华北农业干旱监测与冬小麦估产研究

评估了基于植被光学厚度(VOD)和日光诱导叶绿素荧光(SIF)等植被广义光学特性构建的标准化植被指数(Z_VI)监测农业干旱的适用性;并采用VOD、SIF两种指数和土壤水分等环境变量的不同组合建立冬小麦估产的岭回归模型,以探究其对冬小麦产量的预报能力。结果表明：相较于Z_VOD,旬尺度Z_SIF对华北地区的农业干旱具有更好的监测能力,对重旱的正确检测率达到77%。Z_SIF能够有效反映干旱发生、发展直至减轻的演变过程,其低值区与站点记录的干旱空间分布相吻合。在华北地区南部,生长季C波段和Ku波段的VOD对冬小麦单产的预报能力优于SIF;利用VOD、SIF两种指数和环境变量的全变量模型取得了最好的估产精度,影响冬小麦产量估算精度的关键预测变量为生长高峰期的SIF。研究可为大范围农业干旱监测和粮食安全提供技术支持。     

Use of environmental predictors for vegetation mapping in semi‐arid mountain rangelands and the determination of conservation hotspots

Question: Can we predict the spatial distribution of plant communities in semi‐arid rangelands based on a limited set of environmental variables? Where are priority areas for conservation located? Location: Al Jabal al Akhdar, Sultanate of Oman. Methods: A Classification Tree Analysis (CTA) was used to model the presence/absence of seven rangeland communities and agricultural areas based on seven selected environmental predictor variables. The latter were either obtained from existing digital datasets or derived from a digital elevation model and satellite images, whereas the grazing intensity was spatially modelled with the kernel density estimation technique. The resulting decision rules of a CTA were applied for predictive mapping within the study area (400 km², resolution of 5 m) by means of ENVI's decision tree classifier. Plant communities of natural rangelands were subsequently evaluated to determine priority areas for nature conservation. Results: Altitude, grazing intensity and landform revealed the highest predictive power. Most of the rangelands were predicted as Sideroxylon–Oleetum. The overall classification accuracy was 89%, whereby agricultural areas and the Ziziphus spina‐christi‐Nerium oleander community at wadi sites had no misclassification. Inaccuracies occurred mainly because of low sample numbers and errors in available maps of predictor variables. The highest rank for nature conservation was observed for the Teucrio‐Juniperetum occupying 20% of the study area. Conclusions: Vegetation mapping using CTA is a valuable tool for rangeland monitoring and identification of key representative areas for nature conservation. An extrapolation of the model used might be feasible to regions adjacent to the central Hajar Mountains.     

On the Estimation of the Distribution Function Using Extreme and Median Ranked Set Sampling

We study relationships between extreme ranked set samples (ERSSs) and median ranked set sample (MRSS) with simple random sample (SRS). For a random variable X, we show that the distribution function estimator when using ERSSs and MRSS are more efficient than when using SRS and ranked set sampling for some values of a given x. It is shown that using ERSSs can reduce the necessary sample size by a factor of 1.33 to 4 when estimating the median of the distribution. Asymptotic results for the estimation of the distribution function is given for the center of the distribution function. Data on the bilirubin level of babies in neonatal intensive care is used to illustrate the method.     

Inverse‐probability‐of‐treatment weighted estimation of causal parameters in the presence of error‐contaminated and time‐dependent confounders

Inverse‐probability‐of‐treatment weighted (IPTW) estimation has been widely used to consistently estimate the causal parameters in marginal structural models, with time‐dependent confounding effects adjusted for. Just like other causal inference methods, the validity of IPTW estimation typically requires the crucial condition that all variables are precisely measured. However, this condition, is often violated in practice due to various reasons. It has been well documented that ignoring measurement error often leads to biased inference results. In this paper, we consider the IPTW estimation of the causal parameters in marginal structural models in the presence of error‐contaminated and time‐dependent confounders. We explore several methods to correct for the effects of measurement error on the estimation of causal parameters. Numerical studies are reported to assess the finite sample performance of the proposed methods.     

