黑龙江省野生马鹿种群资源现状研究

采用多阶抽样方法 ,研究了黑龙江省野生马鹿资源现状。结果表明 ,1 990年该省林区野生马鹿种群数量为 ( 4 430 0± 3972 )头 ,平均分布密度为 0 2 5头 /km2 ,总栖息面积约为1 789 6万hm2 。与 1 975年全省野生马鹿调查结果相比 ,野生马鹿种群数量略呈增长趋势。1 5年间种群数量增长了 1 5 39%,年增长率为 0 95%,而分布区面积减少了 30 %～ 40 %,出现了明显的分布区退缩现象 ,初步形成了新的分布格局     

大豆蚜种群的空间结构参数特征及其应用

对大豆蚜的空间分布型进行了研究，１１组样本中有１０组符合负二项分布，１组属聚集分布，所有样本不符合泊松，奈曼（ｎ＝０～４和ｎ＝∞），泊松二项，复合泊松分布，大豆蚜空间分布基本成分为个体群的聚集分布，聚集程度随密度的增加而提高。利用大豆蚜种群的空间格局参数ｍ，ＣＡ，Ｉｂ，ｍ／ｘ，α，β和种群聚集均数λ值，分析了大豆蚜种群聚集的原因，估计了个体群平均大小及其与平均密度的关系，通过Ｔａｙｌｏｒ的指数回归     

油松毛虫蛹种群密度简易估计方法的研究

通过对油松毛虫蛹种群密度简易估计方法的研究，证明Ｇｅｒｒａｒｄ＆Ｃｈｉａｎｇ（１９７０）模型、Ｎａｃｈｍａｎ（１９８４）模型以及Ｋｕｎｏ＆Ｓｕｇｉｎｏ（１９５８）模型是等同的，是同一模型的３种不同表现形式。修正后的Ｎａｃｈｍａｎ模型拟合效果最好，并通过模型参数间的相互转换关系，使另外两个模型也同时得到了修正。文中还提出一个更简捷、实用的确定理论抽样数的公式。此外，研究结果还证明，Ｓｙｌｖｅｓｔｅｒ＆Ｃｏｘ（１９６１）模型及其抽样数公式都是不合用的。     

桑蓝叶虫成虫空间分布型及抽样技术研究

通过田间调查 ,对春伐桑园中的桑蓝叶虫的空间分布型、分布图式进行了分析。结果证明 :桑蓝叶虫的空间分布型 80 %符合负二项分布 ,70 %符合Neyman分布 ,不符合Poisson分布 ;空间分布图式为聚集型。序贯抽样方程为 :T1(q) =3q + 4.5 5 41q ,T2 (q) =3q - 4 5 5 41q。     

钢筋接头取样方法检验及试验结果评定

本文对施工中常用的钢筋接头形式、取样方法及试验结果的评定进行了简单阐述；明确了不同接头方式在施工中应注意的事项。     

蜀柏毒蛾种群密度简易调查法的研究

对蜀柏毒蛾低龄幼虫有虫株率(p)与虫口密度(m)进行相关分析,两者存在着极显著的相关关系,种群大暴发时期的相关模型为m_1=9.16346〔—In(1—p)〕~(1.27004)。种群数量相对稳定时期的相关模型和m_2=4.23675[—In(l—p]~(1.03675)]经协方差分析,两者差异极显著。由此分别编制了有虫株率与虫口密度的关系表。实际应用时只需调查有虫株率,即可从这个关系表中查出虫口密度的估计值,而不需实查虫口数。并对误差进行了检验,其准确率80％左右。同时,对目标精度固定时的抽样数以及抽样数固定时的抽样误差进行探讨。另外对以有虫株率来检定林间蜀柏毒蛾的发生程度的序贯抽样进行了分析。     

Pathology in Continuous Infusion Studies in Rodents and Non-Rodents and ITO (Infusion Technology Organisation)-Recommended Protocol for Tissue Sampling and Terminology for Procedure-Related Lesions

Many variables may affect the outcome of continuous infusion studies. The results largely depend on the experience of the laboratory performing these studies, the technical equipment used, the choice of blood vessels and hence the surgical technique as well the quality of pathological evaluation. The latter is of major interest due to the fact that the pathologist is not involved until necropsy in most cases, i.e. not dealing with the complicated surgical or in-life procedures of this study type. The technique of tissue sampling during necropsy and the histology processing procedures may influence the tissues presented for evaluation, hence the pathologist may be a source of misinterpretation. Therefore, ITO proposes a tissue sampling procedure and a standard nomenclature for pathological lesions for all sites and tissues in contact with the port-access and/or catheter system.     

Sampling procedure simulating on‐the‐go sensing for soil nutrients

The emergence of a new sensor technology based on the use of ion‐selective membranes provides an increasing number of opportunities for on‐the‐go field measurements of soil nutrients and soil pH. In the future, on‐the‐go sensing should provide a cost‐effective monitoring of heterogeneous soils with high sampling resolution. It is suitable for site‐specific management because it can be focused on the spatial representativity of observation. This study evaluates the on‐the‐go‐sensing sampling design by comparing it with a standard approach to soil sampling for soil pH and the base nutrients P, K, and Mg under local field conditions in Germany. Soil samples were taken in two test sites at a resolution and in a manner as if they were sampled with an on‐the‐go sensing system and were compared with soil samples taken at a coarser resolution and with standard methods. In general, a higher variability was observed among the on‐the‐go samples due to their smaller sample support. The finer sampling resolution of the on‐the‐go design improved field‐scale semivariogram‐analysis results, identifying the spatial structures for soil pH, P, and Mg clearly. In addition, kriged maps of these soil parameters had predominantly higher estimation accuracies. However, the on‐the‐go samples were strongly influenced by the small‐scale variability of K in one of the test sites. This variability increased the kriging standard deviation for K by 50% compared with standard sampling design. Despite of this problem, the on‐the‐go‐sensing sampling design revealed field‐scale spatial variability for base nutrient status more accurately. Except for K, the mean absolute error of fertilizer‐application maps was reduced when using the on‐the‐go sample design in comparison with the standard sample design (Ca: 210/268 kg ha^–1, P: 2.85/6.75 kg ha^–1, K: 13.7/6.0 kg ha^–1, Mg 5.7/6.8 kg ha^–1). This will reduce over‐ and underfertilization using variable‐rate fertilizer‐application systems. In the future, it will be of interest if real on‐the‐go soil‐sensor measurements exhibit the same variability behavior addressed here or if results will differ substantially.