白三叶和牛鞭草生物量增长规律与环境因子的关系

通过白三叶Trifolium repens和牛鞭草Hemarthria compressa的生物量增长规律的测定以及生长速率与环境因子的相关分析表明,牛鞭草的环境容纳量大于白三叶,瞬时增长率小于白三叶,逻辑斯谛曲线拐点的出现晚于白三叶。白三叶的生长速率与太阳辐射之间达到显著性相关,牛鞭草的生长速率与牛鞭草现存生物量之间达到显著性相关。     

混播草地中多年生黑麦草与白三叶的生长特性及其共存表现

从混播牧草品种，刈牧制度，利用强度及施肥措施等方面综述了多年生黑麦草／白三叶混播草地中，多年生黑麦草分蘖个体和白三叶生长点的生长特性，二者种群产量及其竞争，共存变化的一般表现。指出，在适宜刈牧利用制度下，辅以一定农艺措施（施肥），可以提高白三叶对资源的利用能力和环境适应性，利于混播草地中多年生黑麦草与白三叶的长期稳定共存。     

白三叶无性系种群生态学研究进展

就白三叶无性系种群的研究进展进行了综述。     

环渤海低平原旱碱区冷季型草坪草适宜草种鉴定研究

针对环渤海低平原区较复杂的环境条件及目前草坪建植中存在的引种盲目，失败率高，寿命短等问题，对引进的7个草种47个品种的生育期，抗逆性，坪用价值等进行了综合评价，结果表明，高羊茅，白三叶为最适合的草种，草地早熟禾和百脉根较适合，多年生黑麦草不适宜单播但可以与高羊茅和草地早熟禾以适当的比例混播，紫羊茅和红三叶不适宜在该区种植；最优良的品种高羊茅为皇后，贝克，白三叶为拉吉诺白三叶，威亚白三叶，草地早熟禾为亨特，普通，兰神，奖品，百脉根为哥吉亚1。     

云南大面积白三叶种子生产中杂草的多样性调查

通过对农业部白三叶种子生产项目在云南省马龙县马鸣乡大面积生产中杂草的多样性调查,结果表明:(1)在白三叶种子大面积生产中,随着白三叶的生长,田间杂草的种类和数量逐步增加;(2)由于在白三叶播种当年其生长速度不快,因而在其生长前期应适时除杂,以防止杂草的迅速生长。     

5个草地型草地牧草的生长速率和养分动态研究

在牧草生长季节(5~9月)对小哨牧场放牧演替形成的5个草地型草地的牧草进行生长速率、牧草产量及养分的逐月观测,生长速率测定显示,白三叶+东非狼尾草和东非狼尾草+非洲狗尾草两个草地型草地均出现两个生长峰值,分别在6月和9月,其余3个草地型草地只有一个生长高峰。干物质产量和牧草养分测定结果表明,白三叶+东非狼尾草和白三叶+非洲狗尾草+画眉草两种草地型草地表现最好,可以在气候环境条件类似的地区推广种植利用;白三叶+画眉草和白三叶+非洲狗尾草两种草地型草地表现优良,可以在草地改良中,根据当地实际情况选择性地加以种植;东非狼尾草+非洲狗尾草型草地表现较差,不提倡种植。     

骡鸭早期生长曲线的研究

骡鸭具有生长速度快、瘦肉率高、味道鲜美等特点，深受人们喜爱。骡鸭既适合集约大规模饲养，又适合农村传统分散放牧饲养，是目前生产中较好的一种肉用品种。Gompem非线性生长模型是当前使用较多的用以描述动物生长发育规律的生长曲线模型。该模型具有精确度高、拟合度好、动物生长发育描述准确等特点，因而在研究动物生长发育规律时广泛为人们采用。此次试验对骡鸭的早期生长发育进行了研究，并利用Compertz模型对骡鸭生长曲线进行了拟合。从而建立骡鸭生长模型，为骡鸭生产提供依据。     

云南大面积白三叶种子生产中杂草的多样性调查

通过对农业部白三叶种子生产项目在云南省马龙县马鸣乡大面积生产中杂草的多样性调查,结果表明(1)在白三叶种子大面积生产中,随着白三叶的生长,田间杂草的种类和数量逐步增加;(2)由于在白三叶播种当年其生长速度不快,因而在其生长前期应适时除杂,以防止杂草的迅速生长.     