Generalized spatial mark–resight models with incomplete identification: An application to red fox density estimates

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A new monitoring PAM fluorometer (<Emphasis Type="Italic">MONI-PAM</Emphasis>) to study the short- and long-term acclimation of photosystem II in field conditions

We present and evaluate the performance of a new field monitoring PAM fluorometer (MONI-PAM) which is intended for short- and long-term monitoring of the acclimation of photosystem II (PSII). The instrument measures chlorophyll fluorescence, photosynthetic photon flux density (PPFD), and temperature in the field, and monitors exactly the same leaf area over prolonged periods of time, facilitating the estimation of both rapidly reversible and sustained non-photochemical quenching (NPQ). The MONI-PAM performance is evaluated in the lab and under natural conditions in a Scots pine canopy during spring recovery of photosynthesis. The instrument provides a new tool to study in detail the acclimation of PSII to the environment under natural field conditions.     

Parameter Estimation for the Compartmental Model

An estimation procedure is obtained for a stochastic compartmental model. Compartmental analysis assumes that a system may be divided into homogeneous components, or compartments. The main theory for the compartmental system was studied by Matis and Hartley (1971) with a discrete population in a steady state. All the transitions among the particles are considered to be stochastic in nature. An estimation procedure, Regular Best Asymptotic Normal (RBAN), discussed by Chiang (1956) is investigated for a stochastic m-compartmental system. The detailed proof of the procedure is provided here. Asymptotic properties for the estimator has been studied and computation has been carried out on our proposed nonlinear model. The downhill simplex search method, originally developed by Nelder and Mead (1965), and applied to minimize our quadratic form is inherently nonlinear in nature, thus avoiding the need to evaluate any derivative for point estimation of the parameters. The procedure applied to an experimental situation involving two compartments gives very encouraging results.     

Occupancy models including local and landscape variables are useful to assess the distribution of a salamander species at risk

Numerous amphibian species are at risk of extinction worldwide. Therefore, reliable estimations of the distribution and abundance of these species are necessary for their conservation. Generally, amphibians are difficult to detect in the wild, which compromises the accuracy of long-term population monitoring and management. Occupancy models are useful tools to assess how environmental variables, at a local and at a landscape scale, affect the distribution and abundance of organisms taking into account species imperfect detectability. In this study, we evaluated with an environmental multiscale approach the seasonal variation of the occupation area of the threatened salamander, Ambystoma ordinarium along its distribution range. We obtained readings in 60 streams of physicochemical variables associated with habitat quality and landscape features. We found that detection and occupation probability of A. ordinarium are seasonally associated with different environmental variables. During the dry season, detectability was positively associated with temperature and stream depth, whereas occupancy was positively associated with the proportion of crops in the landscape and stream elevation. In the rainy season, the detection probability was not explained by any variable considered, and occupancy was negatively associated with stream's electrical conductivity and dissolved oxygen. Based on the estimation of occupied sites, we showed that A. ordinarium presents a more restricted distribution range than previously projected. Therefore, our results reveal the importance of evaluating the accuracy of distribution estimates for the conservation of threatened species as A. ordinarium.     

联合GF-6和Sentinel-2红边波段的森林地上生物量反演

光谱反射率能反映地物差异,是森林地上生物量（Aboveground Biomass,AGB）遥感反演的理论基础。红边波段处于近红外与红光波段交界处快速变化的区域,能对植被冠层结构和叶绿素含量的微小变化做出快速反应,对植被生长状况较敏感。研究以GF-6和Sentinel-2多光谱影像作为数据源,结合野外调查AGB数据,构建落叶松和樟子松AGB线性和非线性估测模型,通过比较模型精度选择最优模型进行森林AGB反演和空间分布制图。结果表明：GF-6和Sentinel-2影像红边波段反射率与落叶松、樟子松AGB均呈显著相关（P<0.05）,红边波段对AGB估测较敏感。多变量估测模型整体估测效果优于单变量模型,所有模型中多元线性回归模型取得了最优的决定系数（落叶松R²=0.66,樟子松R²=0.65）和最低的均方根误差（落叶松RMSE=31.45 t/hm²,樟子松RMSE=54.77 t/hm²）。相比单个数据源,联合GF-6和Sentinel-2影像构建的多元线性回归模型估测效果得到了显著提升,模型RMSE对于落叶松和樟子松AGB估测分别最大降低了22.9%和11.2%。增加红边波段进行AGB估测能显著提高模型估测精度,三组数据源分别加入红边波段信息后进行建模,模型RMSE得到了显著降低。GF-6拥有800 km观测幅宽和高效的重访周期,可以快速地提供大尺度时间序列数据,在森林地上生物量反演和动态监测方面有着很大潜力。     

Determination of fatty acid composition in seed oil of rapeseed (<Emphasis Type="Italic">Brassica napus</Emphasis> L.) by mutated alleles of the FAD3 desaturase genes

