柠条重要害虫四线奇尺蛾天津亚种幼虫的空间分布研究

四线奇尺蛾天津亚种是近几年新发现的危害柠条的重要害虫,研究四线奇尺蛾种群的空间格局和抽样技术,可为该害虫的危害调查与防治提供理论依据。本文应用6个聚集指标和Taylor幂法则及Iwao的m~*-m回归分析法,对四线奇尺蛾天津亚种幼虫的空间分布型和抽样技术进行了研究,并做了影响因素分析。结果表明:四线奇尺蛾天津亚种幼虫在6个样地均呈聚集分布,分布的基本成分是个体群,通过分布型参数,采用Iwao法计算出了在不同精度下幼虫抽样数公式和序贯抽样模型,该模型可为四线奇尺蛾天津亚种的预测预报及防治提供理论依据。     

稻秆蝇空间分布型及抽样技术研究

稻秆蝇在晚稻田为害呈聚集分布。应用Iwao的X-X回归法、Taylor幂指法、Southwood公共Kc值法求得三个理论抽样数模型,并由Iwao的X-X回归法导出最适抽样单位。当采用Iwao及Willsoo等（1983)提出的序贯抽样法时．得到两个序贯抽样模型。结合Iwao的X-X回归法。Taylor幂指法、Southwood公共Kc值法等三模型,Iwao和Willson的二模型．获得了该虫的复合理论抽样法和复合序贯抽样法。     

草地贪夜蛾幼虫在苗期玉米田的空间分布格局及其抽样技术

草地贪夜蛾Spodoptera frugiperda(Smith)是2019年1月新入侵我国的重要害虫,研究并明确空间格局对确定该虫田间抽样技术、调查虫情具有指导意义。本研究调查获得了玉米苗期喇叭口初期、大喇叭口期草地贪夜蛾幼虫空间分布数据,应用多个聚集指标、Iwao m~*-m模型、Taylor幂法则等方法分析明确了该虫幼虫空间格局,发现苗期整体上幼虫个体群为聚集分布,喇叭口初期为聚集分布而大喇叭口期为均匀分布;苗期整体上及大喇叭口期幼虫聚集度对密度不具依赖性,而喇叭口初期幼虫聚集度对密度具依赖性;同一幼虫密度和误差条件下不同苗期抽样数量显著不同,玉米生育期越早则抽样数量越大。     

杜仲梦妮夜蛾幼虫的空间分布型及抽样技术

杜仲梦妮夜蛾是近年来严重危害杜仲的主要食叶害虫,幼虫专性取食杜仲叶片。本研究运用6种聚集度指标（m*/m、c、k、Iδ、I、Ca）和两种回归模型（Taylor幂法则和m*-m回归模型）研究分析了种植园中杜仲梦妮夜蛾幼虫种群的空间分布型,利用Blackith种群聚集均数λ解析幼虫种群的聚集成因。结果表明杜仲梦妮夜蛾幼虫种群呈均匀分布,而且这种空间分布型是由环境因素引起的。建立了杜仲梦妮夜蛾幼虫抽样数公式N=\[38416(0022/m-0152)\]/D2和序贯抽样模型T(n)=01n±0053〖KF(〗n〖KF)〗。本研究为杜仲梦妮夜蛾虫情调查、害虫治理决策提供参考依据。     

基于改进的Iwao m~*—m模型的抽样方法(Ⅱ)——序贯抽样和零频率抽样法↑

一、引言〔3〕已论及,Iwao的m*-m模型是70年代发燕尾服的有关空间图式和抽样方法的重要研究成果。其应用也在迅速推广,然而,m*-m往往不呈线性。这就使得用线性模型时参数估值有较大误差,对空间图式的解释和抽样应用均有较大影响。基于这种考虑,我们曾提出了一个改进的Iwao m*-m模型m*=α＇+β＇m+γm~2, (1) 其中,α＇为每个基本成分中个体数的分布的平均拥挤度。β＇为在低密度下基本成分分布的相对聚集度。γ为基本成分的分布的相对聚集度随种群密度而变化的速率。     

菱角萤叶甲种群抽样技术研究

在研究菱角萤叶甲各虫态空间分布型的基础上,利用负二项公共Kc值;Iwao回归式中的α,β;Teylor幂法则中的a,b以及Morisita的I_δ求得理论抽样模型,从而导出在不同置信度t,不同的平均虫口密度(?)和不同允许误差D下的理论抽样数．同时利用Kuno(1969),Iwao(1975)和Willson(1983)提出的序贯抽样法,得到菱角萤叶甲各虫态的序贯抽样模型,并对这些模型进行比较．最后,结合Kuno和Iwao模型获得了菱角萤叶甲各虫态的Kuno-Iwao复合序贯抽样法,从而大大减少了在某种情况下的抽样数．     