5个草地型草地牧草的生长速率和养分动态研究

在牧草生长季节(5～9月)对小哨牧场放牧演替形成的5个草地型草地的牧草进行生长速率、牧草产量及养分的逐月观测,生长速率测定显示,白三叶+东非狼尾草和东非狼尾草+非洲狗尾草两个草地型草地均出现两个生长峰值,分别在6月和9月,其余3个草地型草地只有一个生长高峰.干物质产量和牧草养分测定结果表明,白三叶+东非狼尾草和白三叶+非洲狗尾草+画眉草两种草地型草地表现最好,可以在气候环境条件类似的地区推广种植利用;白三叶+画眉草和白三叶+非洲狗尾草两种草地型草地表现优良,可以在草地改良中,根据当地实际情况选择性地加以种植;东非狼尾草+非洲狗尾草型草地表现较差,不提倡种植.     

无性系植物种群的研究进展

就无性系植物种群的无性生、生理整合作用、生长格局、生长型和对环境适宜的生长可塑性反应、等级选择模型和生态对策等方面进行了综述论述。     

青藏高原东缘中国沙棘的克隆多样性及克隆结构

应用随机扩增多态性DNA标记(RAPD markers)对青藏高原东缘中国沙棘的克隆结构、克隆多样性进行了初步的探讨。结果表明,1) 14条RAPD引物在8个中国沙棘居群共184个样本中平均形成55.75种条带,平均有15.13种条带在居群内表现出多态性,平均多态条带百分率为27.64%;鉴别出26种基因型,平均每基株形成3.4个分株;2)与应用DNA分子标记研究的其他克隆繁殖能力较强的植物相比,中国沙棘的克隆多样性水平偏低,Simpson’s多样性指数(D)平均值为0.85;Fager’s均匀度指数(E)平均值为0.87;3)克隆结构分析表明,中国沙棘每克隆内分株间的平均距离为1.75 m,克隆繁殖的生长型主要为游击型,并且在居群内存在以某一优势基株为主的克隆分布方式;4) 居群的定居时间、生境及群落结构等对中国沙棘的克隆多样性有一定影响。     

Comparison on accuracy of different nonlinear models in predicting growth of Podolica bulls

Animal growth does not follow a linear pattern, being explained mathematically by functions that have parameters with biological meaning. These parameters are used to estimate the expected weight of animals at specific ages. Several nonlinear models have been used to describe growth. This study was carried out to estimate the parameters of logistic, Gompertz, Richards and von Bertalanffy growth curve models in a sample of Podolica young bulls to determine the goodness of fit. Animals were weighed every 3 months from birth to 810 days of age. The results indicate that all the growth models used were easily fitted to the observed data with Gompertz and logistic functions presenting less computational difficulty in terms of number of iterations to achieve convergence. Moreover, logistic and Richards equations provided the best overall fit being useful to describe the growth of Podolica bulls. Considering that the literature lacks information on growth curves in Podolica breed, the study of a mathematical model for growth describing the developmental pattern of a specific population within a peculiar environment is a useful tool to improve Podolica breed production.     

Nonlinear Mixed Effects Model for Swine Growth

A mixed effects model version of a common swine growth function is introduced. This version, in which the mature BW of each pig is considered random, accounts for increasing variation with age and serial correlations across age that are common in serial BW growth data sets, thereby providing a much better fit and significantly smaller parameter SE. The inclusion of random effects also reduces the impact of selective sampling, which occurs when fast-growing pigs are removed for marketing, and provides a statistic (i.e., variance estimate) that describes the between-pig variation within a group. Comparisons between the fixed and mixed effects models are made using swine growth data consisting of 93 pigs with biweekly BW data from 54 to 138 d of age. In addition to a much better fit, the mixed effects model version is easily adaptable to stochastic modeling because only an additional random effect (i.e., mature BW) for each pig needs to be generated.     

