新疆巴音布鲁克繁殖期大天鹅的生境选择

2011年和2012年的6-8月,考察了分布于巴音布鲁克自然保护区的大天鹅(Cygnus cygnus)种群,分析了大天鹅巢址选择特征和育雏期生境选择特征。在野外共记录到了26个大天鹅巢址。与对照样方比较发现,植被高度、水深、所在水域面积、距干扰源距离和安全等级存在显著差异。进一步逻辑斯蒂回归分析表明,植被高度、水深和安全等级是影响大天鹅巢址选择的最主要的3种生境因子。采用样线法对育雏期大天鹅的生境选择进行了调查,发现大天鹅对沼泽湿地表现出正选择性,对草地表现出负选择性。样方法调查中,利用样方与对照样方比较发现,9种生境因子均存在显著差异,逻辑斯蒂回归分析表明,距干扰源距离和安全等级是影响大天鹅育雏期生境选择的最重要的两种因子。这样的选择机制有利于大天鹅更好的躲避敌害,顺利完成孵化和育雏工作。     

京杭运河堤坝区域狗獾的栖息地特征

2006年10-11月和2007年10-11月,在京杭运河邵伯-高邮段的西侧堤坝上,采用样方法测定了狗獾3个不同类型栖息地的特征变量和利用强度,结果表明:狗獾主要生活在郁闭度较高的森林中,对泡桐、泡桐-杨树次生林的利用强度显著的高于杨树人工林(P<0.05);多元线性逐步回归分析表明:洞口数主要受灌木密度、大树密度、草本植物盖度、土壤含水率和人为干扰强度的影响(P=0.002),而粪堆数主要受灌木密度、大树密度和人为干扰强度的影响(P=0.012)。整体来看,影响狗獾栖息地选择的因素主要是郁闭度、人为干扰水平有关的因子。     

甘肃莲花山血雉春夏季栖息地选择

2012年4~8月,在甘肃莲花山国家级自然保护区采用无线电遥测技术并结合野外观察,对血雉春季和夏季的栖息地特征进行了比较研究。逻辑斯蒂回归分析表明,雪深度、灌木盖度和距水源距离是影响血雉春季栖息地选择的主要因子;夏季血雉以乔木数量、灌木高度和坡向作为栖息地选择的主要因子。t检验和U检验结果表明:乔木胸径、针叶树数量、灌木高度、灌木盖度、苔藓盖度、草盖度在春、夏两个季节内差异极显著,而坡向在两个季节内差异显著。     

人工神经网络在糖尿病住院费用研究中的应用

目的 通过对糖尿病住院费用的主要影响因素进行分析，探寻有效分析方法，为合理控制糖尿病住院费用提供理论依据。方法 研究对象为北京市2011年13家三级甲等医院的全部2型糖尿病住院患者病案首页数据，诊断依据为出院主诊断（ICD编码为E11）。采用反向传播神经网络建模，并与多元线性回归模型相比较，分析糖尿病住院费用影响因素。结果 (1)反向传播神经网络预测误差比多元线性回归低，在数据拟合方面优于多元线性回归模型。(2)两模型结果显示，糖尿病住院费用影响因素中排前两位的是住院日和出院科别，单项费用中药品费、化验费、检查费占据前三位。结论 在对糖尿病住院费用分析时反向传播神经网络优于多元线性回归。     

河南董寨白冠长尾雉繁殖期栖息地选择

2001年至2003年春季,采用样线调查和媒鸟招引,在河南董寨国家级自然保护区对我国特有珍稀雉类白冠长尾雉（Syrmaticus reevesii）的栖息地选择进行了调查,结合RS和GIS分析了在景观水平上对栖息地的选择性,并借助逐步逻辑斯谛回归分析了影响繁殖期白冠长尾雉栖息地的关键尺度和主要因素。结果表明,在白云保护站,占区雄性白冠长尾雉在不同栖息地类型中的出现频率不同,出现最多的是混交林,其次是杉木林,随后是松林、灌丛、阔叶林;在董寨自然保护区内,在115 m尺度和250 m尺度上,针叶林的面积比例均是影响其栖息地选择的关键因子,而到农田的距离是距离因素中最重要的因素。根据回归分析和AIC_C及ΔAIC_C值,115 m尺度上栖息地变量对白冠长尾雉繁殖期的栖息地选择影响最大。综合分析表明,影响白冠长尾雉繁殖期栖息地选择的主要因子为115 m尺度上针叶林的面积比例和到农田的距离。建议在制定白冠长尾雉栖息地保护策略时,应加强现有适宜栖息地的管理,改善栖息地布局,并从景观尺度上开展针叶林对白冠长尾雉种群影响方面的研究工作。     

