of biomass carbon into the atmosphere. Aboveground forest biomass carbon (AFBC), an important ecological indicator, and fire-induced carbon emissions at regional scales are highly relevant to forest sustainable management and climate change. It is challenging to accurately estimate the spatial distribution of AFBC across large areas because of the spatial heterogeneity of forest cover types and canopy structure. In this study, Forest Inventory and Analysis (FIA) data, Landsat, and Landscape Fire and Resource Management Planning Tools Project (LANDFIRE) data were integrated in a regression tree model for estimating AFBC at a 30-m resolution in the Utah High Plateaus. AFBC were calculated from 225 FIA field plots and used as the dependent variable in the model. Of these plots, 10% were held out for model evaluation with stratified random sampling, and the other 90% were used as training data to develop the regression tree model. Independent variable layers included Landsat imagery and the derived spectral indicators, digital elevation model (DEM) data and derivatives, biophysical gradient data, existing vegetation cover type and vegetation structure. The cross-validation correlation coefficient (r value) was 0.81 for the training model. Independent validation using withheld plot data was similar with r value of 0.82. This validated regression tree model was applied to map AFBC in the Utah High Plateaus and then combined with burn severity information to estimate loss of AFBC in the Longston fire of Zion National Park in 2001. The final dataset represented 24 forest cover types for a 4 million ha forested area. We estimated a total of 353 Tg AFBC with an average of 87 MgC/ha in the Utah High Plateaus. We also estimated that 8054 Mg AFBC were released from 2.24 km² burned forest area in the Longston fire. These results demonstrate that an AFBC spatial map and estimated biomass carbon consumption can readily be generated using existing database. The methodology provides a consistent, practical, and inexpensive way for estimating AFBC at 30-m resolution over large areas throughout the United States.     

Ecosystem respiration and its controlling factors in a coniferous and broad-leaved mixed forest in Dinghushan, China

Accurate estimation of ecosystem respiration (R_eco) in forest ecosysteM_s is critical for validating terrestrial carbon models. Continuous eddy covariance measuremenT_s of R_eco were conducted in a coniferous and broad-leaved mixed forest located in Dinghushan Nature Reserve of southern China. R_eco was estimated and the controlling environmental factors were analyzed based on two years' data from 2003 to 2004. Major resulT_s included that: (1) R_eco was affected by soil temperature, soil moisture, canopy air temperature and humidity, where soil temperature at 5 cm depth was the dominant factor. (2) The exponential equation, Van't Hoff equation, Arrhenius equation and Lyold-Talor equation can be used to describe the relationship between R_eco and temperature factors with similar statistical significance, while Lyold-Talor equation was the most sensitive to the temperature index (Q₁₀). (3) The multiplicative model driven by soil temperature (T_s) and soil moisture (M_s) was more corresponsive to R_eco, which explained that there were more R_eco variations than Lyold-Talor equation, both for higher and lower M_s. However, there was no statistical difference between the two models. (4) Annually accumulated R_eco of the mixed forest in 2003 was estimated as 1100–1135.6 gC m^?2 a^?1 by using daytime data, which was 12%–25% higher than R_eco (921–975 gC m^?2 a^?1) estimated by using nighttime data. The resulT_s suggested that using daytime data to estimate R_eco can avoid the common underestimation problem caused by using eddy covariance methods. The study provides a basic method for further study on accurate estimation of net ecosystem CO₂ exchange (NEE) in the coniferous and broad-leaved mixed forest in southern China.     

