黄土高原半干旱区油松人工林林冠截留模型及其参数特征研究

利用2008—2011年定位观测数据,采用回归分析方法对黄土高原半干旱区油松人工林林冠截留与林外降雨之间的关系进行建模研究,并运用Bootstrap方法,对模型参数特征进行分析。结果表明:林冠截留量(I)与林外降雨量(P)可用非线性模型I=a Pb进行回归模拟,回归方程为I=0.584P0.576;该模型不仅具有较高的拟合精度,且拟合方程参数稳定性良好,经检验,方程均达到显著性要求,可在该区域油松人工林应用。     

A method for calculating numerical estimates of gear selectivity curve

The present paper proposes a method for estimating the numerical curve of gear selectivity without the assumption of function of selectivity curve and estimating confidence intervals, using data of catch per unit effort at length for several nets of different mesh sizes obtained from fishing experiments. The paper assumes such geometric similarity as the selectivity is the same if the rate of fish length to mesh size is the same. The basic idea is due to a technique of numerical integration that uses the derivatives at various points. The natural logarithm of selectivity is expressed by an approximation equation using its cumulated derivatives. The values of derivatives at various lengths are calculated from the data. The confidence intervals are made from the bootstrap samples. The method was applied to the data of catch at length by mesh size of gill net for pink salmon Oncorhynchus gorbuscha . Data collection, modification and issues of the method are discussed.     

STUDY ON SELF -SCANNING CIRCUIT OF LOW POWER DISSIPATION MOS IMAGE SENSOR

The paper studies a self- scanned circuit used in MOS image sensor with the demand of developing of high -order.low power disspation MOS image sensor and puts forward a kind of self -scanned circuit .which uses a three tramister dynamic nocomparing circuit whith a changing capacitor bootstrap circuit as MOS image sensor. The circuit adopted a silicon gote P -MOS technique, which is a high -speed low-dissipation dynamic no-coparing circuit. It is a practical unit circuit in the high -order array and solves the problem that the power dissipation of array rises with the increase of the array bite.     

基于科学调查与渔业生产数据的山东近海口虾蛄生长参数估算

渔业资源评估一般有两种数据来源,即科学调查数据和渔业生产数据;前者需要定期出海采样,耗时长且费用高,后者易于获取但样本代表性存在问题。本研究以山东近海口虾蛄为例,基于电子体长频率方法(ELEFAN)评估了口虾蛄的生长参数,采用bootstrap 重抽样方法比较了基于渔业生产数据与科学调查数据分析结果的差异,旨在探讨渔业生产数据在估算生长参数上的准确性。结果表明,科学调查数据估算得到的口虾蛄von Bertalanffy季节性生长方程中的极限体长L∞=193.16 mm, K=0.62,生产数据估算得到的口虾蛄极限体长L∞=171.70 mm,K=0.67;非参数检验表明基于两种采样方法所求得的口虾蛄的极限体长L∞呈现显著性差异, K 和“夏季点”ts 均呈现不显著性差异。本研究表明,渔业生产数据在一定程度上能够反映生物的生长状况,对K 和ts 的估算与科学调查数据估算的结果较为接近,但对极限体长的估算误差较大。因此口虾蛄生长研究需要依靠科学调查数据的支持,同时渔业生产数据可以作为辅助信息。     

Quantifying uncertainty in the wild‐caught fisheries goal of the Ocean Health Index

Sustainability indices are proliferating, both to help synthesize scientific understanding and inform policy. However, it remains poorly understood how such indices are affected by underlying assumptions of the data and modelling approaches used to compute indicator values. Here, we focus on one such indicator, the fisheries goal within the Ocean Health Index (OHI), which evaluates the sustainable provision of food from wild fisheries. We quantify uncertainty in the fisheries goal status arising from the (a) approach for estimating missing data (i.e., fish stocks with no status) and (b) reliance on a data‐limited method (catch‐MSY) to estimate stock status (i.e., B/B_MSY). We also compare several other models to estimate B/B_MSY, including an ensemble approach, to determine whether alternative models might reduce uncertainty and bias. We find that the current OHI fisheries goal model results in overly optimistic fisheries goal statuses. Uncertainty and bias can be reduced by (a) using a mean (vs. median) gap‐filling approach to estimate missing stock scores and (b) estimating fisheries status using the central tendency from a simulated distribution of status scores generated by a bootstrap approach that incorporates error in B/B_MSY. This multitiered approach to measure and describe uncertainty improves the transparency and interpretation of the indicator and allows us to better understand uncertainty around our OHI fisheries model and outputs for country‐level interpretation and use.     