土壤中微生物含量影响因素的统计方法分析

【目的】采用多元方差分析、多元线性回归分析、通径分析等统计方法,并利用多元统计方法对土壤中微生物含量进行分析。【方法】通过研究这3种常用统计方法的原理、应用条件、适用于解决的问题等,用不同的方法分析影响土壤中微生物含量的各种因素,利用SPSS软件求解。【结果】得出不同降雨量下土壤中的微生物含量存在差异性;铵态氮含量和海拔高度与变形菌门含量呈显著的线性关系,土壤中变形菌门含量随着铵态氮含量增多而增多,且铵态氮含量对变形菌门含量所起的直接作用最大;土壤中铵态氮含量、总氮量和海拔高度与放线菌门含量呈显著的线性关系,且均为负相关;其中总氮量对土壤中放线菌门含量的变化所起的负面作用最大,但铵态氮含量对放线菌门含量所起的直接作用最大。对比描述性统计方法与多元方差分析方法可知,应用描述性统计方法只能得出降雨量是影响土壤中不同细菌群落相对含量的因素之一,而不知降雨量对微生物含量变化的影响程度,6个梯度降雨量引起地显著差异究竟是由哪种微生物引起的,方差分析方法可以解决以上的问题。对比多元线性回归分析方法与通径分析方法可知,多元线性回归分析得出的相关系数只能表示铵态氮含量、总氮量和海拔与变形菌门含量(放线菌门含量)之间关系的密切程度,但无法解释和分析这种关系的构成和来源,通径分析方法可以解决这个问题。【结论】通径分析方法在分析相关问题时要优于多元线性回归分析方法,通径分析比多元线性回归分析方法更能体现因素对变形菌门含量(放线菌门含量)的直接影响和间接影响,分析结论更直观、更能说明问题。     

医院管理人员与医护人员对医患关系评价的差异性分析

目的 了解医院行政管理人员与医护人员对医患关系的总体评价,探究其差异产生的原因。方法 对北京市5家三级甲等医院114名医院行政管理人员和378名医护人员进行问卷调查,运用秩和检验、Logistic回归等方法对其结果进行统计学分析。结果 医院行政管理人员与医务人员对医患关系的评价存在差异,具有统计学意义;差异产生的原因与他们的生活满意度和工作总体感受相关。结论 医院行政管理人员与医护人员对医患关系评价的差异与其各自的岗位特点有直接关系;医院行政管理需加强人本管理理念,科学管理医院,同时追求更高的医疗质量,打造坚实的医院品牌,构建和谐的医患关系。     

黄河三角洲自然保护区东方白鹳的巢址利用

2009年3-5月,在黄河三角洲保护区,运用Vanderploeg和Scavia选择指数和资源选择函数对25个东方白鹳巢址的利用进行了研究。结果表明:东方白鹳偏好选择在芦苇沼泽中的电线杆上筑巢,巢区水面比例较大,样方内水深度>15cm,植被较高,盖度较大,植被密度200-500根/m²,巢址距明水面、芦苇沼泽、树林距离较近,明水面深度<0.5m,距离人为干扰、农田、草地较远。东方白鹳巢址利用的资源选择函数为:logist(p)=-0.206-2.773×距明水面距离+2.200×距公路距离+1.444×距轻度干扰地距离+1.205×距草地距离＋1.191×植被密度,模型正确率:88.6％,R²=0.721。食物、人为干扰和植被密度是影响东方白鹳巢址利用的主要因素。     

