一种模型模拟结果的统计检验方法

模拟结果的准确性是衡量生态学模型是否成功的关键,但采用统计学方法判别模型模拟结果与观察值相符程度的报道较少。根据两个直线回归方程能否合并为一个方程的统计学检验方法,提出了通过检验观察值与模拟值直线回归方程和1∶1直线方程截距与斜率是否相同,进而在统计显著水平上判断生态学模型模拟值与观察值一致性的统计学检验方法。数据检验表明,此方法可以较好解决判断生态学模型模拟结果准确性的问题。     

最大熵原理及其在生态学研究中的应用

最大熵原理(the principle of maximum entropy)起源于信息论和统计力学,是基于有限的已知信息对未知分布进行无偏推断的一种数学方法.这一方法在很多领域都有成功应用,但只是近几年才被应用到生态学研究中,并且还存在很多争论.我们从基本概念和方法出发,用掷骰子的例子阐明了最大熵原理的概念,并提出运用最大熵原理解决问题需要遵从的步骤.最大熵原理在生态学中的应用主要包括以下方面:(1)用群落水平功能性状的平均值作为约束条件来预测群落物种相对多度的模型;(2)基于气候、海拔、植被等环境因子构建物种地理分布的生态位模型;(3)对物种多度分布、种一面积关系等宏生态学格局的推断;(4)对物种相互作用的推断;(5)对食物网度分布的研究等等.最后我们综合分析了最大熵原理在生态学应用中所存在的争议,包括相应模型的有效性、可靠性等方面,介绍了一些对最大熵原理预测能力及其局限性的检验结果,强调了生态学家应用最大熵原理需要注意的问题,比如先验分布的选择、约束条件的设置等等.在物种相互作用、宏生态学格局等方面对最大熵原理更广泛的讨论与应用可能会给生态学带来新的发展机会.     

太平洋甲胁虱（虱目：甲胁虱科）在其主要宿主黄胸鼠体表的空间分布格局

太平洋甲胁虱是寄生于黄胸鼠体表的一种主要吸虱昆虫 ,在云南广泛分布。应用Iwao直线回归方法及其随机偏离度检验对太平洋甲胁虱在黄胸鼠不同个体间的空间分布格局进行了研究。根据Iwao直线回归方法 ,建立了M =12 .10 +4.76M (r=0 .75 ,P <0 .0 1)的回归方程 ,所得到的α与 β值 (α =12 .10 ,β =4 .76 )均明显高于判定界线值 0和 1。对α与β值进行随机偏离度检验 ,F =6 .0 7(P <0 .0 5 ) ,由此判定太平洋甲胁虱在黄胸鼠不同个体间的空间分布格局为聚集型分布 ,这说明太平洋甲胁虱对黄胸鼠的寄生是不均匀的 ,存在聚集并有形成大小不一的吸虱个体群的趋势。     

从中指骨长度推算身高的研究

作者对近年在华南地区收集的,已知生前身高的汉族成年男性骨骼的中指骨近节、中节进行了测量。用直线回归方程、多元回归方程对从中指骨长度推算身高进行了研究。并用50例国人骨骼标本对这些推算身高的方法作了检验。结果表明,中指骨与四肢大型长骨,以及从中指骨长度推算身高的直线回归方程与多元回归方程,对推算身高的价值都是相同的。     

最小一乘线性回归在昆虫预报中的应用

<正> 回归分析方法作为一项有效的预报方法,目前已成功地应用于虫情预报上。尤其是一元回归方程,其方法简单实用,深受昆虫工作者欢迎。 我们对于一元回归方程,利用最小二乘法求出回归方程中的回归系数及其截距,即设有自变量x和因变量y,我们进行n次观测,得数据(x_1,y_1),(x_2,y_2),…(x_n,y_n)到直线的离     

用中国汉族男性髋骨推断向往身高的研究

本文研究了以中国汉族男性髋骨推断身高的方法。研究样本来自公安部第二研究所法医室近年收集的２４８对已知确切身高的汉族成年男性完整干燥髋骨。依据人体测量学方法,共测量了１２项指标。相关分析表明,所测指标与身高的相关系数的统计学检验均有非常显著的意义。进而建立了髋骨推断身高的直线回归方程及多元回归方程。并用３０对国人髋骨对各方程进行了盲测。结果表明,髋骨推算身高的直线回归方程。并用３０对国人髋骨对各方程     