Evaluation of nitrogen excretion equations for ryegrass pasture-fed dairy cows

Accurate and precise estimates of nitrogen (N) excretion in faeces and urine of dairy cattle may provide direct tools to improve N management and thus, to mitigate environmental pollution from dairy production. Empirical equations of N excretion have been evaluated for indoor dairy cattle but there is no evaluation for cows fed high proportions of fresh forage. Therefore, the objective of the current study was to evaluate N excretion equations with a unique data set of zero-grazing experiments. Through literature searches, 89 predictive equations were identified from 13 studies. An independent data set was developed from seven zero-grazing experiments with, in total, 55 dairy Holstein-Friesian cows. Models’ performance was evaluated with statistics derived from a mixed-effect model and a simple regression analysis model. Squared sample correlation coefficients were used as indicators of precision and based on either the best linear unbiased predictions (R²_BLUP) or model-predicted estimates (R²_MDP) derived from the mixed model and simple regression analysis, respectively. The slope (β₀), the intercept (β₁) and the root mean square prediction error (RMSPE_m%) were calculated with the mixed-effect model and used to assess accuracy. The root mean square prediction error (RMSPE_sr%) and the decomposition of the mean square prediction error were calculated with the simple regression analysis and were used to estimate the error due to central tendency (mean bias), regression (systematic bias), and random variation. Concordance correlation coefficient (CCC) were also calculated with the simple regression analysis model and were used to simultaneously assess accuracy and precision. Considering both analysis models, results suggested that urinary N excretion (UN; R²_MDP = 0.76, R²_BLUP = 0.89, RMSPE_m% = 17.2, CCC = 0.82), total manure N excretion (ManN; R²_MDP = 0.83, R²_BLUP = 0.90, RMSPE_m% = 11.0, CCC = 0.84) and N apparently digested (NAD; R²_MDP = 0.97, R²_BLUP = 0.97, RMSPE_m% = 5.3, CCC = 0.95) were closely related to N intake. Milk N secretion was better predicted using milk yield as a single independent variable (MilkN; R²_MDP = 0.77, R²_BLUP = 0.97, RMSPE_m% = 6.0, CCC = 0.74). Additionally, DM intake was a good predictor of UN and ManN and dietary CP concentration of UN and ManN. Consequently, results suggest that several evaluated empirical equations can be used to make accurate and precise predictions concerning N excretion from dairy cows being fed on fresh forage.     

Assessment of the restoration of a degraded semi-humid evergreen broadleaf forest ecosystem by combined single-indicator and comprehensive model method

We conducted a long-term restoration experiment in the degraded ecosystems of a semi-humid evergreen broadleaf forest in Muding County, Yunan Province, China. We used single-indicator assessment and our newly established comprehensive assessment model to compare the effects of four types of management (different historical disturbances + restoration measures) on forest restoration based on a vegetation survey. (1) Species richness in each of the four restoring communities was still lower than that of the zonal forest. There was a compensatory effect of species richness among different layers within communities. Restoration management by natural succession was clearly efficient at restoring species richness and composition, but the effect of disturbance history was minor. Human-assisted restoration had a great effect on biomass accumulation and model tree growth. Plant density was also affected by the different management types, which progressively led to differences in model tree growth and biomass accumulation. (2) The comprehensive assessment model, a simple method based on the restoration mechanism, can precisely quantify the overall restoration of ecosystems, historical disturbance and actual disturbance, using only one set of data. Restoration index (R_d), turning-point restoration index (R₀), restoration-effect index (R_a), turning-point disturbance index (D₀), actual disturbance index (D_r) and overcoming disturbance index (D_a) presented gradual changes in the four restoring communities. The combined single-indicator and comprehensive model method fully assessed the restoration of degraded ecosystems in a semi-humid evergreen broadleaf forest.     