基于树木起源、立地分级和龄组的单木生物量模型

以马尾松(Pinus massoniana)和落叶松(Larix)的大样本实测资料为建模样本,以独立抽取的样本为验证样本,把样本按起源、立地和龄组进行分级,采用与材积相容的两种相对生长方程,分普通最小二乘和两种加权最小二乘,对地上部分总生物量、地上各部分生物量和地下生物量进行模型拟合和验证,使用决定系数、均方根误差、总相对误差和估计精度等8项统计量对结果进行分析。结果表明:两个树种地上部分总生物量,立地分类方法,模型的拟合结果和适用性都最优;马尾松VAR模型较优,而落叶松CAR模型较好;两种加权最小二乘方法,在建模样本和验证样本中表现得不一致。在建模样本中,加权回归2(权重函数1/f^0.5)略优于加权回归1(权重函数1/y^0.5),但在验证样本中,加权回归1却明显优于加权回归2。而同时满足建模样本拟合结果最优和验证样本检验结果最优的组合中,只有加权回归1。两个树种地上部分各分量生物量,模型拟合结果和适用性,均为干材最优,树叶最差、树枝和树皮居中,样本分类、模型类型和加权最小二乘方法对干材生物量的影响,规律和地上部分总生物量相同;样本分类、模型类型和加权最小二乘方法的最优组合,用验证样本检验的结果,总相对误差树枝不超过±10.0%,树皮不超过±5.0%,树叶马尾松不超过±30.0%,落叶松不超过±20.0%。两个树种地下部分(根)生物量,样本按龄组分类方法,模型拟合结果最优,与材积相容的模型总体上优于与地上部分总生物量相容模型。     

白马鸡繁殖早期栖息地选择和空间分布

2003年1-6月和2004年4-6月考察了分布于四川省稻城县著杰寺周围的白马鸡(Crossoptiloncrossop-tilon)种群,分析繁殖早期白马鸡繁殖对的栖息地选择和空间分布特征。雌雄个体配对前,对整个研究区域进行系统取样并测量若干环境变量的参数。配对后,利用随机样线调查繁殖对出现的位置和数量。有繁殖对出现的栅格定义为探测栅格,反之为非探测栅格。水源距离、灌木盖度、灌木高度和草本高度等变量在探测和非探测栅格间差异显著。将显著差异变量及这些变量间的一级互作经单变量逻辑斯蒂回归进行筛选,以保留变量为自变量进行向前筛选的逐步逻辑斯蒂回归,最后选择具有最小AICC值的回归等式为最佳的回归模型。模型表明繁殖对的栖息地选择与水源距离负相关,与灌木盖度正相关。2004年进行了重复调查,获得实际的探测和非探测栅格与模型预测值之间无显著差异。计算繁殖对在0°(东-西),45°(东北-西南),90°(南-北)和135°(西北-东南)4个方向上的变异函数。结果表明:繁殖对的方向性变异函数可用球状模型来拟合,拟合曲线和实验曲线间的拟合程度达到显著水平,繁殖对呈明显的各向异性聚集分布[动物学报51(3):383-392,2005]。     

中国大陆鸟类栖息地选择研究十年

栖息地选择研究一直是鸟类生态学研究的重要内容之一。通过对2001年1月至2010年12月10年期间中国大陆鸟类学家在国内外期刊上发表的鸟类栖息地选择研究的论文进行分析,对我国鸟类栖息地选择研究提出展望。10年间,我国鸟类学家共发表有关鸟类栖息地选择或利用的论文170篇,共涉及到鸟类10目31科73种。在这10年里,中国大陆有关鸟类栖息地选择或利用的文章持续增长。然而,我国鸟类栖息地选择的研究也存在着如下问题:(1)存在栖息地选择和栖息地利用误用的现象,这种现象在10年内并无明显改善;(2)在选择研究方法时,很少考虑个体的可获得性、种群密度及抽样尺度对栖息地选择的影响;(3)大多数的栖息地选择的论文没有对鸟类的栖息地选择行为进行研究,也缺乏对其选择的适合度背景去进行研究。根据这些问题,对我国未来的鸟类栖息地选择研究提出了建议。     

