芦芽山自然保护区金雕的繁殖习性

１９９５年—１９９７年每年的３月—９月在芦芽山自然保护区对金雕的繁殖习性进行了观察。其巢营建于悬崖峭壁的平台或凹处,呈扁盘状,巢高６０．１９（４０～９８）ｃｍ,巢深１６．２５（１３～２５．５）ｃｍ,内径５２．００ｃｍ×３１．２０ｃｍ,窝卵数多为１枚～３枚,卵的大小为７６（７１～７９）ｍｍ×６０（５７～６３）ｍｍ,卵重１４９（１４０～１５２）ｇ,孵卵期３５ｄ,孵化率为８３．３３％,雏鸟在巢内有相残现象。     

The effects of persecution on age of breeding and territory occupation in golden eagles in Scotland

Several studies have documented negative effects of persecution by humans on the population dynamics of large birds of prey. Several areas have seen a reduction in persecution in modern times, but the demography of large raptors still makes these birds vulnerable to such effects. In a GIS analysis, utilising data collected during two national censuses of the golden eagle Aquila chrysaetos in Scotland and contemporary data on the distribution of poisoning incidents (a common method of raptor persecution in Scotland), we show that persecution was associated with a reduction in the age of first breeding, territory vacancies, and the use of territories by non-breeding immatures. Persecution was therefore reducing adult survival and distribution and was probably creating ecological traps that attract mobile immatures, increasing subadult mortality in birds that may originate from persecution-free areas. Mapping a form of land management unique to moors managed for red grouse Lagopus lagopus scoticus shooting, where most poisoning occurs, we also show similar associations between golden eagle demography and grouse moor management. As such, persecution may be having a major impact on the golden eagle population of Scotland, since it occurs over a wide area and at least in some areas has continued unchecked over many decades.     

基于粒子群优化算法的农田多目标控制排水模型

该文研究内容包括2个方面： 1）针对南方丘陵地区地表排水方式,通过田间排水试验,探求控制排水中氮、磷浓度的变化规律,拟合了累计入渗量Philip公式的参数,建立了排涝、除渍和减少氮、磷流失的多目标控制排水模型。2）在分析多目标优化问题Pareto最优解的基础上,运用MATLAB编制计算程序,针对控制排水模型的具体特点,分别定义了粒子的适应值、个体极值和全局极值的计算公式,利用粒子群优化算法对模型进行了求解。结果表明,运用基于粒子群的多目标优化算法求解农田多目标控制排水模型,所求得的控制排水时间与实际控制排水时间基本一致,为农田排水的科学管理提供参考。     

动态调整粒子群-霍尔特模型在径流预测中的应用

为了提高水库和河流中长期径流预测精度,针对粒子群算法存在的缺陷,提出了动态调整粒子群算法（DAPSO）。借助霍尔特-温特斯线性季节性模型的预测功能,应用DAPSO算法求解和优化霍尔特-温特斯线性季节性模型组合参数,形成动态调整粒子群-霍尔特-温特斯线性季节性模型组合算法,对石泉水库进行中长期径流预测。仿真计算表明,动态调整粒子群-霍尔特-温特斯线性季节性模型算法收敛速度快于霍尔特-温特斯线性季节性模型算法、粒子群-霍尔特-温特斯线性季节性模型算法。该组合算法克服了按梯度试算法搜索质量差和精度不高的缺点,输出稳定性好,预报精度显著提高,置信度为95%时的预测相对误差小于6%。该算法可应用于水库和河川中长期径流预测。     

Modified evisceration technique in a golden eagle (Aquila chrysaetos)

Two different modified techniques have been described for enucleation in raptors, including the transaural approach and the globe-collapsing procedure. This case report describes an alternative, modified evisceration procedure in a mature female Golden Eagle (Aquila chrysaetos). The advantages of this procedure are decreased anesthetic time, ease of procedure, decreased risk of excessive traction of the optic nerve, decreased intraoperative orbital trauma, and preservation of the natural symmetry of the head. The major disadvantage of this procedure is that it does not allow complete histologic examination of the globe. Patients with intraocular infection or neoplasia, or significant orbital disease may be poor candidates for this technique.     

水库优化调度的粒子群算法

提出了在用粒子群算法求解水库优化调度问题时,将模拟退火因子引入罚函数,可以克服不变罚因子的缺陷.采用动态规划法和PSO算法优化调度程序,结果发现PSO算法计算结果更优于动态规划结果.实例计算表明,粒子群算法原理简单,易于编程,占用内存少,求解精度高,适合求解具有复杂约束的水库优化调度问题.     

