二化螟种群空间格局的经典分析与地统计学比较研究

为揭示二化螟幼虫空间格局特征并阐明经典格局分析的局限性与地统计分析的优越性,从2个原始样本出发,另构建了一系列格栅样本、随机样本和顺序样本并进行了比较研究。结果表明,经典格局分析不能有效区分频次分布相同但分布型或聚集程度不同的样本,存在受样方大小、格栅初始点位置和样本容量大小影响等许多局限性,其中受格栅初始点位置影响是首次报道;而地统计学半变异函数能有效刻划二化螟种群空间分布格局,表征其聚集强度和空间异质性,且受样方大小、格栅初始点位置和样本容量大小的影响较小,二化螟种群在低密度下呈随机分布;在高密度下呈聚集分布,聚集强度为0．1056,空间依赖范围为193cm,在高密度下二化螟种群空间分布存在几何异向性,行方向上的聚集强度（0．2716)明显高于列方向(0．0867),但行方向上的空间依赖范围(115cm)小于列方向(264cm)。     

抽样理论在估计田间昆虫数量和为害方面的应用(一)

在进行田间昆虫种群数量的研究工作中,要正确地估计出该种昆虫种群在其种作物田内的总体数,或它对某种作物的为害程度时,比较经济和有科学依据的办法是应用抽样方法。因为抽样理论不仅可以告诉我们对被研究对象的适当抽样数量,而且还可告诉我们抽样估值的误差情况,这样就使研究质量有了保证。但如何能准确地反映出被研究对象的实况,和保证抽样指标与总体中同一指标的标准离差为最小,则除对被研究对象的生物学特性有一定的了解外,必须熟悉抽样的理论,然后根据这些知识才能确定采用适合于它的抽样方法。 抽样的方法,按组织方式的不同,一般可分为下列几种: 1.简单随机抽样法(或称纯随机抽样法); 2.分层抽样法(或称典型抽样法、分区抽样法); 3.集团抽样法(或称成组抽样法); 4.二阶抽样法; 5.双重抽样法。 在上述每种抽样法中,又分为无放回抽样与有放回抽样两种,前者又称不重复抽样,后者称为重复抽样。由于抽样时是用随机抽取,而随机抽取的方法,室内常用的是抽签法或随机数表。田间在昆虫方面所常     

乌兰布和沙漠天然白刺适应性群团抽样技术的研究

以乌兰布和沙漠地区天然白刺为具体研究对象,对不同抽样方法获取的数据进行了分析.研究结果表明,当适应性标准大于等于占位单元平均数时,适应性群团抽样比简单随机抽样更有效,并且可以保证估计精度;初始抽样比例越大,总体均值估计值和抽样相对效率越稳定.在该研究区域采用二阶适应性群团抽样,可以达到估计精度,但与简单随机抽样和适应性群团抽样相比效率偏低.     

长鞘卷叶甲种群空间分布格局的生物地理统计学分析

应用生物地理统计学方法分析了长鞘卷叶甲成虫种群的空间分布格局,在3 m×3 m样方下建立东南→西北方向的半方差函数理论模型,并应用克立格插值方法对其种群密度和空间结构进行估计和模拟。结果表明:球状模型对长鞘卷叶甲成虫种群在该方向上的理论半方差函数有较好拟合效果,其空间分布格局为弱聚集型;成虫在山坡竹林与河岸竹林的变程分别为21.2157和38.8266 m,空间聚集分布的连续性强度分别只有0.3633和0.1758,说明种群在研究区内由空间自相关而产生的结构性变异在总变异中所占比例较小,即变异主要由种群结构的随机性成分引起。     

花生蚜种群分布型及抽样技术的研究

采用两级随机抽样法 (two stagerandomsampling) ,通过 2年的系统调查 ,取得 1 9个样本资料 ,运用 3种聚集度指标、Taylor的幂法则以及M -m的回归关系综合分析 ,花生蚜的混合种群为核心分布型 ,其中 ,有翅蚜呈随机分布、无翅蚜呈核心分布。在此基础上提出了最佳理论抽样数和序贯抽样模型。     

田间昆虫的取样调查技术

田间昆虫取样调查技术直接关系到昆虫种群数量估计以及预测预报的准确性。田间取样调查结果的有效性由调查时抽样方法、抽样数和样本采集方法选取的科学合理性所决定。抽样方法有随机抽样、分层抽样、多重抽样、选择性抽样和顺序抽样。抽样方法的选择需根据昆虫种群的空间分布及作物类型而定。抽样数的多少由要求的调查结果的准确程度及调查种群数量的变异程度所决定。现有的昆虫样本的采集方法较多,主要有直接目测法、振落法、扫网法、吸虫器法和诱集法等。样本采集方法的选择要遵循"调查结果准确、操作简单方便和工作量小"的原则。总之,田间昆虫种群的取样调查,既要保证调查结果的准确性,也要保证调查时间和花费的经济性。     