中国红豆属植物生物与生态学特征研究现状

红豆属(Ormosia)隶属豆科(Fabaceae),全属约130种,均为木本植物,我国有37种,广东、广西、云南、海南为主要分布区。该属野外自然更新能力较差,对生存环境要求严格。由于该属大部分物种具有极高的经济价值和开发利用价值,因此人为干扰及盗伐现象严重,分布范围收缩且野外种群数量减少。本文在查阅文献的基础上,对红豆属植物的生态学特征、组织培养、生理生化、遗传、濒危现状与保护对策等方面的研究成果进行归纳总结并进行了展望,以期为深入研究红豆属植物、拯救红豆属物种提供参考。     

Non-linear heteroscedastic regression model for determination of methotrexate in human plasma by high-performance liquid chromatography

Generalized least squares regression with variance function estimation was used to derive the calibration function for measurement of methotrexate plasma concentration and its results were compared with weighted least squares regression by usual weight factors and also with that of ordinary least squares method. In the calibration curve range of 0.05 to 100 microM, both heteroscedasticity and non-linearity were present therefore ordinary least squares linear regression methods could result in large errors in the calculation of methotrexate concentration. Generalized least squares regression with variance function estimation worked better than both the weighted regression with the usual weight factors and ordinary least squares regression and gave better estimates for methotrexate concentration.     

Effects of selection and drift on G matrix evolution in a heterogeneous environment: a multivariate Qst-Fst Test with the freshwater snail Galba truncatula

Unraveling the effect of selection vs. drift on the evolution of quantitative traits is commonly achieved by one of two methods. Either one contrasts population differentiation estimates for genetic markers and quantitative traits (the Q(st)-F(st) contrast) or multivariate methods are used to study the covariance between sets of traits. In particular, many studies have focused on the genetic variance-covariance matrix (the G matrix). However, both drift and selection can cause changes in G. To understand their joint effects, we recently combined the two methods into a single test (accompanying article by Martin et al.), which we apply here to a network of 16 natural populations of the freshwater snail Galba truncatula. Using this new neutrality test, extended to hierarchical population structures, we studied the multivariate equivalent of the Q(st)-F(st) contrast for several life-history traits of G. truncatula. We found strong evidence of selection acting on multivariate phenotypes. Selection was homogeneous among populations within each habitat and heterogeneous between habitats. We found that the G matrices were relatively stable within each habitat, with proportionality between the among-populations (D) and the within-populations (G) covariance matrices. The effect of habitat heterogeneity is to break this proportionality because of selection for habitat-dependent optima. Individual-based simulations mimicking our empirical system confirmed that these patterns are expected under the selective regime inferred. We show that homogenizing selection can mimic some effect of drift on the G matrix (G and D almost proportional), but that incorporating information from molecular markers (multivariate Q(st)-F(st)) allows disentangling the two effects.     

Correlation analysis of dissimilarity matrices

Distance-based methods have been a valuable tool for ecologists for decades. Indirectly, distance-based ordination and cluster analysis, in particular, have been widely practiced as they allow the visualization of a multivariate data set in a few dimensions. The explicitly distance-based Mantel test and multiple regression on distance matrices (MRM) add hypothesis testing to the toolbox. One concern for ecologists wishing to use these methods lies in deciding whether to combine data vectors into a compound multivariate dissimilarity to analyze them individually. For Euclidean distances on scaled data, the correlation of a pair of multivariate distance matrices can be calculated from the correlations between the two sets of individual distance matrices if one set is orthogonal, demonstrating a clear link between individual and compound distances. The choice between Mantel and MRM should be driven by ecological hypotheses rather than mathematical concerns. The relationship between individual and compound distance matrices also provides a means for calculating the maximum possible value of the Mantel statistic, which can be considerably less than 1 for a given analysis. These relationships are demonstrated with simulated data. Although these mathematical relationships are only strictly true for Euclidean distances when one set of variables is orthogonal, simulations show that they are approximately true for weakly correlated variables and Bray–Curtis dissimilarities.     