罗氏Cobas c501检测系统尿素分析测量范围的验证及评价

目的:核实并评价罗氏Cobasc501检测系统尿素分析测量范围。方法:主要参照美国临床实验室标准化委员会(NCCLS)指南文件EP6-P的要求,收集含高值待测物的新鲜病人血清,按一定比例混合、离心,计算混合物的浓度并将之作为高值样品(H),与经同样处理获得的低值样品(L)分别按5L、4L+1H、3L+2H、2L+3H、1L+4H、5H的关系配制,形成系列样品,在罗氏Cobasc501检测系统上对各样品的尿素进行检测,每个样品检测4次,数据进行回归分析。结果:回归方程为y=0.9889x+0.19,b=0.9889,介于0.97～1.03之间,ta小于t0.05,说明截距与0无显著性差异,回归直线事实上通过零点。结论:罗氏Cobasc501检测系统检测尿素的分析测量范围为0.7～49.9mmol/L,宽于厂家提供的分析测量范围0.5～40.0mmol/L,完全符合临床检验要求。     

动物栖息地选择评估的常用统计方法

利用合适的统计学方法能够更准确地理解动物的栖息地选择。本文通过对2003~2012年期间,10个国际期刊所发表的177篇关于鸟类和兽类栖息地选择论文的30种统计学方法进行分析,简要概述了目前流行的栖息地选择统计学分析方法及特点,同时对同时期的中文文献也进行了简要分析。目前关于动物栖息地选择较为流行的分析方法主要有逻辑斯蒂回归、资源选择函数、成分分析、广义线性模型、多元方差分析、基于欧几里德距离的方法、广义线性混合模型、生态位因子分析、基于个体模型、典型相关分析、物种分布模型等。广义线性模型、逻辑斯蒂回归、多元方差分析和基于欧几里德距离这些方法可以很灵活地用来分析数据,但是缺乏一个有生态学意义的理论框架。资源选择函数和生态位因子分析各自为栖息地选择研究提供了一个统一的理论框架。基于个体的模型是一个自下而上的过程,很难在系统水平形成理论。232篇国内文章中使用较多的方法是主成分分析、Mann-Whitney U检验、t检验、卡方检验、判别分析、方差分析、Vanderloeg选择系数和Scavia选择指数、逻辑斯蒂回归、Kruskal-Wallis H检验和多元回归分析等。在实际研究中,应根据所要解决的研究问题,选择切实可行的分析方法。     

男性锁骨的测量及其最大长推算

目的本文在于探讨用锁骨的某一项测量指标建立推算其最大长的回归方程,然后可根据所得值间接推断死者的身高.方法本地收集的75例成年男性尸体,按体质人类学测量方法,用人体测量仪器对其锁骨的各项指标进行测量,所得值用SPSS统计学软件分析处理.结果相关分析和回归分析,表明锁骨的各项指标与锁骨的最大长之间均有着非常显著性的关系存在,P<0.001.进而建立了相应的回归方程.结论在实际工作中,如果能测得锁骨残段的某一项指标,就可用所建立的回归方程推算其最大长,这在法医人类学上具有一定的参考价值.     

用中国汉族男性髓骨推断身高的研究

本文研究了以中国汉族男性航骨推断身高的方法．研究样本来自公安部第二研究所法医室近年收集的２４８对已知确切身高的汉族成年男性完整干燥防骨．依据人体测量学方法,共测量了１２项指标．相关分析表明,所测指标与身高的相关系数的统计学检验均有非常显著的意义．进而建立了航骨推断身高的直线回归方程及多元回归方程．并用３０对国人脱骨对各方程进行了盲测．结果表明,助骨推算身高的直线回归方程与多元日归方程所得结果与实际身高相近．效果较好．并且田骨推算身高的一元回归方程与多元回归方程的实用价值基本相同．     

Statistical tests as inappropriate tools for data analysis performed on non-random samples of plant communities

Many authors apply statistical tests to sets of relevés obtained using non-random methods to investigate phytosociological and ecological relationships. Frequently applied tests include thet-test, ANOVA, Mann-Whitney test, Kruskal-Wallis test, chi-square test (of independence, goodness-of-fit, and homogeneity), Kolmogorov-Smirnov test, concentration analysis, tests of linear correlation and Spearman rank correlation coefficient, computer intensive methods (such as randomization and re-sampling) and others. I examined the extent of reliability of the results of such tests applied to non-random data by examining the tests requirements according to statistical theory. I conclude that when used for such data, the statistical tests do not provide reliable support for the inferences made because non-randomness of samples violated the demand for observations to be independent, and different parts of the investigated communities did not have equal chance to be represented in the sample. Additional requirements, e.g. of normality and homoscedasticity, were also neglected in several cases. The importance of data satisfying the basic requirements set by statistical tests is stressed.     