西瓜枯萎病病株空间分布格局及其抽样技术

研究了西瓜枯萎病病株田间空间分格局及其抽样技术 ,通过频次分布和聚集度指数的测定以及 m* - m回归和 Taylor幂法则分析 ,结果表明 :西瓜枯萎病病株田间分布趋向于聚集分布。m* - m回归分析表明病株空间分布的基本成分是个体群 ,病株个体间相互吸引 ,病株在大田中存在明显的发病中心 ,个体群在田间呈均匀分布格局 ,即分布的基本成分个体群之间趋于均匀分布 ,而个体群内的个体与核心分布相吻合。Taylor幂法则分析表明 ,西瓜枯萎病病株个体的空间格局随着病株密度的提高越趋均匀分布。用 Iwao最适理论抽样模型 n=t2a/D2 [(α 1) /X β- 1],计算出不同病情指数情况下所需的最适抽样数 ,随着病情指数的增加 ,所需抽样数递减。序贯抽样模型为 T0 ( N) =0 .9871N± 1.2 347N ,调查株数 N株时 ,若累计病情指数超过上界可定为防治对象田 ,若累计病情指数未达到下界时 ,可定为不防治田 ,若累计病情指数在上下界之间 ,则应继续调查 ,直到最大样本数 m0 =0 .9871时 ,也即病情指数 2 5 % ,所需抽样数 130株止。     

三叶斑潜蝇幼虫种群空间格局及抽样技术研究

三叶斑潜蝇Liriomyza trifolii是我国重要的园艺及蔬菜害虫,研究三叶斑潜蝇种群的空间格局和抽样技术,可为该虫的危害调查与防治提供理论依据。应用Iwao m*-m回归分析法、Taylor的幂法则及6个聚集指标,对三叶斑潜蝇幼虫在番茄和豇豆上的空间分布型和抽样技术进行了研究,并做了影响因素分析。结果表明:三叶斑潜蝇幼虫在番茄和豇豆上均呈聚集分布,分布的基本成分是以个体群形式存在,通过分布型参数,采用Kc法、Iwao法及Taylor幂法计算出了在不同精度下三叶斑潜蝇田间的理论抽样数。     

桑瘿蚊幼虫的空间分布参数特征及其应用

利用桑瘿蚊的空间分布参数m^#．k．α.β.b值．确定桑瘿蚊幼虫属聚集分布,其原因主要是由于本身的聚集习性引起的,同时根据Iwao模型进行了资料代换。采用Iwao法．Taylor幂法则及Southwood。公共值Kc的理论抽样模式得出在不同密度(x)和不同精度(D)要求下的理论抽样数。应用Kuno模型探讨了序贯抽样方法。     

枣飞象成虫在陕北枣区的空间分布型及抽样技术

【目的】枣飞象Scythropus yasumatsui Kono et Morimoto是枣树Ziziphus jujuba Mill主要害虫之一。近年来,枣飞象在陕北黄河沿岸枣树种植区暴发成灾,给陕北红枣产业造成了严重的经济损失。明确枣飞象成虫在陕北枣区的空间分布型及最佳抽样技术,为枣飞象的监测预报和有效防治提供理论依据。【方法】2015年于陕西省佳县枣区,首先选取具有代表性的枣树样地,采用五点抽样法、双对角线抽样法、棋盘式抽样法、平行线抽样法和Z形抽样法抽取受害枣树,随后采取分层取样法分别统计每株枣树上枣飞象成虫数量。采用t-检验对这5种抽样方法的适合性、代表性和变异程度进行了比较,确定最理想的抽样方法;随后用聚集度指标、Iwao m*-m回归分析法以及Taylor幂法则对枣飞象成虫在枣区的空间分布型进行了研究,选用Iwao提出的理论抽样公式和序贯抽样公式确定了枣飞象成虫的理论抽样数和序贯抽样模型。【结果】结果表明,5种抽样方法都适合枣飞象成虫在陕北枣园的调查,其中以平行线抽样方法最为理想,枣飞象成虫的空间分布型为聚集分布,分布的基本成分是个体间相互吸引的个体群,且个体群的聚集度与其成虫数量成正比,枣飞象个体群聚集原因是由于某些环境成分或者是本身聚集性活动中的一个因素或共同作用引起的。建立的理论抽样数公式为N=t~2/D~2(18.995/m+0.0203),序贯抽样模型为T_0', T_(0(n))'=30n±47.53(1/2)~n。【结论】枣飞象成虫在田间最理想调查方法为平行线抽样法,枣飞象在陕北枣区成虫分布型呈聚集分布,为陕北枣区枣飞象的监测预报及可持续防治提供基础资料。     

