Food, density, and microhabitat: factors affecting growth and recruitment potential of juvenile saltmarsh fishes

The relationships among microhabitat use, food habits, conspecific density and recent growth rate for estuarine-dependent juvenile spotted seatrout, Cynoscion nebulosus, and red drum, Sciaenops ocellatus, were studied to determine how nursery habitat influences early growth. Juvenile spotted seatrout and red drum were quantified along the marsh-edge ecotone from multiple drop samples, and their immediate environments characterized by a suite of physical and chemical variables along with substrate type and Spartina stem density. Recent daily growth of individual fish was modeled in a series of multiple regression analyses that considered the relative contributions of food, microhabitat, and conspecific density. The spotted seatrout model (p < 0.0001) included four independent variables, otolith radius, prey diversity, salinity, and a salinity-DO interaction term, and explained 67.9% of the variation in daily growth. All variables were significant (p < 0.05), and regression slopes were positive for all variables except salinity. The red drum model (p > 0.0001) included five independent variables, otolith radius, temperature, salinity, depth and substrate, and explained 63.3% of the variation in daily growth. All variables were significant (p > 0.05), and all regression slopes were positive. Fish size (as estimated by otolith radius) accounted for most of the variance in the spotted seatrout (60.2%) and red drum (44.8%) models, while the remaining environmental variables were significant and responsible for 7.7 and 18.5%, respectively. Density variables were not selected for either model, suggesting that density-dependence was not an important influence on recent daily growth. Generally, physico-chemical variables such as temperature, salinity, and dissolved oxygen contributed more to growth than diet or extrinsic factors such as grass stem density.     

热泉微生物驱动的氮循环研究进展及展望

热泉微生物是驱动热泉氮(N)循环的主导力量,开展热泉生态系统中驱动氮循环微生物种群构成及其与环境响应的研究,对于探索热泉中氮的生物地球化学循环、生命进化、生物修复等方面都具有重要的理论和应用价值。本文综合阐述了热泉生态系统驱动氮循环的功能微生物(如固氮菌、氨氧化菌、厌氧氨氧化菌、反硝化菌、异化硝酸盐还原菌)在系统发育学上的分布、功能基因的相对丰度、活性及其与环境因子(如温度、pH)的相关性等方面的研究现状和亟待解决的问题。并展望了热泉生境中驱动氮循环微生物未来的研究方向。     

AnthropMMD: An R package with a graphical user interface for the mean measure of divergence

The mean measure of divergence is a dissimilarity measure between groups of individuals described by dichotomous variables. It is well suited to datasets with many missing values, and it is generally used to compute distance matrices and represent phenograms. Although often used in biological anthropology and archaeozoology, this method suffers from a lack of implementation in common statistical software. A package for the R statistical software, AnthropMMD, is presented here. Offering a dynamic graphical user interface, it is the first one dedicated to Smith's mean measure of divergence. The package also provides facilities for graphical representations and the crucial step of trait selection, so that the entire analysis can be performed through the graphical user interface. Its use is demonstrated using an artificial dataset, and the impact of trait selection is discussed. Finally, AnthropMMD is compared to three other free tools available for calculating the mean measure of divergence, and is proven to be consistent with them.     

Hierarchical models for combining ecological and case-control data

The ecological study design suffers from a broad range of biases that result from the loss of information regarding the joint distribution of individual-level outcomes, exposures, and confounders. The consequent nonidentifiability of individual-level models cannot be overcome without additional information; we combine ecological data with a sample of individual-level case-control data. The focus of this article is hierarchical models to account for between-group heterogeneity. Estimation and inference pose serious computational challenges. We present a Bayesian implementation based on a data augmentation scheme where the unobserved data are treated as auxiliary variables. The methods are illustrated with a dataset of county-specific infant mortality data from the state of North Carolina.     

Boosted regression trees with errors in variables

In this article, we consider nonparametric regression when covariates are measured with error. Estimation is performed using boosted regression trees, with the sum of the trees forming the estimate of the conditional expectation of the response. Both binary and continuous response regression are investigated. An approach to fitting regression trees when covariates are measured with error is described, and the boosting algorithms consist of its repeated application. The main feature of the approach is that it handles situations where multiple covariates are measured with error. Some simulation results are given as well as its application to data from the Framingham Heart Study.     