Ecologists should not use statistical significance tests to interpret simulation model results

Simulation models are widely used to represent the dynamics of ecological systems. A common question with such models is how changes to a parameter value or functional form in the model alter the results. Some authors have chosen to answer that question using frequentist statistical hypothesis tests (e.g. ANOVA). This is inappropriate for two reasons. First, p‐values are determined by statistical power (i.e. replication), which can be arbitrarily high in a simulation context, producing minuscule p‐values regardless of the effect size. Second, the null hypothesis of no difference between treatments (e.g. parameter values) is known a priori to be false, invalidating the premise of the test. Use of p‐values is troublesome (rather than simply irrelevant) because small p‐values lend a false sense of importance to observed differences. We argue that modelers should abandon this practice and focus on evaluating the magnitude of differences between simulations. Synthesis Researchers analyzing field or lab data often test ecological hypotheses using frequentist statistics (t‐tests, ANOVA, etc.) that focus on p‐values. Field and lab data usually have limited sample sizes, and p‐values are valuable for quantifying the probability of making incorrect inferences in that situation. However, modern ecologists increasingly rely on simulation models to address complex questions, and those who were trained in frequentist statistics often apply the hypothesis‐testing approach inappropriately to their simulation results. Our paper explains why p‐values are not informative for interpreting simulation models, and suggests better ways to evaluate the ecological significance of model results.     

山西翅果油树群落优势种群分布格局研究

 应用扩散系数、聚集指数、平均拥挤度、聚块性指数、Green指数、聚集强度、Poisson分布和负二项分布的X2拟合检验等方法,研究了山西翅果油树群落优势种群的分布格局,并用相关分析比较了6个指数间的关系,结果表明：翅果油树分布格局呈随机型,其余22个优势种的分布格局皆为聚集型,这主要与物种本身的生态和生物学特性有关,以及与物种的竞争排斥作用有密切联系。在判定物种分布格局的8种方法中,以方差／均值比率、Poisson分布和负二项分布的X2拟合检验联合运用效果较好,不仅生态学意义明确,而且结果具有严格的统计学意义。     

The statistical analysis of fluctuating asymmetry: REML estimation of a mixed regression model

The unbiased estimation of fluctuating asymmetry (FA) requires independent repeated measurements on both sides. The statistical analysis of such data is currently performed by a two-way mixed ANOVA analysis. Although this approach produces unbiased estimates of FA, many studies do not utilize this method. This may be attributed in part to the fact that the complete analysis of FA is very cumbersome and cannot be performed automatically with standard statistical software. Therefore, further elaboration of the statistical tools to analyse FA should focus on the usefulness of the method, in order for the correct statistical approaches to be applied more regularly. In this paper we propose a mixed regression model with restricted maximum likelihood (REML) parameter estimation to model FA. This routine yields exactly the same estimates of FA as the two-way mixed ANOVA . Yet the advantages of this approach are that it allows (a) testing the statistical significance of FA, (b) modelling and testing heterogeneity in both FA and measurement error (ME) among samples, (c) testing for nonzero directional asymmetry and (d) obtaining unbiased estimates of individual FA levels. The switch from a mixed two-way ANOVA to a mixed regression model was made to avoid overparametrization. Two simulation studies are presented. The first shows that a previously proposed method to test the significance of FA is incorrect, contrary to our mixed regression approach. In the second simulation study we show that a traditionally applied measure of individual FA [abs(left – right)] is biased by ME. The proposed mixed regression method, however, produces unbiased estimates of individual FA after modelling heterogeneity in ME. The applicability of this method is illustrated with two analyses.     

欧美杨杂交种'中嘉8'净光合速率与若干生态因子的相关分析

本文利用DPS数据处理系统,采用多元逐步回归及通径分析方法,得出欧美杨杂交种‘中嘉8’(Populusdeltoides CL.‘Zhongjia 8’)在6月和9月净光合速率与生态因子的关系。结果表明,‘中嘉8’在6月晴天净光合速率呈单峰曲线,其最主要影响因子为光合有效辐射PAR(X1),二者极显著相关,净光合速率日变化最优方程为:Y=17.8271 0.0108X1-0.2185X2;9月晴天净光合速率呈双峰曲线,影响光合速率日变化的最主要因子为空气CO2浓度Ca(X4),净光合速率日变化最优方程为:Y=5.2915-0.0030X1 0.3414X3-0.0216X4。统计分析表明,采用多元逐步回归及通径分析应用于统计光合速率与生态因子的相关关系,较二元变量的相关分析更为科学合理。     