Use of matrix population models to estimate the efficacy of euthanasia versus trap-neuter-return for management of free-roaming cats

OBJECTIVE: To evaluate the efficacy of trap-neuter-return and trap-euthanatize management strategies for controlling urban free-roaming cat populations by use of matrix population models. DESIGN: Prospective study. SAMPLE POPULATION: Estimates of free-roaming cat populations in urban environments. PROCEDURE: Data from the literature describing the biology of free-roaming cat populations in urban environments were gathered. A matrix population model was developed with a range of high and low survival and fecundity values and all combinations of those values. The response of population growth rate to a range of management actions was assessed with an elasticity analysis. RESULTS: All possible combinations of survival and fecundity values of free-roaming cats led to predictions of rapid, exponential population growth. The model predicted effective cat population control by use of annual euthanasia of > or = 50% of the population or by annual neutering of > 75% of the fertile population. Elasticity analyses revealed that the modeled population was most susceptible to control through euthanasia. CONCLUSIONS AND CLINICAL RELEVANCE: Free-roaming cat populations have a high intrinsic growth rate, and euthanasia is estimated to be more effective at reducing cat populations than trap-neuter-return programs.     

Graphical approaches to support the analysis of linear-multilevel models of lamb pre-weaning growth in Kolda (Senegal)

Linear-multilevel models (LMM) are mixed-effects models in which several levels of grouping may be specified (village, herd, animal, …). This study highlighted the usefulness of graphical methods in their analysis through: (1) the choice of the fixed and random effects and their structure, (2) the assessment of goodness-of-fit and (3) distributional assumptions for random effects and residuals.

An LMM was developed to study the effect of ewe deworming with morantel on lamb pre-weaning growth in a field experiment involving 182 lambs in 45 herds and 10 villages in Kolda, Senegal. Growth was described as a quadratic polynomial of age. Other covariates were sex, litter-size and treatment. The choice of fixed and random effects relied on three graphs: (1) a trellis display of mean live-weight vs. age, to select main effects and interactions (fixed effects); (2) a trellis display of individual growth curves, to decide which growth-curve terms should be included as random effects and (3) a scatter plot of parameters of lamb-specific regressions (live-weight vs. quadratic polynomial of age) to choose the random-effects covariance structure.

Age, litter-size, age×litter-size, litter-size×treatment and age×litter-size×treatment were selected graphically as fixed effects and were significant (p<0.05) in subsequent statistical models. The selection of random-effect structures was guided by graphical assessment and comparison of the Akaike’s information criterion for different models. The final random-effects selected included no random effect at the village level but intercept, age and squared-age at the herd and lamb levels. The structure of the random-effects variance–covariance matrices were blocked-diagonal at the herd level and unstructured at the lamb level. An order-1 autoregressive structure was retained to account for serial correlations of residuals. Smaller residual variance at 90 days than at younger ages was modeled with a dummy variable taking a value of 1 at 90 days and 0 elsewhere.

Ewe-deworming with morantel during the rainy season lead to higher lamb live-weights (probably related to a better ewe-nutrition and -health status). A positive correlation was demonstrated between early weight and growth rate at the population level (with important lamb and herd-level random deviations). The persistence of this correlation at older ages should be checked to determine whether early weights are good predictors of mature weights and ewe-reproductive lifetime performance.     

Fitting the Gompertz function to dose-response data of larval tick populations

Samples of 6, 1st generation larval populations of Boophilus decoloratus, originating from field collected females, were subjected to increasing doses of the organophosphate acaricide, Dioxathion. The dose-response relationship for 3 populations showed random heterogeneity, while systematic deviations from the linear probit lines were observed for the other 3 populations. Logistic and Gompertz regressions were also fitted for all 6 populations. Probit regressions fitted best for 2 populations exhibiting heterogeneous responses. The logistic regression fitted best for 1 population with heterogeneous responses and 1 population with systematic deviating responses. The Gompertz regression fitted best for the 2 remaining populations exhibiting systematic deviating responses. The Gompertz function may be useful in describing the dose-response relationship obtained for certain acaricidal toxicity tests.     