滇西北森林郁闭度估测模型——基于全球生态系统动力学调查多波束激光雷达数据

探究全球生态系统动力学调查(GEDI)多波束激光雷达数据估测区域森林郁闭度(FCC)的潜力,对于评估森林生态系统状态和林分环境具有重要作用。选取滇西北典型生态脆弱区香格里拉为研究区,以GEDI波形数据为信息源,提取46245个有林地光斑参数,使用经验贝叶斯克里金法(EBK)获取光斑参数在研究区未知空间的连续分布,结合54块实测样地数据,采用支持向量机的递归特征消除法(SVM-RFE)、随机森林(RF)和Pearson分析分别优选特征变量,基于贝叶斯优化(BO)随机森林回归模型(BO-RFR)、贝叶斯优化梯度回归模型(BO-GBRT)和偏最小二乘法(PLSR)研建森林郁闭度最佳估测模型。结果表明:(1)EBK法预测精度高,估测结果可靠,R²:0.20-0.92,RMSE:0.004-2812.912,MAE:0.003-1996.258,MRE:0.007-4.423;(2)基于不同特征优选方法筛选的特征变量和数量略有差异,SVM-RFE 法优选出6个参数(cover、pai、sensitivity、rv_a1、rv_a4、rg_a4)的平均交叉验证精度达0.84,RF法以贡献度5%为阈值筛选出5个参数(cover、pai、pgap_theta_error、modis_treecover、modis_nonvegetated),Pearson法以相关性大于0.3且在0.01水平显著优选出5个参数(cover、pai、rv_a5、rg_a5、pgap_theta_error);(3)不同特征变量优选方法筛选的建模参数研建估测模型精度差异性较大,以SVM-RFE和RF方法优选参数构建估测模型的精度更佳,SVM-RFE方法优选参数研建估测模型精度变化相对稳定,以 RF方法中的BO-GBRT模型为最佳FCC估测模型(R²=0.85、RMSE=0.069,P=86.5%);(4)采用BO-GBRT模型估测研究区森林郁闭度和空间制图,与GEDI pai参数预测的FCC具有较高空间相关性达0.53,FCC均值分别为0.58、0.61,主要分布在0.4-0.7,分别占比65.45%、51.79%。研究区森林郁闭度主要处于中度郁闭,北部区域主要为高度郁闭区,与研究区植被覆盖度的空间分布具有一致性,说明使用GEDI数据估测森林郁闭度的方法具有可行性、结果具有可靠性。研究为使用GEDI数据高效、及时、低成本估测大空间尺度的森林水平结构参数的相关研究奠定了基础。     

Quantifying Components of Soil Respiration and Their Response to Abiotic Factors in Two Typical Subtropical Forest Stands,Southwest China

Separating the components of soil respiration and understanding the roles of abiotic factors at a temporal scale among different forest types are critical issues in forest ecosystem carbon cycling. This study quantified the proportions of autotrophic (R_A) and heterotrophic (R_H) in total soil (R_T) respiration using trenching and litter removal. Field studies were conducted in two typical subtropical forest stands (broadleaf and needle leaf mixed forest; bamboo forest) at Jinyun Mountain, near the Three Georges Reservoir in southwest China, during the growing season (Apr.–Sep.) from 2010 to 2012. The effects of air temperature (AT), soil temperature (ST) and soil moisture (SM) at 6cm depth, solar radiation (SR), pH on components of soil respiration were analyzed. Results show that: 1) SR, AT, and ST exhibited a similar temporal trend. The observed abiotic factors showed slight interannual variability for the two forest stands. 2) The contributions of R_H and R_A to R_T for broadleaf and needle leaf mixed forest were 73.25% and 26.75%, respectively, while those for bamboo forest were 89.02% and 10.98%, respectively; soil respiration peaked from June to July. In both stands, CO₂ released from the decomposition of soil organic matter (SOM), the strongest contributor to R_T, accounted for over 63% of R_H. 3) AT and ST were significantly positively correlated with R_T and its components (p<0.05), and were major factors affecting soil respiration. 4) Components of soil respiration were significantly different between two forest stands (p<0.05), indicating that vegetation types played a role in soil respiration and its components.     