Modelling populations of long-lived birds of prey for conservation: A study of imperial eagles (Aquila heliaca) in Kazakhstan

Eagle populations worldwide are in decline and their demography is generally poorly understood. We use novel sensitivity analysis of stochastic simulation models to analyse the demography of the worlds highest-density and longest-studied population of eastern imperial eagles (Aquila heliaca), at the Naurzum National Nature Reserve in Kazakhstan. Single variable perturbation (a simple elasticity-type analysis) showed that population growth was most sensitive to changes in adult survival but provided no information on how interactions between parameters may influence population growth. Multiple-variable perturbations (a more comprehensive elasticity-type analysis) suggested that population growth is relatively more sensitive to adult survival than is indicated by single-variable perturbation but also that when adult survival is within a biologically reasonable range, other parameters are still highly consequential to model outputs. For Naurzum’s imperial eagles, and for other structured populations of vertebrates, effective conservation and management likely requires an approach that addresses the importance of simultaneous variation in multiple vital rates including both survivorship and reproductive output.     

用多阈值多目标无人机图像分割优化算法检测秸秆覆盖率

为了适应航拍采集秸秆覆盖图像大尺度处理需求,提高当前多阈值差分灰狼优化算法（Differential Evolution Grey Wolf Optimizer,DE-GWO）的图像分割质量和速度,提出一种用于检测秸秆覆盖率的图像分割优化算法。该研究借鉴了人工蜂群多目标灰狼优化算法（Artificial Bee Colony Survey Multi-Objective Grey Wolf Optimizer,AS-MOGWO）,在DE-GWO算法中加入了多目标灰狼优化算法（Multi-Objective Grey Wolf Optimizer,MOGWO）的外部存档,引入多目标的概念,并添加了人工蜂群算法（Artificial Bee Colony,ABC）中观察蜂的搜索策略,提出了基于多阈值的多目标秸秆覆盖图像自动分割的优化算法（Differential Evolution Artificial Bee Colony Survey Multi-Objective Grey Wolf Optimization,DE-AS-MOGWO）。该算法不仅继承了DE-GWO算法的自动分割特性,还兼备AS-MOGWO算法的高效收敛性,提高了图像分割的准确性和处理速度。分析结果显示,在无外界影响的情况下,该研究提出的DE-AS-MOGWO优化算法与人工实际测量法匹配的误差可控制在8%以内。在算法性能方面,DE-AS-MOGWO相比于PSO（Particle Swarm Optimization）、GWO（Grey Wolf Optimizer）、DE-GWO和DE-MOGWO在平均匹配率上分别提高了4.967、3.617、2.188和3.404个百分点,平均误分率分别降低了0.168、0.131、0.089和0.116个百分点,而算法耗时分别降低了82%、84%、17%和32%。试验结果表明,多阈值多目标图像分割方法在大尺度无人机图像中可获得较好的分割效果,且针对不同秸秆覆盖率图像均具有普遍适用性,为大面积秸秆覆盖率检测以及其他相关图像检测提供了高效算法支持。     

Regional conservation priorities for a large predator: golden eagles (Aquila chrysaetos) in the Alpine range

We compared the density of golden eagles (Aquila chrysaetos) and their nest dispersion, productivity and diet in Alpine and pre-Alpine areas. The comparison was made at two spatial scales: (1) at the territory level within two contiguous populations, and (2) at the population level, using published data for 22 populations scattered across four countries (France, Switzerland, Germany and Italy). Availability of golden eagle's main prey species was higher in Alpine than in pre-Alpine regions. The potential foraging ranges of pairs of eagles in the pre-Alps had larger amounts of woodland and lower amounts of suitable foraging habitat than those of Alpine pairs. At both spatial scales, density and productivity were lower in the pre-Alps. Also, pre-Alpine populations were characterised by a lower percentage of main prey species in the diet, which was significantly correlated with productivity, and by a higher breadth of diet, which was negatively correlated with density. Overall, higher prey availability and habitat suitability in the Alps resulted in higher density and breeding success, in a manner compatible with theoretical metapopulation models. We suggest that priority be given to conservation of eagle populations in high quality Alpine habitat, and that conservation action be carried out by a mixture of site-protection measures and conservation of the wider environment.     

基于GWO优化SVM的小麦籽粒优劣分级研究

小麦籽粒优劣不仅是产量及品质的重要决定因素,也是育种适应性的综合指标。为了提高小麦籽粒优劣分级的准确率,同时克服神经网络中存在的收敛速度慢、容易陷入局部极值等缺陷,提出一种灰狼算法（GWO）优化支持向量机（SVM）的小麦籽粒优劣分级方法,以航麦8805为研究对象,利用图像处理技术对小麦籽粒图像进行预处理并提取小麦籽粒的形态、颜色和纹理等21个特征。然后采用灰狼算法对支持向量机的两个参数（c、σ）进行优化,建立GWO-SVM模型,从而对小麦籽粒进行优劣分级。与其他算法相比,GWO优化SVM的算法对小麦籽粒的分级准确率有明显的提高,对小麦籽粒优劣分级的准确率可达到95.08%。