昆虫种群密度的二项抽样估计模型研究进展

昆虫种群密度的二项抽样估计模型主要有两类,一类是根据空间分布型理论演绎而来,另一类是根据样方中有虫样方比例或不大于某一阈值密度T头的样方比例与平均密度的经验关系拟合的．本文综述了这两类模型、模型的变异分析、模型的理论抽样数估计等方面的研究进展．     

棉田蜘蛛混合种群空间格局分析及应用

1996年5月上旬至下旬,在山西运城地区临猗县,选择高水肥及中水肥棉田进行蜘蛛混合种群空间格局的调查分析。用经典频次法分析,大部分样本符合负二项分布,少数样本有多解和无解现象。9种聚集指标结果均符合聚集分布型,聚类分析结果表明,9种指标可按其性质分成3类。Iwao M~＃-m模型为M~＃=3.5151 1.4765 m,改进的M~＃-M模型为M~＃=2.6423 1.9587 m-0.0246 m~2,Taylor的幂法则式为S~2=22.089M~(2.6466),其结果均为聚集分布。应用以上分析结果,建立棉田蜘蛛混合种群调查的简化抽样方案。用零样出现的百分率来估计百株蜘蛛数量的指数回归式为:P_O=(0.6011/(0.6011 m)~(0.6011);用有蜘蛛的株率来估计百株蛛量,则用以下指数回归式:Y=256.52~(1.5345)。     

外来杂草薇甘菊种群分布格局研究

在3个群落中设置样方,采用方差/均值比率法测定了薇甘菊种群的分布格局类型。结果表明,薇甘菊种群的分布格局主要受自身的生物学特征和微环境的影响,呈随机或集群分布。随机分布的种群其聚集强度指标扩散型指数、丛生指数、聚块性指标和平均拥挤度多对1.0没有显著的偏离,负二项参数则较大;而聚集分布的种群其聚集强度指标则符合聚集特征。     

四种主要大豆食叶害虫种群空间分布型及其应用研究

通过分层随机与连片调查获得114个样本．利用微机分别对豆天蛾卵和豆天蛾．银纹夜蛾．棉铃虫．豆灰蝶及混合种群的幼虫．进行了4种频次分布型检验和6项聚集度指标的测定．结果表明．上述害虫在豆田内均属零集分布．中分析了聚集原因．提出了“Z”字型10样点,每样点以1／3m双行为单位的抽样方法．确立了在两种允许误差下的抽样数量．进行了序贯抽样分析。     

Performance of sampling strategies in the presence of known spatial patterns

In crop protection and ecology accurate and precise estimates of insect populations are required for many purposes. The spatial pattern of the organism sampled, in relation to the sampling scheme adopted, affects the difference between the actual and estimated population density, the bias, and the variability of that estimate, the precision. Field monitoring schemes usually adopt time‐efficient sampling regimes involving contiguous units rather than the most efficient for estimation, the completely random sample. This paper uses spatially‐explicit ecological field data on aphids and beetles to compare common sampling regimes. The random sample was the most accurate method and often the most precise; of the contiguous schemes the line transect was superior to more compact arrangements such as a square block. Bias depended on the relationship between the size and shape of the group of units comprising the sample and the dominant cluster size underlying the spatial pattern. Existing knowledge of spatial pattern to inform the choice of sampling scheme may provide considerable improvements in accuracy. It is recommended to use line transects longer than the grain of the spatial pattern, where grain is defined as the average dimension of clusters over both patches and gaps, and with length at least twice the dominant cluster size.     

Ranked set sampling for replicated sampling designs

In practical ecological sampling studies, a certain design (such as plot sampling or line-intercept sampling) is usually replicated more than once. For each replication, the Horvitz-Thompson estimation of the objective parameter is considered. Finally, an overall estimator is achieved by averaging the single Horvitz-Thompson estimators. Because the design replications are drawn independently and under the same conditions, the overall estimator is simply the sample mean of the Horvitz-Thompson estimators under simple random sampling. This procedure may be wisely improved by using ranked set sampling. Hence, we propose the replicated protocol under ranked set sampling, which gives rise to a more accurate estimation than the replicated protocol under simple random sampling.     