One of the goals in oilseed rape programs is to develop genotypes producing oil with low linolenic acid content (C18:3, ≤3%). Low linolenic mutant lines of canola rapeseed were obtained via chemical mutagenesis at the Plant Breeding and Acclimatization Institute – NRI, in Poznan, Poland, and allele-specific SNP markers were designed for monitoring of two statistically important single nucleotide polymorphisms detected by SNaPshot analysis in two FAD3 desaturase genes, BnaA.FAD3 and BnaC.FAD3, respectively. Strong negative correlation between the presence of mutant alleles of the genes and linolenic acid content was revealed by analysis of variance. In this paper we present detailed characteristics of the markers by estimation of the additive and dominance effects of the FAD3 genes with respect to particular fatty acid content in seed oil, as well as by calculation of the phenotypic variation of seed oil fatty acid composition accounted by particular allele-specific marker. The obtained percentage of variation in fatty acid composition was considerable only for linolenic acid content and equaled 35.6% for BnaA.FAD3 and 39.3% for BnaC.FAD3, whereas the total percentage of variation in linolenic acid content was 53.2% when accounted for mutations in both genes simultaneously. Our results revealed high specificity of the markers for effective monitoring of the wild-type and mutated alleles of the Brassica napus FAD3 desaturase genes in the low linolenic mutant recombinants in breeding programs.     

Whole‐landscape modelling of compositional turnover in aquatic invertebrates informs conservation gap analysis: An example from south‐western Australia

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Allometric model for nondestructive leaf area estimation in clary sage (<Emphasis Type="Italic">Salvia sclarea</Emphasis> L.)

Leaf area estimation is an important biometrical observation recorded for evaluating plant growth in field and pot experiments. In this study, conducted in 2009, a leaf area estimation model was developed for aromatic crop clary sage (Salvia sclarea L.), using linear measurements of leaf length (L) and maximum width (W). Leaves from four genotypes of clary sage, collected at different stages, were used to develop the model. The actual leaf area (LA) and leaf dimensions were measured with a Laser Area meter. Different combinations of prediction equations were obtained from L, W, product of LW and dry mass of leaves (DM) to create linear (y = a + bx), quadratic (y = a + bx + cx²), exponential (y = ae^bx), logarithmic (y = a + bLnx), and power models (y = ax^b) for each genotype. Data for all four genotypes were pooled and compared with earlier models by graphical procedures and statistical measures viz. Mean Square Error (MSE) and Prediction Sum of Squares (PRESS). A linear model having LW as the independent variables (y = −3.4444 + 0.729 LW) provided the most accurate estimate (R ² = 0.99, MSE = 50.05, PRESS = 12.51) of clary sage leaf area. Validation of the regression model using the data from another experiment showed that the correlation between measured and predicted values was very high (R ² = 0.98) with low MSE (107.74) and PRESS (26.96).     

Combined monitor for direct and indirect measurement of biofouling

Biofouling is one of the most important problems associated with heat exchangers, leading to a loss of thermal performance in their cycle. To maintain them in optimum working condition, biofouling must be kept under control and, to do so, instrumentation is required for its monitoring. The development of the biofouling layer can be qualitatively followed, but only during maintenance shutdown periods is it possible to attain a quantitative assessment. The CMDIMB [Combined Monitor for Direct and Indirect Measurement of Biofouling] was conceived as a means of discovering the evolution of the frictional resistance (f) and the heat transfer resistance (R _f) of a fluid because these are variables that indirectly define the biofouling deposited in the tubes of a seawater-cooled heat exchanger. They likewise serve to directly indicate its mass and thickness according to the total solid matter adhered over time. The results obtained allowed the values of the variables taken by the CMDIMB to be extrapolated to the heat exchanger that was set up in parallel. The CMDIMB is proposed as a highly useful tool for directly and indirectly monitoring biofouling growth in heat exchangers that do not possess the necessary instrumentation to monitor this phenomenon.     