新疆北塔山雪豹对秋季栖息地的选择

2004年9~10月,对新疆北塔山地区雪豹(Unicaunica)栖息地选择进行了调查。在选定的15条样带上测定了59个利用样方及30个任意样方的6种生境特征(海拔、地形、植被类型、生境平坦度、放牧状况和坡向),其结果如下:Vanderploeg和Scavia选择指数表明:雪豹对海拔、地形、植被类型、生境平坦度和坡向存在选择性:(1)雪豹偏好利用海拔2000~2200m,而避开2600~3000m区域;(2)倾向于悬崖底部和山嵴,避开山坡和山谷;(3)偏好于灌丛,避开森林;(4)避开平坦的开阔地;(5)倾向于选择非放牧区域活动;(6)倾向于选择北坡,避开南坡。对生境特征的主成分分析显示:前3个主成分(海拔、地形和植被类型)的累积贡献率达到75·76%,可以反映雪豹的栖息地特征,同时表明:影响雪豹栖息地选择的主要因素依次为放牧状况、植被类型、地形和生境平坦度。     

Island biogeography as a test of reproductive interference

Theoretical studies have predicted that reproductive interference must exclude either of the interacting species, but no testing of this prediction has ever been reported for natural populations. This study surveyed the distribution patterns of herbaceous Veronica plants, including one native and three alien species, to test whether the invasion of the alien species exerting reproductive interference excluded the native species. Results showed that the native species was repeatedly excluded from islands where an alien species invaded, exerting reproductive interference, and that other alien species had no significant effect on the native population survival. This survey also demonstrated that the native species altered its habitat from the ground to stone walls on the mainland where the alien species had been predominant. In the mainland populations, the fruit morphology differed from that of the islands, and the morphology in the mainland population seemed suitable for seed dispersion by ants at a stone wall habitat. We also surveyed the genetic differentiation among populations, the results of which did not support the native species genetically differentiated between mainland and island populations before the alien species invasion. These results strongly suggest that the reproductive interference excluded the recipient species at the population level and facilitated the habitat change. Additionally, results indicated that a series of field surveys of islands close to the mainland can be a powerful tool to test the ecological importance of reproductive interference.     

Estimating resource selection with count data

Resource selection functions (RSFs) are typically estimated by comparing covariates at a discrete set of “used” locations to those from an “available” set of locations. This RSF approach treats the response as binary and does not account for intensity of use among habitat units where locations were recorded. Advances in global positioning system (GPS) technology allow animal location data to be collected at fine spatiotemporal scales and have increased the size and correlation of data used in RSF analyses. We suggest that a more contemporary approach to analyzing such data is to model intensity of use, which can be estimated for one or more animals by relating the relative frequency of locations in a set of sampling units to the habitat characteristics of those units with count‐based regression and, in particular, negative binomial (NB) regression. We demonstrate this NB RSF approach with location data collected from 10 GPS‐collared Rocky Mountain elk (Cervus elaphus) in the Starkey Experimental Forest and Range enclosure. We discuss modeling assumptions and show how RSF estimation with NB regression can easily accommodate contemporary research needs, including: analysis of large GPS data sets, computational ease, accounting for among‐animal variation, and interpretation of model covariates. We recommend the NB approach because of its conceptual and computational simplicity, and the fact that estimates of intensity of use are unbiased in the face of temporally correlated animal location data.     

Why do we still use stepwise modelling in ecology and behaviour?

1. The biases and shortcomings of stepwise multiple regression are well established within the statistical literature. However, an examination of papers published in 2004 by three leading ecological and behavioural journals suggested that the use of this technique remains widespread: of 65 papers in which a multiple regression approach was used, 57% of studies used a stepwise procedure. 2. The principal drawbacks of stepwise multiple regression include bias in parameter estimation, inconsistencies among model selection algorithms, an inherent (but often overlooked) problem of multiple hypothesis testing, and an inappropriate focus or reliance on a single best model. We discuss each of these issues with examples. 3. We use a worked example of data on yellowhammer distribution collected over 4 years to highlight the pitfalls of stepwise regression. We show that stepwise regression allows models containing significant predictors to be obtained from each year's data. In spite of the significance of the selected models, they vary substantially between years and suggest patterns that are at odds with those determined by analysing the full, 4-year data set. 4. An information theoretic (IT) analysis of the yellowhammer data set illustrates why the varying outcomes of stepwise analyses arise. In particular, the IT approach identifies large numbers of competing models that could describe the data equally well, showing that no one model should be relied upon for inference.