畜禽遗传资源冷冻保存中的取样方法探讨

本文基于遗传学和数理统计原理,提出了运用冷冻生殖细胞的方法长期保存畜禽遗传资源时,冷冻生殖细胞的4种取样方法,并推证了其误差公式和最低保存数量的估计公式。最后通过实例对公式的实际应用作了说明和讨论分析。     

Empirically simulated study to compare and validate sampling methods used in aerial surveys of wildlife populations

This paper compares the distribution, sampling and estimation of abundance for two animal species in an African ecosystem by means of an intensive simulation of the sampling process under a geographical information system (GIS) environment. It focuses on systematic and random sampling designs, commonly used in wildlife surveys, comparing their performance to an adaptive design at three increasing sampling intensities, using the root mean square errors (RMSE). It further assesses the impact of sampling designs and intensities on estimates of population parameters. The simulation is based on data collected during a prior survey, in which geographical locations of all observed animals were recorded. This provides more detailed data than that usually available from transect surveys. The results show precision of estimates to increase with increasing sampling intensity, while no significant differences are observed between estimates obtained under random and systematic designs. An increase in precision is observed for the adaptive design, thereby validating the use of this design for sampling clustered populations. The study illustrates the benefits of combining statistical methods with GIS techniques to increase insight into wildlife population dynamics.     

Improved procedures for estimation of disease prevalence using ranked set sampling

Ranked set sampling (RSS) is a sampling procedure that can be considerably more efficient than simple random sampling (SRS). When the variable of interest is binary, ranking of the sample observations can be implemented using the estimated probabilities of success obtained from a logistic regression model developed for the binary variable. The main objective of this study is to use substantial data sets to investigate the application of RSS to estimation of a proportion for a population that is different from the one that provides the logistic regression. Our results indicate that precision in estimation of a population proportion is improved through the use of logistic regression to carry out the RSS ranking and, hence, the sample size required to achieve a desired precision is reduced. Further, the choice and the distribution of covariates in the logistic regression model are not overly crucial for the performance of a balanced RSS procedure.     

Using helicopter counts to estimate the abundance of Himalayan tahr in New Zealand's Southern Alps

Estimating the abundance and density of mountain ungulates is difficult because of rugged and remote terrain, high elevations, and rapidly changing weather. Helicopter surveys could overcome these problems, but researchers have seldom applied helicopter-based survey methods at large spatial scales in mountain terrain. We used helicopters to count introduced Himalayan tahr (Hemitragus jemlahicus) at 117 plots, each of 4 km², in New Zealand's Southern Alps during 2016–2019. The sampling frame was 7,844 km² and we located the plots at the vertices of an 8-km grid superimposed over the sampling frame (i.e., a systematic random sampling design). We conducted 3 repeat counts at each plot during summer–autumn. We used the repeat counts to estimate tahr abundance and density, corrected for imperfect detection, using a dynamic N-mixture model for open populations. We estimated the population of tahr in the sampling frame using design-based, finite sampling methods and model-based inference procedures. The mean estimated density of tahr on each plot varied from zero to 31.7 tahr/km². The mean densities of tahr varied among management units, ranging from 0.3 to 10.7 tahr/km², and exceeded specified intervention densities in 6 of the 7 management units. The total design-based estimate of tahr abundance in the sampling frame was 34,500 (95% CI = 27,750–42,900), with a coefficient of variation (CV) of 0.11. The corresponding model-based estimate of total abundance was similar (34,550, 95% CI = 30,250–38,700) but was substantially more precise (CV = 0.06) than the design-based estimate. The precision of the estimates for the individual management units was also better than that of the design-based estimates, with CVs of <0.20 for all but 1 management unit. Our study provides a repeatable method for sampling mountain ungulates. More generally, robust estimation of abundance and density of mountain ungulates is possible by combining aerial surveys and open population models with an objective, probabilistic sampling design.     

Bayesian Estimation of Hunting Success Rate and Harvest for Spatially Correlated Post‐stratified Data

We consider the estimation of success rate and harvest under post survey stratification at the sub‐domain (county) level. Often in this situation, the population size for the sub‐domain is unknown and the random mechanism that dictates the sample size for sub‐domains is ignored. Finding good estimators of success rate and harvest is very important for wildlife abundance. A Bayesian hierarchical model is developed to estimate both success rate and harvest simultaneously. The model includes a random sub‐domain sample size correlated with the number of successes in the sub‐domain, fixed week effects, random geographic effects, and spatial correlations between neighboring sub‐domains. The computation is done by Gibbs sampling and adaptive rejection sampling techniques. The method developed is illustrated using data from the Missouri Turkey Hunting Survey. The estimation of success rate is improved by treating the the sub‐domain sample size as a random variable instead of a fixed constant. The Bayesian model yields a reasonable harvest estimation. The spatial pattern of the estimated harvest matches the pattern of the check station data.