THE IMPORTANCE OF CONSIDERING PREDICTION VARIANCE IN ANALYSES USING PHOTOGRAMMETRIC MASS ESTIMATES

Development and application of photogrammetric mass-estimation techniques in marine mammal studies is becoming increasingly common. When a photogrammetrically estimated mass is used as a covariate in regression modeling, the error associated with estimating mass induces bias in regression statistics and decreases model explanatory power. Thus, it is important to understand and account for prediction variance when addressing ecological questions that require use of estimated mass values. In a simulation study based on data collected from Weddell seals, we developed regression models of pup weaning mass as a function of maternal postparturition mass where maternal mass was directly measured and second where maternal mass was photogrammetrically estimated. We demonstrate that when estimated mass was used, the regression coefficient was biased toward zero and the coefficient of determination was 30% less than the value obtained when using maternal postparturition mass obtained from direct measurement. After applying bias correction procedures, however, the regression coefficient and coefficient of determination were within 2% of their true values. To effectively use photogrammetrically estimated masses, prediction variance should be understood and accounted for in all analyses. The methods presented in this paper are effective and simple techniques to explore and account for prediction variance.     

Phylogenetic ANOVA: Group‐clade aggregation,biological challenges,and a refined permutation procedure

Phylogenetic regression is frequently used in macroevolutionary studies, and its statistical properties have been thoroughly investigated. By contrast, phylogenetic ANOVA has received relatively less attention, and the conditions leading to incorrect statistical and biological inferences when comparing multivariate phenotypes among groups remain underexplored. Here, we propose a refined method of randomizing residuals in a permutation procedure (RRPP) for evaluating phenotypic differences among groups while conditioning the data on the phylogeny. We show that RRPP displays appropriate statistical properties for both phylogenetic ANOVA and regression models, and for univariate and multivariate datasets. For ANOVA, we find that RRPP exhibits higher statistical power than methods utilizing phylogenetic simulation. Additionally, we investigate how group dispersion across the phylogeny affects inferences, and reveal that highly aggregated groups generate strong and significant correlations with the phylogeny, which reduce statistical power and subsequently affect biological interpretations. We discuss the broader implications of this phylogenetic group aggregation, and its relation to challenges encountered with other comparative methods where one or a few transitions in discrete traits are observed on the phylogeny. Finally, we recommend that phylogenetic comparative studies of continuous trait data use RRPP for assessing the significance of indicator variables as sources of trait variation.     

An evaluation of Salmonella (Ames) test data in the published literature: application of statistical procedures and analysis of mutagenic potency

We searched the published literature for Salmonella test data on some 450 chemicals. Only 137 of more than 400 articles containing original data satisfied minimum criteria for a quantitative analysis [1751 experiments, comprising data on 152 chemicals (Table 1)]. Many of these papers did not report basic information about the test protocol (Table 2). We used previously described statistical procedures (Bernstein et al., 1982) to estimate the initial slopes of the dose-response curves and corresponding standard errors. We also applied tests for significance and linear goodness-of-fit. We then used the results of these analyses to examine several issues: (1) Linearity of the low dose region of the dose-response curve. We found that the overwhelming majority of curves were linear, though ability to detect non-linearity of dose-response curves in the standard plate test is only limited. 7% of all experiments to which the goodness-of-fit test was applied were curves of increasing slope, and with a few possible exceptions, these were not obviously associated with any particular mutagens, even those generally considered to produce non-linear effects such as MNNG and EMS (Table 3). (2) Performance of the statistical test for significance. Results of the statistical test for significance of the dose-response were compared with author's opinions as to positivity. In almost all cases (94%) results of the statistical test and authors opinions were the same. In the examples of conflicting opinions, the reasons were: (a) the statistical test places more weight than do most authors on the presence of a linear dose-response; (b) most authors tend to require at least a 2-fold increase over the spontaneous background for 'significance', and (c) when the number of spontaneous revertants is small (e.g., TA1537), authors tend to require a larger increase in induced revertants than when the spontaneous background is large, whereas the statistical procedure makes no such distinction. These factors result in the statistical test tending to identify more experiments as positive than do authors, provided there is a linear dose-response, and authors tending to judge more experiments as positive when the dose-response is not linear. (3) Reproducibility. Among the 1751 experiments there were 122 data-sets (a total of 333 experiments) in which the same chemical was tested by two or more different laboratories under the same protocol. 21 of the 122 data-sets had some disagreement between experiments as to whether results were positive or negative (Table 4).(ABSTRACT TRUNCATED AT 400 WORDS)