三种回归分析方法在Hyperion影像LAI反演中的比较

借助GPS进行地面精确定位,利用LAI-2000冠层分析仅在攸县黄丰桥林场开展130个样地(60m×60m)的叶面积指数(Leaf Area Index,LAI)测量.采用FLAASH模块对Hyperion数据进行大气校正并与地面同步冠层观测数据进行拟合,通过研究地面实测LAI与Hyperion影像波段及其衍生的系列植被指数(NDVI、RVI等)的相关性,筛选出估算叶面积指数的植被指数因子.应用曲线估计、逐步回归及偏最小二乘三种回归分析技术分别建立叶面积指数的最优估算模型.结果表明:参与建模的因子中,比值植被指数(RVI)与LAI的相关性最大,敏感性最高,其次是SARVI0.1,NDVI705,NDVI,SARVI0.1,SARVI0.25;曲线估计、逐步回归分析和偏最小二乘回归三种分析方法所建的6个回归模型中,偏最小二乘回归的拟合效果最好,预测值与实测值的决定系数R2为0.84、曲线估计的拟合效果最低,预测值与实测值的决定系数R2为0.64;建模精度分析表明,选用5-6个自变量因子进行LAI建模是可靠的,以6个植被因子建立的偏最小二乘回归模型预测精度最高.     

Graph theory defining non-local dependency of rainfall in Northeast Brazil

This work shows that graph theory provides a framework to quantify the behavior of the time-correlation function among precipitation records within a given region. The method amounts to consider each station, where one data series was recorded, as a vertex in the graph. An edge, characterized by its geodesic distance d, is inserted between any pair of nodes, for which the Pearson correlation coefficient R, calculated from the corresponding series, is larger than a threshold value R_th. Then, the dependence between N(?), the total number of Pearson-correlated pairs of stations with geodesic distance d ≤ ?, is evaluated as a function of ?. Results are presented for a set of spatially distributed pluviometric stations in Northeast Brazil. The reliability of the proposed procedure is tested in a two-fold way: First, values of N(?) are evaluated for graphs built up by sets of regular and random distributions of nodes within the actual region where the data is collected. Next, an investigation of the influence of the choice for R_th on the results is performed. The results lead to the identification of a power law N(?) ～ ?^α for all time periods and regions that have been investigated, suggesting the presence of a robust non-metric fractal behavior. The value of α is found to depend both on seasonal and intrinsic features of the region rainfall distribution, but rather weakly on the value of R_th. The comparison of the results shows that, in contrast with the values obtained from Hurst exponent analysis, the values of α are related to the uniformity of Pearson correlation within the considered region, not with persistence of the signal.     

基于高光谱数据的互花米草叶片功能性状反演

互花米草成功入侵的关键是其生长繁殖能力以及对环境的适应能力,叶片含水率、相对叶绿素含量、碳氮比、总氮、总磷以及比叶面积等叶片功能性状反应的是互花米草对资源的利用能力以及环境的适应能力。以江苏盐城滨海湿地为研究对象,进行互花米草叶片功能性状与高光谱数据的关系研究。通过对原始光谱数据以及一阶微分转换光谱数据进行主成分分析提取新的主成分变量作为自变量分别建立不同性状的逐步回归、BP神经网络、支持向量机、随机森林4种预测模型,通过比较构建模型的R²以及RMSE选择最优模型,进而基于相关性分析得到的敏感波段构建最优模型,验证其准确性和适用性。研究结果发现：（1）一阶微分数据的建模效果优于原始光谱数据;（2）通过对不同功能性状的预测建模,发现4种模型的预测效果排序为：随机森林>支持向量机>BP神经网络>逐步回归,其中随机森林模型的准确性高、稳定性强,明显优于其他3种模型,而逐步回归模型的效果最差,不适用于互花米草叶片功能性状的高光谱建模;（3）通过对相关性分析得到的敏感波段建立随机森林模型,建模R²均大于0.90,验证R²介于0.73-0.95之间,进一步证实了随机森林模型的准确性和稳定性。研究结果表明,高光谱数据可以作为快速监测互花米草生长状况的有力手段,而随机森林模型可以作为高精度模型实现对互花米草不同叶片功能性状的估测。     

Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system

Canopy height (H_canopy) and aboveground biomass (AGB) of crops are two basic agro-ecological indicators that can provide important indications on the growth, light use efficiency, and carbon stocks in agro-ecosystems. In this study, hundreds of stereo images with very high resolution were collected to estimate H_canopy and AGB of maize using a low-cost unmanned aerial vehicle (UAV) system. Millions of point clouds that are related to the structure from motion (SfM) were produced from the UAV stereo images through a photogrammetric workflow. Metrics that are commonly used in airborne laser scanning (ALS) were calculated from the SfM point clouds and were tested in the estimation of maize parameters for the first time. In addition, the commonly used spectral vegetation indices calculated from the UAV orthorectified image were also tested. Estimation models were established based on the UAV variables and field measurements with cross validation, during which the performance of the UAV variables was quantified. Finally, the following results were achieved: (1) the spatial patterns of maize H_canopy and AGB were predicted by a multiple stepwise linear (SWL) regression model (R² = 0.88, rRMSE = 6.40%) and a random forest regression (RF) model (R² = 0.78, rRMSE = 16.66%), respectively. (2) The UAV-estimated maize parameters were proved to be comparable to the field measurements with a mean error (ME) of 0.11 m for H_canopy, and 0.05 kg/m² for AGB. (3) The SfM point metrics, especially the mean point height (H_mean) greatly contributed to the estimation model of maize H_canopy and AGB, which can be promising indicators in the detection of maize biophysical parameters. To conclude, the variations in spectral and structural attributes for maize canopy should be simultaneously considered when only simple RGB images are available for estimating maize AGB. This study provides some suggestions on how to make full use of the low-cost and high-resolution UAV stereo images in precision agro-ecological applications and management.     

Experimental and finite element analysis of the mouse caudal vertebrae loading model: prediction of cortical and trabecular bone adaptation

In this study, we attempt to predict cortical and trabecular bone adaptation in the mouse caudal vertebrae loading model using knowledge of bone’s local mechanical environment at the onset of loading. In a previous study, we demonstrated appreciable 25.9 and 11% increases in both trabecular and cortical bone volume density, respectively, when subjecting the fifth caudal vertebrae (C5) of C57BL/6 (B6) mice to an acute loading regime (amplitude of 8N, 3000 cycles, 10 Hz, 3 times a week for 4 weeks). We have also established a validated finite element (FE) model of the C5 vertebra using micro-computed tomography (micro-CT), which characterizes, in 3D, the micro-mechanical strains present in both cortical and trabecular compartments due to the applied loads. To investigate the relationship between load-induced bone adaptation and mechanical strains in-vivo and in-silico data sets were compared. Using data from the previous cross-sectional study, we divided cortical and trabecular compartments into 15 subregions and determined, for each region, a bone formation parameter ΔBV/BS (a cross-sectional measure of the bone volume added to cortical and trabecular surfaces following the described loading regime). Linear regression was then used to correlate mean regional values of ΔBV/BS with mean values of mechanical strains derived from the FE models which were similarly regionalized. The mechanical parameters investigated were strain energy density (SED), the orthogonal strains (e _x , e _y , e _z ) and the three shear strains (e _xy , e _yz , e _zx ). For cortical regions, regression analysis showed SED to correlate extremely well with ΔBV/BS (R ² =?0.82) and e _z (R ²?=?0.89). Furthermore, SED was found to predict expansion of the cortical shell correlating significantly with the regional percentage increases in cortical tissue volume (R ² = 0.92), cortical marrow volume (R ² =?0.91) and cortical thickness (R ² = 0.56). For trabecular regions, FE parameters were found not to correlate with load-induced trabecular bone morphology. These results indicate that load-induced cortical morphology can be predicted from population data, whereas the prediction of trabecular morphology requires subject-specific micro- architecture.     

