Geographic patterns of simulated establishment of two Bouteloua species: implications for distributions of dominants and ecotones

Abstract. Our overall objective was to use a soil water model to predict spatial patterns in germination and establishment of two important perennial C₄-bunchgrasses across the North American shortgrass steppe and desert grassland regions. We also predicted changes in establishment patterns under climate change scenarios. Bouteloua gracilis dominates the shortgrass steppe from northeastern Colorado to southeastern New Mexico. Bouteloua eriopoda dominates desert grasslands in central and southern New Mexico. Germination and establishment for each species were predicted at 16 sites along the gradient using a daily time step, multi-layer soil water model (SOILWAT) to determine the percentage of years that temperature and soil water criteria for germination and establishment were met. Percentage of years with predicted establishment decreased from north to south for B. gracilis, but increased from north to south for B. eriopoda, comparable to observed dominance patterns. The 95 % confidence interval around the point at which simulated establishment were equal for the two species was near the location of the shortgrass steppe-desert grassland ecotone where both species are abundant. The intersection in percentage of years with establishment for the two species was predicted to move further north when climate was scaled using three Global Circulation Models (GCMs), indicating a possible northward expansion of B. eriopoda. Our results suggest that recruitment by seed may be an important process in determining, at least in part, the geographic distribution of these two species. Changes in climate that affect establishment constraints could result in shifts of species dominance that may or may not be accompanied by changes in species composition.     

A comparison of species diversity estimators

Although having been much criticized, diversity indices are still widely used in animal and plant ecology to evaluate, survey, and conserve ecosystems. It is possible to quantify biodiversity by using estimators for which statistical characteristics and performance are, as yet, poorly defined. In the present study, four of the most frequently used diversity indices were compared: the Shannon index, the Simpson index, the Camargo eveness index, and the Pielou regularity index. Comparisons were performed by simulating the Zipf–Mandelbrot parametric model and estimating three statistics of these indices, i.e., the relative bias, the coefficient of variation, and the relative root-mean-squared error. Analysis of variance was used to determine which of the factors contributed most to the observed variation in the four diversity estimators: abundance distribution model or sample size. The results have revealed that the Camargo eveness index tends to demonstrate a high bias and a large relative root-mean-squared error whereas the Simpson index is least biased and the Shannon index shows a smaller relative root-mean-squared error, regardless of the abundance distribution model used and even when sample size is small. Shannon and Pielou estimators are sensitive to changes in species abundance pattern and present a nonnegligible bias for small sample sizes (<1000 individuals). Received: May 8, 1998 / Accepted: May 6, 1999     

生物学实验中的湿度控制：改良的装置及工作原理

相对湿度是生物学实验中一个十分重要而又较难准确控制的环境因素．本项研究设计出一种改进的简易控湿装置,可利用普通温光型培养箱同时进行不同湿度水平的生物学实验．该装置由微型气泵、３个试剂瓶串连而成的三联体和一个用有机玻璃制成的生长箱组成,经在２０℃下对饱和酒石酸钠溶液调节的湿度（理论值９２％）连续测定和校正,控湿效果良好,误差仅±１％左右．同时对不同浓度硫酸溶液控制生长箱内不同水平的湿度进行了测定和检验,亦效果良好,且对环境温度的适应性强,在５～３５℃范围内同一硫酸浓度控制相同的稳定湿度．最后,对上述装置控制湿度的稳定性及其影响因素进行了讨论     

Shrinkage and Pretest Nonparametric Estimation of Regression Parameters from Censored Data with Multiple Observations at Each Level of Covariate

A simple linear regression model is considered where the independent variable assumes only a finite number of values and the response variable is randomly right censored. However, the censoring distribution may depend on the covariate values. A class of noniterative estimators for the slope parameter, namely, the noniterative unrestricted estimator, noniterative restricted estimator and noniterative improved pretest estimator are proposed. The asymptotic bias and mean squared errors of the proposed estimators are derived and compared. The relative dominance picture of the estimators is investigated. A simulation study is also performed to asses the properties of the various estimators for small samples.     

A Generalized Class of Synthetic Estimators with Application to Crop Acreage Estimation for Small Domains

This paper defines and discusses a generalized class of synthetic estimators for small domains, using auxiliary information, under simple random sampling and stratified random sampling schemes. The generalized class of synthetic estimators, among others, includes the simple, ratio and product synthetic estimators. The proposed class of synthetic estimators gives consistent estimators if the synthetic assumption holds. Further, it demonstrates the use of the generalized synthetic and ratio synthetic estimators for estimating crop acreage for small domains and also compare their relative performance with direct estimators, empirically, through a simulation study.     