Roles of bootstrap resampling,measure of central tendency,and method for calculating thermal units when developing phenology models for pest management

The successful development of phenology models from field studies depends on many factors, some of which are entirely under the control of pest managers. For example, one such factor is the choice of method for calculating thermal units. In this study, we have demonstrated that four methods for calculating thermal units provided for acceptable predictions of one phenological event of one insect species, while another method for calculating thermal units did not. The measure of central tendency (mean or median) that is used to estimate lower developmental temperatures and required thermal summations is another factor that pest managers can control when developing phenology models from field studies. Here, we show that predictions that were made when using phenology models based on median lower developmental temperatures and median required thermal summations were superior to predictions that were made when using phenology models based on mean lower developmental temperatures and mean required thermal summations. The use of bootstrap vs. non-bootstrap estimates of lower developmental temperatures and required thermal summations is yet another factor that pest managers can control when developing phenology models from field studies. In this study, we found that calculating and using bootstrap estimates of lower developmental temperatures and required thermal summations in phenology models did not improve the predictions of one phenological event for one insect species. The implications of these and other findings are discussed.     

Analysis of Influencing Factor of Land Price Based on Bootstrap Method

There are a lot of influencing factors of land price. Method of t-test of regression analysis model can be used to conduct the analysis of significance of influencing factor of land price. In the case of small sample or indefinite error distribution, there is a problem of unlikelihood of statistical referrer. So that, introduce Bootstrap method in the model to increase precision of estimated variance of parameters and reliability of statistical referrer of significance of influencing factor. Take an empirical analysis based on the data of land deal of Chongqing. The result indicates that it is very efficient to solve such problem by Bootstrap method.     

Prediction of calibration interval for a measuring instrument using rolling grey bootstrap fusion model

A rolling grey bootstrap fusion model (RGBFM(1,1)) is proposed to predict calibration interval of a measuring instrument under small sample. The model combines GM(1,1) model with bootstrap method. Bootstrap re-sampling is used in the process of modeling the grey differential coefficient function to mine more information about systems. Both the instantaneous value and interval assessment values can be predicted using RGBFM(1,1), which can reduce prediction risk of calibration interval. In contrast, other prediction models only predict the instantaneous value. Experiments show that the RGBFM(1,1) can exactly describe the random wave of original sample data in prediction of instantaneous value, interval upper limit and lower limit, and has higher prediction reliability. Therefore, the RGBFM(1,1) is suitable for the prediction of calibration interval for a measuring instrument.     

Analysis of diversity in populations of plant pathogens: the barley powdery mildew pathogen across Europe

Understanding population genetics and evolution within species requires recognition of variation within and between populations and the ability to distinguish between the potential causes of an observed distribution of variation. For this aim several established indices of diversity, and a novel one, were applied to population samples of the barley powdery mildew pathogen,Erysiphe graminis f. sp.hordei. Random spore samples were obtained from the air along transects through regions of interest across large parts of Europe in 1990. Significant geographical differences in diversity of virulence genotypes occurred among regional sub-samples. Diversity was highest in the samples from eastern Germany, Denmark and Austria, whereas the lowest values were found in the samples from Italy, southern France and parts of western Germany. Diversity in the pathogen population was generally related to the degree of diversification of host resistance in time and space, although there was considerable variation in ranking among different measures of diversity. Sensitivity to sample size proved to be the major problem with the use of several established indices of diversity. Working with very large sample sizes we used multiple random subsamples of various smaller sizes to determine how the mean index values changed with changing sample size. The Shannon index proved to be considerably affected by sample size, in contrast to the Simpson index that was therefore used as a global measure of diversity. Limits of confidence were estimated for the Simpson index using the bootstrap method of numerical resampling. The two aspects contributing to global diversity, richness and evenness, were considered separately to allow meaningful interpretation of the Simpson index. Random sub-sampling was used to reduce the influence of sample size differences for these measures. Dissimilarity, a novel measure of diversity for use in plant pathology, indicates the average number of major genes for host resistance against which pathotypes in a sample respond differently. It is thus able to account for the genetic relationship among pathotypes, which is not considered by any other index. The approaches developed in this study help to compare major forces driving evolution of large-scale populations of the barley mildew pathogen.