Machine learning prediction of above-ground biomass in pure Calabrian pine (Pinus brutia Ten.) stands of the Mediterranean region,Türkiye

Above-ground biomass (AGB) is an important component for identifying carbon stocks, monitoring the impacts of climate change, and evaluating merchantable timber. Accurate prediction of forest AGB is central to the correct interpretation of these components and to produce usable data for planners and researchers. In this study, remotely sensed time-series data derived from Landsat 8 (reflectance (R) and vegetation indices (VI)), topographic (T) and climate (C) data were used as independent variables to predict AGB of pure Calabrian pine (Pinus brutia Ten.) stands using multiple regression analysis (MLR) and support vector machines (SVM) methods. The AGB modeling was done by using independent variables individually and by combining variables, and the AGB maps of the most successful models obtained from MLR and SVM methods were produced. It was determined that the most successful variable group was the VI when the independent variables were used one by one (MLR Training R² = 0.50, SVM Training R² = 0.67). The most successful predictions in AGB modeling were obtained with combining all independent variables and using the SVM method (Training R² = 0.85, Validation R² = 0.69). In the combination of independent variables, VI and C data made the greatest contribution to the success of the AGB prediction. The ‘green leaf index’ vegetation indices had the most significant effect on the modeling AGB. In this study, T and C in addition to spectral data has increased the AGB estimation performance. It has been found that the SVM method yielded higher model accuracy than MLR method in predicting AGB. Overall, the spectral data and the SVM method can contribute to improving the accuracy of AGB estimates and provide an effective approach towards the capability for forest ecosystem monitoring.     

Interobserver error in grassland vegetation surveys: sources and implications

人类观测误差是植被测量中不可避免的一个问题。我们量化了与高草草原植被长期监测相关的观测者间误差的四个组成部分：忽略误 差、误识别误差、谨慎误差和估计误差。由于观察者会产生误差，我们还评估了地块大小与伪周转率的关系，以及对比了物种组成和丰度的伪变化与四年间植被变化之间的关系。这项研究是在美国堪萨斯州的高草草原国家保护区进行的。监测点包括10个地块，每个地块由一系列的四个嵌套框架(0.01, 0.1, 1和10 m²)组成。在每个嵌套框架中记录了所有的草本物种，并且在10 m²的空间尺度下，视觉估计了7个覆盖类别内的叶面覆盖。总共调查了300个地块(30个地点)，并随机选择28个地块重新进行测量以评估观测者的误差。所有的调查由四名观测者分两组完成。研究结果表明，在10 m²空间尺度上，由忽略误差引起的伪周转率平均为18.6%，而由误识别误差和谨慎误差引起的伪周转率平均值分别为1.4%和0.6%。尽管由重新定位引起的误差可能也起一定的作用，由忽略误差导致的伪周转率随样地面积的减小而增 加。物种组成在四年期间的变化(排除潜在的误识别误差和谨慎误差)为30.7%，其中包括由忽略误差和实际变化引起的伪周转率。18.6%的忽略误差表明四年期间的实际变化只有12.1%。对于估计误差，26.2%会记录为不同的覆盖等级。在四年的时间内，46.9%的记录显示了不同的覆盖等级，这表明两个时间段间覆盖率变化的56%是由于观测者误差造成的。     

Movements of bonnetheads, <Emphasis Type="Italic">Sphyrna tiburo</Emphasis>, as a response to salinity change in a Florida estuary

The movement of bonnetheads, Sphyrna tiburo, within an estuarine system on the Gulf of Mexico coast of Florida was examined to define response to salinity change. Shark presence and movements were evaluated by acoustic monitoring and gillnet sampling. Acoustic monitoring data were used to investigate active selection of different zones within the estuary based on differences in salinity among zones. Sharks were monitored for 187 days in 2003 and 217 days in 2004 in salinities ranging from 11.0 to 31.0 ppt in 2003 and 15.8 to 34.6 ppt in 2004. Monitoring data supported the hypothesis that salinity played a role in the distribution and movement of S. tiburo. Catch per unit effort (CPUE) data obtained from gillnet sampling from 1995 to 2004 were examined to determine affinity or avoidance of specific salinities within the study site as calculated using an electivity index. Electivity analysis showed almost no affinity or avoidance for specific salinity values. The difference in results between the CPUE and acoustic monitoring in relation to the potential effects of salinity likely relate to the nature of the data, with acoustic monitoring providing continuous data and CPUE providing snapshot location data. The results of this study suggest that although S. tiburo are collected within a wide range of salinity levels, salinity may affect movement and distribution. Salinity effects may be more pronounced during periods of prolonged and/or large changes in salinity as detected by long-term monitoring.     