L‘—V模型在检验昆虫种群空间分布型中的应用

本文在系统研究种群聚集度批标与分布型判断准则的基础上,提出了一个新的种群聚集度指标：Ｌ’＝Ｖ／ｍ＋Ｖ。指标Ｌ’不仅可用于判断昆虫种群的空间分布型,而且Ｌ’与方差（Ｖ）之间存在关显著性的关系。     

玉米田斜纹夜蛾空间分布型及抽样技术

对秋甜玉米田的斜纹夜蛾不同发生密度田块调查 ,取得了 7组样本资料 ,运用聚集度指标法、Iwao法和Taylor法等对其空间分布型进行测定检验 ,结果表明斜纹夜蛾幼虫呈聚集分布 ,其聚集原因经Blackith种群聚集均数测定 ,当m <3 .2 60 4时 ,其聚集是由于某些环境如气侯、土壤湿度、植株生长状况等所致 ;当m >3 .2 60 4时 ,其聚集是由于害虫本身的群集行为与环境条件综合影响所致。在此基础上 ,通过几种抽样方式比较以五点式为最佳 ,并提出了最佳理论抽样数和最佳序贯抽样模型 :N =1 D2 (3 .8981 m +0 .75 0 3 ) ,To(n) =0 .5n± 2 .865n。     

An exact sampling formula for the Wright-Fisher model and a solution to a conjecture about the finite-island model

An exact sampling formula for a Wright-Fisher population of fixed size N under the infinitely many neutral alleles model is deduced. This extends the Ewens formula for the configuration of a random sample to the case where the sample is drawn from a population of small size, that is, without the usual large-N and small-mutation-rate assumption. The formula is used to prove a conjecture ascertaining the validity of a diffusion approximation for the frequency of a mutant-type allele under weak selection in segregation with a wild-type allele in the limit finite-island model, namely, a population that is subdivided into a finite number of demes of size N and that receives an expected fraction m of migrants from a common migrant pool each generation, as the number of demes goes to infinity. This is done by applying the formula to the migrant ancestors of a single deme and sampling their types at random. The proof of the conjecture confirms an analogy between the island model and a random-mating population, but with a different timescale that has implications for estimation procedures.     

烟蚜及其捕食性天敌草间小黑蛛种群空间结构分析

采用地统计学原理和方法,拟合了不同时期烟蚜种群及其捕食性天敌草间小黑蛛种群的空间结构模型,分析了其空间关系．结果表明,不同时期烟蚜种群的空间结构模型均为球型,空间格局呈聚集型分布,空间相关距离在2．0252～4．1495m之间,异质系数为10281．36～300216．30,空间依赖程度为12176．81～303433．70;不同时期草间小黑蛛种群的空间结构模型也均为球型,空间格局呈聚集型分布,空间相关距离在3．7328～4．8983m之间,异质系数为1．4482～4．4134,空间依赖程度为1．6941～5．8167．该结果和方法可用于监测烟田目标害虫的时空格局动态．     

害虫防治决策的复序贯分析方法及抽样技术研究

复序贯抽样决策技术实际应用的受限 ,原因在于截止限序贯抽样模型的缺乏。本文在检验昆虫种群空间格局回归模型的基础上 ,推导出了目前国内常用检验回归模型的截止限序贯抽样模型 ,并将其运用于复序贯分析决策过程中。实例分析表明 ,对于同一种生物种群 ,在一定的精度 (D)和置信水平(tα)要求下 ,复序贯抽样决策技术可以大幅度地节约抽样成本     

The influence of management practice on the spatial distribution of Lepidopteran pests in <Emphasis Type="Italic">Brassica</Emphasis> crops in the Democratic People’s Republic of Korea: implications for sequential sampling plans

Plutella xylostella and Pieris rapae are the key components of a pest complex that attacks Brassica crops in the Democratic People’s Republic of Korea (DPRK). We examined the spatial distributions of these insects within crops both as individual species and when combined as a standard insect that was derived from their relative feeding rates. The influence of standard co-operative management practice and an integrated pest management (IPM) strategy on the dispersion of the standard insect was tested. Iwao’s m* − m relation was then used to describe the distribution of standard insects by management categories and of Pieris rapae using all data. Pest management practices only affected the distribution of the species when they were combined into standard insects. Enumerative sampling plans were therefore designed for standard insects based on population data derived from IPM-managed fields and for Pieris rapae from population data from all experimental fields. The presented plans have the potential to make a significant contribution to managing lepidopteran pests in the DPRK. The approach will be useful in the design of sequential sampling plans for other geographical regions where these pests co-occur and can also contribute to the development of sequential sampling plans for other pest complexes for which standard insects can be derived.     