The Autocovariance Function for Marked Point Processes: A Comparison between Two Different Approaches

Exploratory analysis of marked point patterns has previously been conducted using two disjoint techniques, namely the mark correlation function and spectral analysis. Our purpose here is to present two alternative autocovariance estimators to the mark correlation function which not only apply in both planar and lattice situations, but which in the lattice case can also be considered in terms of the inverse Fourier transform of the spectrum. Moreover, they can be applied to isotropic or anisotropic marked point patterns. Various examples are presented to show how these estimators perform when applied to data sets possessing different kinds of mark structure, and a rank test procedure is proposed to enable the construction of empirical tests of hypothesis.     

Efficient Simulation of Multivariate Binomial and Poisson Distributions

Power investigations, for example, in statistical procedures for the assessment of agreement among multiple raters often require the simultaneous simulation of several dependent binomial or Poisson distributions to appropriately model the stochastical dependencies between the raters' results. Regarding the rather large dimensions of the random vectors to be generated and the even larger number of interactions to be introduced into the simulation scenarios to determine all necessary information on their distributions' dependence stucture, one needs efficient and fast algorithms for the simulation of multivariate Poisson and binomial distributions. Therefore two equivalent models for the multivariate Poisson distribution are combined to obtain an algorithm for the quick implementation of its multivariate dependence structure. Simulation of the multivariate Poisson distribution then becomes feasible by first generating and then convoluting independent univariate Poisson variates with appropriate expectations. The latter can be computed via linear recursion formulae. Similar means for simulation are also considered for the binomial setting. In this scenario it turns out, however, that exact computation of the probability function is even easier to perform; therefore corresponding linear recursion formulae for the point probabilities of multivariate binomial distributions are presented, which only require information about the index parameter and the (simultaneous) success probabilities, that is the multivariate dependence structure among the binomial marginals.     

A Likelihood Ratio Test for the Nonparametric Behrens‐Fisher Problem

The nonparametric Behrens‐Fisher hypothesis is the most appropriate null hypothesis for the two‐sample comparison when one does not wish to make restrictive assumptions about possible distributions. In this paper, a numerical approach is described by which the likelihood ratio test can be calculated for the nonparametric Behrens‐Fisher problem. The approach taken here effectively reduces the number of parameters in the score equations to one by using a recursive formula for the remaining parameters. The resulting single dimensional problem can be solved numerically. The power of the likelihood ratio test is compared by simulation to that of a generalized Wilcoxon test of Brunner and Munzel. The tests have similar power for all alternatives considered when a simulated null distribution is used to generate cutoff values for the tests. The methods are illustrated on data on shoulder pain from a clinical trial.     

塔拉多糖的脱色工艺化研究

塔拉多糖是一种半乳甘露聚糖胶,对于我们具有非常重要的应用价值。本实验主要对塔拉提取物中的塔拉多糖进行脱色工艺化研究;在单因素实验的基础上,对活性炭颗粒质量、脱色时间、脱色温度以及脱色次数这四种因素进行正交优化实验,其最佳脱色实验参数为：活性炭颗粒0.6 g,脱色45 min,脱色温度45℃脱色次数3次,脱色率可以达到50.21%,同时多糖类保留率为90.39%。     

Potential biases in screening for plant resistance to insect pests: an illustration with oilseed rape

Breeding to increase crop resistance is a common strategy to decrease damage caused by insect pests, especially in the current context where insecticides are becoming at the same time less accepted by society and less efficient because of widespread pest resistance. The main bottleneck of this strategy is phenotyping. Although simple, high‐throughput methods have been proposed which could be highly useful, they may raise conceptual issues. Using field and laboratory experiments on oilseed rape (Brassica napus) and the pollen beetle (Brassicogethes aeneus syn. Meligethes aeneus), we illustrated possible difficulties with this approach: (i) field screenings might not represent the real attractiveness of the tested genotypes; (ii) plant phenology or spatial organization of the genotypes might bias field screening results; (iii) experiments based on detached plant parts (here, single flower buds or anthers) might not allow to infer the plant–insect relationship of the whole plant. We propose ways to better take these risks into account.