The potential of random forest and neural networks for biomass and recombinant protein modeling in Escherichia coli fed‐batch fermentations

Product quality assurance strategies in production of biopharmaceuticals currently undergo a transformation from empirical “quality by testing” to rational, knowledge‐based “quality by design” approaches. The major challenges in this context are the fragmentary understanding of bioprocesses and the severely limited real‐time access to process variables related to product quality and quantity. Data driven modeling of process variables in combination with model predictive process control concepts represent a potential solution to these problems. The selection of statistical techniques best qualified for bioprocess data analysis and modeling is a key criterion. In this work a series of recombinant Escherichia coli fed‐batch production processes with varying cultivation conditions employing a comprehensive on‐ and offline process monitoring platform was conducted. The applicability of two machine learning methods, random forest and neural networks, for the prediction of cell dry mass and recombinant protein based on online available process parameters and two‐dimensional multi‐wavelength fluorescence spectroscopy is investigated. Models solely based on routinely measured process variables give a satisfying prediction accuracy of about ± 4% for the cell dry mass, while additional spectroscopic information allows for an estimation of the protein concentration within ± 12%. The results clearly argue for a combined approach: neural networks as modeling technique and random forest as variable selection tool.     

A fine‐grained spatial prediction model for the red‐listed vascular plant Scorzonera humilis

We explored the applied use of distribution modelling as a tool for making spatial predictions of occurrences of the red‐listed vascular plant species Scorzonera humilis in a study area in southeast Norway. Scorzonera is typical of extensively managed semi‐natural grasslands. A Maxent model was trained on all known records of the species, accurately georeferenced and gridded to fine resolution (grid cells of 25×25 m). Model performance was assessed on the training data by data‐splitting (by which some records were set off for evaluation) and on independent evaluation data collected in the field. Of the eight predictor variables used in the modelling, distance to roads and to arable land were most important followed by land‐cover class and altitude. Judged from the area under curve (AUC), the model was good to excellent and a significant, positive relationship was found between relative probabilities of occurrence predicted by the model and true probability of presence provided by the independently collected evaluation data. The model was used together with the evaluation data to estimate presence of Scorzonera humilis in 0.7% of the grid cells in the study area. The grid cells in which the model predicted highest probability for Scorzonera to be present had a true probability of presence of ca 12%, i.e. 17×higher than in an average cell. The present study demonstrates that, even when only simple predictor variables are available, spatial prediction modelling contributes important knowledge about rare species such as prevalence estimates, spatial prediction maps and insights into the species’ autecology. Spatial prediction modelling also makes cost‐efficient monitoring of rare species possible. However, it is pointed out that these benefits require evaluation of the model on independently sampled evaluation data.     

Monitoring of piglets' open field activity and choice behaviour during the replay of maternal vocalization: a comparison between Observer and PID technique

The paper presents a new system for the automatic monitoring of open field activity and choice behaviour of medium-sized animals. Passive infrared motion detectors (PID) were linked on-line via a digital I/O interface to a personal computer provided with self-developed analysis software based on LabVIEW (PID technique). The set up was used for testing 18 one-week-old piglets (Sus scrofa) for their approach to their mother's nursing vocalization replayed through loudspeakers. The results were validated by comparison with a conventional Observer technique, a computer-aided direct observation. In most of the cases, no differences were seen between the Observer and PID technique regarding the percentage of stay in previously defined open field segments, the locomotor open field activity, and the choice behaviour. The results revealed that piglets are clearly attracted by their mother's nursing vocalization. The monitoring system presented in this study is thus suitable for detailed behavioural investigations of individual acoustic recognition. In general, the PID technique is a useful tool for research into the behaviour of individual animals in a restricted open field which does not rely on subjective analysis by a human observer.     

Chemometric modeling and two-dimensional fluorescence analysis of bioprocess with a new strain of <Emphasis Type="Italic">Klebsiella pneumoniae</Emphasis> to convert residual glycerol into 1,3-propanediol

The goal of this study was to show that the metabolism of Klebsiella pneumoniae under different aeration strategies could be monitored and predicted by the application of chemometric models and fluorescence spectroscopy. Multi-wavelength fluorescence was applied to the on-line monitoring of process parameters for K. pneumoniae cultivations. Differences observed in spectra collected under aerobiosis and anaerobiosis can be explained by the different metabolic states of the cells. To predict process variables such as biomass, glycerol, and 1,3-propanediol (1,3-PD), chemometric models were developed on the basis of the acquired fluorescence spectra, which were measured continuously. Although glycerol and 1,3-PD are not fluorescent compounds, the results showed that this technique could be successfully applied to the on-line monitoring of variables in order to understand the process and thus improve 1,3-PD production. The root mean square errors of predictions were 0.78 units, 10 g/L, and 2.6 g/L for optical density, glycerol, and 1,3-PD, respectively.