Enumerative and binomial sampling plans for citrus mealybug (Homoptera: pseudococcidae) in citrus groves

The spatial distribution of the citrus mealybug, Planococcus citri (Risso) (Homoptera: Pseudococcidae), was studied in citrus groves in northeastern Spain. Constant precision sampling plans were designed for all developmental stages of citrus mealybug under the fruit calyx, for late stages on fruit, and for females on trunks and main branches; more than 66, 286, and 101 data sets, respectively, were collected from nine commercial fields during 1992-1998. Dispersion parameters were determined using Taylor's power law, giving aggregated spatial patterns for citrus mealybug populations in three locations of the tree sampled. A significant relationship between the number of insects per organ and the percentage of occupied organs was established using either Wilson and Room's binomial model or Kono and Sugino's empirical formula. Constant precision (E = 0.25) sampling plans (i.e., enumerative plans) for estimating mean densities were developed using Green's equation and the two binomial models. For making management decisions, enumerative counts may be less labor-intensive than binomial sampling. Therefore, we recommend enumerative sampling plans for the use in an integrated pest management program in citrus. Required sample sizes for the range of population densities near current management thresholds, in the three plant locations calyx, fruit, and trunk were 50, 110-330, and 30, respectively. Binomial sampling, especially the empirical model, required a higher sample size to achieve equivalent levels of precision.     

Development and economic evaluation of spatial sampling plans for corn rootworm Diabrotica spp. (Col., Chrysomelidae) adults

Abstract  To develop spatial sampling plans for corn rootworm ( Diabrotica spp.) adults, their spatial distributions were characterized and economics of sampling plans were evaluated by comparing sampling costs between spatial and conventional (non-spatial) sampling plans. Semivariogram modelling and spatial by with distance indices showed that corn rootworm adults were significantly (P < 0.05) aggregated at peak population densities and any two samples were spatially correlated within approximately 45 m, with 39–90% of the variability explained by spatial dependence. Sampling costs for spatial sampling plans linearly increased as the sampling distance decreased and exponentially increased as the field size increased. Although sampling costs for non-spatial sampling plans were generally lower, spatial sampling plans could be more economical when the mean insect density became lower and the field size became smaller. This study demonstrated that spatial sampling plans could be optimized to minimize the sampling costs and maximize the spatial resolution.     

Spatial detection of outlier loci with Moran eigenvector maps

The spatial signature of microevolutionary processes structuring genetic variation may play an important role in the detection of loci under selection. However, the spatial location of samples has not yet been used to quantify this. Here, we present a new two‐step method of spatial outlier detection at the individual and deme levels using the power spectrum of Moran eigenvector maps (MEM). The MEM power spectrum quantifies how the variation in a variable, such as the frequency of an allele at a SNP locus, is distributed across a range of spatial scales defined by MEM spatial eigenvectors. The first step (Moran spectral outlier detection: MSOD) uses genetic and spatial information to identify outlier loci by their unusual power spectrum. The second step uses Moran spectral randomization (MSR) to test the association between outlier loci and environmental predictors, accounting for spatial autocorrelation. Using simulated data from two published papers, we tested this two‐step method in different scenarios of landscape configuration, selection strength, dispersal capacity and sampling design. Under scenarios that included spatial structure, MSOD alone was sufficient to detect outlier loci at the individual and deme levels without the need for incorporating environmental predictors. Follow‐up with MSR generally reduced (already low) false‐positive rates, though in some cases led to a reduction in power. The results were surprisingly robust to differences in sample size and sampling design. Our method represents a new tool for detecting potential loci under selection with individual‐based and population‐based sampling by leveraging spatial information that has hitherto been neglected.     

Spatial auto-correlation and auto-regressive models estimation from sample survey data

Maximum likelihood estimation of the model parameters for a spatial population based on data collected from a survey sample is usually straightforward when sampling and non-response are both non-informative, since the model can then usually be fitted using the available sample data, and no allowance is necessary for the fact that only a part of the population has been observed. Although for many regression models this naive strategy yields consistent estimates, this is not the case for some models, such as spatial auto-regressive models. In this paper, we show that for a broad class of such models, a maximum marginal likelihood approach that uses both sample and population data leads to more efficient estimates since it uses spatial information from sampled as well as non-sampled units. Extensive simulation experiments based on two well-known data sets are used to assess the impact of the spatial sampling design, the auto-correlation parameter and the sample size on the performance of this approach. When compared to some widely used methods that use only sample data, the results from these experiments show that the maximum marginal likelihood approach is much more precise.