高海拔地区隐性白羽肉鸡生长曲线的拟合与分析

试验测定了隐性白羽肉鸡在高海拔饲养环境中2～20周龄的体重和胫长,用logistic、Gompertz模型拟合了生长曲线。结果表明隐性白羽肉鸡体重和胫长生长受高海拔环境抑制,logistic和Gompertz模型都适宜拟合隐性白羽肉鸡在高海拔体重和胫长的生长过程,拟合度高,其中logistic模型优于Gompertz模型。隐性白羽公鸡比母鸡有较高的成年体重、拐点体重、绝对生长速度和相对生长速度。     

Evaluation of underlying risk as a source of heterogeneity in meta-analyses: a simulation study of Bayesian and frequentist implementations of three models

Evaluation of important causes of heterogeneity among study results is an important component of any meta-analysis. For factors which can be measured (e.g. population characteristics, indicators of study quality), standard methods such as meta-regression can be used for this evaluation. The underlying risk (i.e. risk of outcome in the control population) can be viewed as a summary of the effects of unmeasured population characteristics so it is a logical candidate for evaluation as a source of heterogeneity. Unfortunately, because of its relationship with the study outcome (odds ratio or relative risk), standard methods should not be used for evaluating underlying risk as a cause of heterogeneity. Three models with different sets of underlying assumptions were evaluated in a simulation study to determine how well they performed in assessing the role of underlying risk as a source of heterogeneity. All models were fit using both Bayesian and frequentist (maximum likelihood random slopes models) estimation procedures and the results compared. Two of the models produced good results (i.e. minimal evidence of bias in parameter estimates), while the third clearly produced biased estimates of some parameters. In general, the Bayesian and frequentist approaches produced similar results. In situations in which the number of studies in a meta-analysis is small ( approximately 10), the maximum likelihood (frequentist) approach was preferable. While the bias induced by heterogeneity associated with underlying risk was generally not large, use of one of the approaches described in this paper will produce better estimates of treatment effect in situations where there is substantial heterogeneity between studies. A model based on the assumption that the number of positive events in each of the treatment and control groups are binomially distributed (Model 1) is the recommended approach.     

Use of structured antedependence models for the genetic analysis of growth curves

Growth curve analysis is an important issue for many agricultural and laboratory species, for both phenotypic and genetic studies. The aim of this paper is to present the use of a novel statistical approach, namely the structured antedependence (SAD) models, to deal with this issue. The basic idea of these models is that an observation at time t can be explained by the previous observations. These models are especially appropriate to deal with cumulative traits such as growth, as BW at age t clearly depends on BW measures at ages (t -1), (t -2), etc. These models were applied on an INRA experimental Charolais herd data set. The data comprised BW records for 560 cows born over an 11-yr period (from 1988 to 1998) from 60 sires and 369 dams. The proposed SAD models were compared with the well-known random regression (RR) models that are already widely used in various areas of longitudinal data analysis. It was found that the SAD models fit the growth process better with far fewer parameters than the RR models (9 instead of 16 covariance parameters for the phenotypic analysis, and 14 instead of 21 for the genetic analysis). Despite this smaller number of covariance parameters, the likelihood value was found to be much higher with the SAD vs. the RR models, with a difference of 262.9 for the phenotypic analysis with a quartic polynomial for the RR and 751.5 for the genetic analysis with a cubic polynomial for both the genetic and environmental parts of the RR model. The SAD models also proved to be better able to interpolate missing values. Heritability, genetic, and environmental correlation coefficients were estimated for weights from birth to adulthood. The structured antedependence models proved, in this study, to be very appropriate to model growth data in a parsimonious and flexible way.     

Challenges in research on rhizomatous clonal plants北大核心CSCD

根茎克隆植物是克隆植物的一个类型,地下系统的特殊性赋予其独特的繁殖和生长方式,也赋予其对环境的特有适应能力。但是,受研究手段限制,人们对根茎克隆植物在表型可塑性、克隆整合、觅食行为和克隆分工等方面与其他类型克隆植物有何异同,这些行为对预防植物入侵、应对气候变化、进行退化生态系统恢复有何特殊意义尚不很清晰。鉴于此,通过梳理和总结前人工作,提出了根茎克隆植物研究面临的挑战。根茎克隆植物下一步研究重点应体现在下述方面:1)可塑性性状的选择与测定方法;2)环境因子对克隆整合的影响机制;3)综合植物特征和环境特征的觅食模型。