Estimating the Basic Reproductive Number (R0) for African Swine Fever Virus (ASFV) Transmission between Pig Herds in Uganda

African swine fever (ASF) is a highly contagious, lethal and economically devastating haemorrhagic disease of domestic pigs. Insights into the dynamics and scale of virus transmission can be obtained from estimates of the basic reproduction number (R₀). We estimate R₀ for ASF virus in small holder, free-range pig production system in Gulu, Uganda. The estimation was based on data collected from outbreaks that affected 43 villages (out of the 289 villages with an overall pig population of 26,570) between April 2010 and November 2011. A total of 211 outbreaks met the criteria for inclusion in the study. Three methods were used, specifically; (i) GIS- based identification of the nearest infectious neighbour based on the Euclidean distance between outbreaks, (ii) epidemic doubling time, and (iii) a compartmental susceptible-infectious (SI) model. For implementation of the SI model, three approaches were used namely; curve fitting (CF), a linear regression model (LRM) and the SI/N proportion. The R₀ estimates from the nearest infectious neighbour and epidemic doubling time methods were 3.24 and 1.63 respectively. Estimates from the SI-based method were 1.58 for the CF approach, 1.90 for the LRM, and 1.77 for the SI/N proportion. Since all these values were above one, they predict the observed persistence of the virus in the population. We hypothesize that the observed variation in the estimates is a consequence of the data used. Higher resolution and temporally better defined data would likely reduce this variation. This is the first estimate of R₀ for ASFV in a free range smallholder pig keeping system in sub-Saharan Africa and highlights the requirement for more efficient application of available disease control measures.     

The influence of seasonality in estimating mangrove leaf chlorophyll-a content from hyperspectral data

Mangrove photosynthetic activity and, consequently, physiological stress can be monitored indirectly using leaf chlorophyll-a (Chla) measurements. Recent studies have demonstrated the feasibility of mangrove leaf Chla content estimation from in situ hyperspectral vegetation indices (VI) but no such research has been conducted using data collected from contrasting seasons (i.e. dry and rainy). In this study, mangrove leaves were collected in a sub-tropical forest of the Mexican Pacific for Chla content determination and in situ hyperspectral measurements (450–1,000 nm). Specifically, we tested 35 VI to estimate Chla content based on a leaf sample of 360 collected from the same trees during both the dry and rainy seasons. The forest examined contained three species of mangrove (Rhizophora mangle, Avicennia germinans and Laguncularia racemosa) exhibiting various conditions of health (dwarf condition, tall and healthy). A principal component analysis, followed by linear regression analyses, were conducted in order to identify those VI that best predict mangrove leaf Chla content during the two seasons. The results indicate that VI derived from hyperspectral measurements collected during the dry season are better at estimating leaf Chla content than those collected during the rainy season. Among the 35 VI, the Vog1 (R₇₄₀/R₇₂₀) index was found to be the best predictor of mangrove leaf Chla content, resulting in R ² values of 0.80 and 0.68 for the dry and rainy season respectively. These results would suggest that for identifying variation in mangrove forest stress (i.e. health) in sub-tropical regions, hyperspectral measurements should be carried out during the dry season.     

四川柏木人工林林下植被生物量与林分结构的关系

森林结构与林下植被生物量的关系是森林持续经营与森林碳计量监测的科学基础,但一直缺乏必要的研究。以四川柏木(Cupressus funebris)人工林为研究对象,揭示林下植被生物量(Wu)、灌木生物量(Ws)和草本生物量(Wh)与林分结构的关系,并试图构建区域性林下植被生物量估测的混合模型。结果表明:(1)乔、灌、草群体共12个结构因子中,灌木群体的平均基径(Ds)、盖度(Cs)、高度(Hs)、体积(Vs)与林下植被生物量关系更紧密,在林下植被生物量模型构建中更有效;(2)多模型拟合与比较表明,柏木林Ws最佳估算模型为Ws=0.0005V1.0411s(R2a=0.762,P0.001,n=40),而Wu的最佳估算模型为ln Wu=0.0158Hs+0.0111Cs-0.5358(R2a=0.695,P0.001,n=40),但对于Wh未能获得较为理想的估算模型(R2a0.410,P0.01,n=40);(3)林分密度(Du)整合进入多元线性模型提高了林下植被生物量的估测精度,ln Wu=a+b Du+c Hs+d Cs(R2a=0.721,P0.001,n=40)。研究为区域性林下生物量估测模型构建提供了新论据。