再生稻二化螟幼虫空间分布型和抽样技术的研究

再生稻二化螟幼虫的空间布型为负二项分布和聚集分布,其lwao的线性回归方程为m^*=0.0099 2.4753m,属负二项分布。根据聚集均数（λ）测定结果,分析了聚集原因,在分布型研究的基础上,探讨了资料代换模式,Kuno‘s种群序贯抽样模型和最适抽样数模型。     

Efficiency of variable-intensity and sequential sampling for insect control decisions in cole crops in the Netherlands

A total of 24 commercial fields of cabbages and Brussels sprouts were sampled in a grid fashion with 20–25 equally spaced cells with four plants per cell. Using this data base of 80–100 plants, we conducted computer stimulations to compare the treatment decisions that would be made for the major insect pests using published sequential sampling programs and a newly developed variable-intensity sampling program. Additionally, we compared the number of samples required to make the decision. At low thresholds (10–20%) for both Lepidoptera and cabbage aphids, variable intensity-sampling required a smaller sample size and provided more reliable decisions, while at high thresholds (40–50%) sequential sampling provided more reliable decisions. In both procedures, the occurrence of incorrect decisions was minimal. The number of cases in which a decision would not be reached after a 40-plant sample was lower for variable-intensity sampling. Considering the number of samples required to make a correct decision and the greater need for reliable decisions at lower thresholds, variable-intensity sampling was superior to sequential sampling. Additionally, variable-intensity sampling has the advantage of requiring samples to be taken in a greater area of the field and thus increases the probability of detecting localized infestations. Although variable-intensity sampling was not designed to classify pest populations for treatment decisions but rather to achieve sampling precision around the population mean, our present studies indicate that it can also be an effective method to aid in treatment decisions.     

The posterior probability distribution of alignments and its application to parameter estimation of evolutionary trees and to optimization of multiple alignments

How to sample alignments from their posterior probability distribution given two strings is shown. This is extended to sampling alignments of more than two strings. The result is first applied to the estimation of the edges of a given evolutionary tree over several strings. Second, when used in conjunction with simulated annealing, it gives a stochastic search method for an optimal multiple alignment.Correspondence to: L. Allison     

Phylogenetic relationships of the garter snakes based on DNA sequence and allozyme variation

We estimated phylogenetic relationships among 26 species of garter snakes (genus Thamnophis ) using allozyme and mitochondrial cytochrome b gene nucleotide sequence variation. Parsimony analyses of the two data sets give substantially different estimates of phylogeny. Several lines of evidence indicate that much of this conflict is due to error associated with the restricted number of characters in each data set. Such sampling error may be reduced by combining all the characters; we therefore present an estimate of phylogeny based on parsimony analysis of all the data combined. All our analyses support several conclusions in conflict with previous views: a very distant relationship between T.errans and T. elegans , non-monophyly of the elegans group (even excluding T: errans ), and nesting of the form validus (previously considered a member of the genus Nerodia ) within Thamnophis.
The combined analysis gives an almost fully resolved tree. However, bootstrapping indicates only weak support for many clades in this tree. Furthermore, paraphyly of the assemblages of cytochrome b gene lineages within T. elegans and T. radix indicate the potential for discordance between the mitochondrial DNA (mtDNA) and species phylogenies through the sorting of ancestral mtDNA polymorphisms. These problems suggest the need for assaying additional characters, especially ones likely to be independent of those used in the present study.     

斑须蝽三代卵块的空间分布和田间抽样技术研究

通过田间调查和计算,明确了斑须蝽三代卵块呈聚集分布,且以负二项分布为主。理论抽样数当t＝1．00,D＝0．3时,n＝13．091/＋63．878,如果防治指标定为百株虫卵块12块时,则最大抽样数为173株,序贯抽样的累积虫卵块数量界限为：T0（N）＝0.12N±0．4735。田间随机取样以平行线和Z字形为最佳。     

甘薯象空间格局及抽样技术研究

通过空间分布型指数分析,甘薯象对薯块、著株危害空间分布型为随机分布或均匀分布;同时确定了理论抽样数     

The influence of sampling method on the classification of wetland macroinvertebrate communities

Macroinvertebrate communities sampled by a corer, plankton net and sweep net from five wetlands on the Swan Coastal Plain were compared. The composition of the fauna collected in sweeps and tows was generally similar and differed from that collected in the cores. Cores caught fewer species than tows and sweeps at all wetlands and did not capture fast swimming hemipterans or less abundant taxa. The highest species richness was recorded in sweep samples in four out of the five wetlands. Classification (TWIN-SPAN) and ordination (SSH) of the samples collected in sweeps and tows gave good separation of the wetlands, whereas classification of core samples did not. Coring appeared to be the least suitable sampling method for describing the major components of the macroinvertebrate communities of these wetlands. Plankton tows were useful if the time available for sorting was limited as these samples were free of sediments and generally gave similar results to those obtained with sweeps. Sweeps appeared to be the most useful method for a large classification study as they collected more species and resulted in the best discrimination amongst wetlands.     

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。     

Nonparametric and semiparametric estimation with sequentially truncated survival data

In observational cohort studies with complex sampling schemes, truncation arises when the time to event of interest is observed only when it falls below or exceeds another random time, that is, the truncation time. In more complex settings, observation may require a particular ordering of event times; we refer to this as sequential truncation. Estimators of the event time distribution have been developed for simple left-truncated or right-truncated data. However, these estimators may be inconsistent under sequential truncation. We propose nonparametric and semiparametric maximum likelihood estimators for the distribution of the event time of interest in the presence of sequential truncation, under two truncation models. We show the equivalence of an inverse probability weighted estimator and a product limit estimator under one of these models. We study the large sample properties of the proposed estimators and derive their asymptotic variance estimators. We evaluate the proposed methods through simulation studies and apply the methods to an Alzheimer's disease study. We have developed an R package, seqTrun , for implementation of our method.     

Enhancing estimation methods for integrating probability and nonprobability survey samples with machine-learning techniques. An application to a Survey on the impact of the COVID-19 pandemic in Spain

Web surveys have replaced Face-to-Face and computer assisted telephone interviewing (CATI) as the main mode of data collection in most countries. This trend was reinforced as a consequence of COVID-19 pandemic-related restrictions. However, this mode still faces significant limitations in obtaining probability-based samples of the general population. For this reason, most web surveys rely on nonprobability survey designs. Whereas probability-based designs continue to be the gold standard in survey sampling, nonprobability web surveys may still prove useful in some situations. For instance, when small subpopulations are the group under study and probability sampling is unlikely to meet sample size requirements, complementing a small probability sample with a larger nonprobability one may improve the efficiency of the estimates. Nonprobability samples may also be designed as a mean for compensating for known biases in probability-based web survey samples by purposely targeting respondent profiles that tend to be underrepresented in these surveys. This is the case in the Survey on the impact of the COVID-19 pandemic in Spain (ESPACOV) that motivates this paper. In this paper, we propose a methodology for combining probability and nonprobability web-based survey samples with the help of machine-learning techniques. We then assess the efficiency of the resulting estimates by comparing them with other strategies that have been used before. Our simulation study and the application of the proposed estimation method to the second wave of the ESPACOV Survey allow us to conclude that this is the best option for reducing the biases observed in our data.