USING TREND INDICES FOR ENDANGERED SPECIES

Initial appraisals of the status of endangered large-mammal populations may have to depend on indices of population trend. Such indices may possibly be improved by using auxiliary variables. Various models were studied for populations of the Florida manatee (Trichechus manatus latirostris), Yellowstone grizzly bear (Ursus arctos horribilis), and Hawaiian monk seal (Monachus schauinslandi). Several criteria for checking validity of the fitted models were considered, and the simple R² criterion appears to provide useful comparisons. Multiple regression models overestimated the rate of change of the East Coast manatee population as determined from three other sources (a covariance model, a non-linear model, and the rate estimated from reproductive and survival data). A multiple regression model for grizzly bears using three auxiliary variables exhibited a fairly high R² (0.84) and appeared to provide a better fit than did a non-linear model. A beach count index for Hawaiian monk seals seemed to be unreliable for year-to-year comparisons in contrast to total population counts and estimates from a capture-recapture method. The use of auxiliary variables for checking and improving trend index data appears feasible and well worthwhile.     

Testing the Equality of the Intra‐Subject Treatment Covariance Matrices in a Crossover Study

In a 2 × 2 crossover bioavailability study, the sets of estimates of the pharmacokinetic parameters quite often have a symmetric covariance structure between the two treatments. For testing the equality of the intra‐subject covariance matrices for the two treatments in such studies, we suggest in this paper some statistical tests. When the response vectors are bivariate, we propose an exact test. Since the statistical procedures depend on the assumption of a symmetric covariance structure between the two treatments, we put forth some statistical tests for this assumption. We then apply the discussed tests to real data from a crossover bioavailability trial.     

One‐Sided Test to Assess Correlation in Linear Logistic Models using Estimating Equations

A score‐type test is proposed for testing the hypothesis of independent binary random variables against positive correlation in linear logistic models with sparse data and cluster specific covariates. The test is developed for univariate and multivariate one‐sided alternatives. The main advantage of using score test is that it requires estimation of the model only under the null hypothesis, that in this case corresponds to the binomial maximum likelihood fit. The score‐type test is developed from a class of estimating equations with block‐diagonal structure in which the coefficients of the linear logistic model are estimated simultaneously with the correlation. The simplicity of the score test is illustrated in two particular examples.     

A Class of Almost Unbiased Estimators for Finite Populations Mean Using Two Auxiliary Variables

For estimating finite population mean -Y₀ of study character y₀, a class of almost unbiased estimators applying jackknife technique envisaged by Quenouille (1956) is derived. Optimum unbiased estimator (OUE) is also investigated with its variance formula. An empirical study is carried out to demonstrate the performance of the constructed estimator over the usual unbiased estimator, Srivastava (1965), Singh (1967), Singh and Biradar (1992), Tracy , Singh , and Singh (1996) and other almost unbiased estimators.     

新疆阿勒泰地区爬行动物区系及多样性海拔分布格局

中国阿勒泰地区是北半球中纬度地区的特殊区域, 与俄罗斯、哈萨克斯坦、蒙古接壤, 境内有平原、沙漠和山地三大地貌特征。2014-2016年, 我们先后3次对阿勒泰地区爬行动物资源开展了实地调查, 共采集到392号爬行动物标本。经鉴定隶属1目6科16种, 结合文献资料, 共计1目8科23种, 均为古北界物种, 其中中亚型17种, 古北型6种, 表现出中亚亚界与欧洲-西伯利亚亚界间的过渡性质。爬行动物Shannon-Wiener多样性指数、Simpson优势度指数随海拔分布呈带有一个中峰的低海拔高原格局, 在第2海拔段(500-600 m)达到峰值; 均匀度指数总体呈现递增格局, 第15海拔段(1,800-1,900 m)达到最大值; 丰富度则呈现一种波动的递减格局, 峰值在第10海拔段(1,300-1,400 m)。3种环境因子对这种格局的独立解释力较低, 但综合作用的解释力较强, 其中海拔与年均温这两个因子的综合作用对物种多样性影响程度最大。