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首页> 外文期刊>Animal Biodiversity and Conservation >Modeling nest-survival datas a comparison of recently developed methods that can be implemented in MARK and SAS
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Modeling nest-survival datas a comparison of recently developed methods that can be implemented in MARK and SAS

机译:对巢生存数据进行建模,比较可以在MARK和SAS中实现的最新开发方法

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Estimating nest success and evaluating factors potentially related to the survival rates of nests are key aspects of many studies of avian populations. A strong interest in nest success has led to a rich literature detailing a variety of estimation methods for this vital rate. In recent years, modeling approaches have undergone especially rapid development. Despite these advances, most researchers still employ Mayfield's ad-hoc method (Mayfield, 1961) or, in some cases, the maximum-likelihood estimator of Johnson (1979) and Bart & Robson (1982) Such methods permit analyses of stratified data but do not allow for more complex and realistic models of nest survival rate that include covariates that vary by individual, nest age, time, etc. and that maybe continuous or categorical. Methods that allow researchers to rigorously assess the importance of a variety of biological factors that might affect nest survival rates can now be readily implemented in Program MARK and in SAS's Proc GENMOD and Proc NLMIXED. Accordingly, use of Mayfield's estimator without first evaluating the need for more complex models of nest survival rate cannot be justified. With the goal of increasing the use of more flexible methods, we first describe the likelihood used for these models and then consider the question of what the effective sample size is for computation of AlCc. Next, we consider the advantages and disadvantages of these different programs in terms of ease of data input and model construction; utility/flexibility of generated estimates and predictions; ease of model selection; and ability to estimate variance components. An example data set is then analyzed using both MARK and SAS to demonstrate implementation of the methods with various models that contain nest-, group- (or block-), and time-specific covariates. Finally, we discuss improvements that would, if they became available, promote a better general understanding of nest survival rates.
机译:估计巢的成功率和评估可能与巢的存活率相关的因素是许多鸟类种群研究的关键方面。对巢穴成功的强烈兴趣催生了丰富的文献,详细介绍了该生命率的各种估算方法。近年来,建模方法得到了特别迅速的发展。尽管取得了这些进步,但大多数研究人员仍采用Mayfield的即席方法(Mayfield,1961),或者在某些情况下,采用Johnson(1979)和Bart&Robson(1982)的最大似然估计器。这些方法可以对分层数据进行分析,但是可以不允许使用更复杂,更实际的巢生存率模型,其中包括随个体,巢龄,时间等而变化的协变量,这些变量可能是连续的或分类的。现在,可以在Program MARK以及SAS的Proc GENMOD和Proc NLMIXED中轻松实施允许研究人员严格评估可能影响巢生存率的多种生物学因素的方法。因此,在没有先评估需要更复杂的巢生存率模型的情况下使用Mayfield估计器是不合理的。为了增加使用更灵活的方法的目标,我们首先描述用于这些模型的可能性,然后考虑有效的样本量对于计算AlCc的问题。接下来,我们从数据输入的简便性和模型构建的角度考虑了这些不同程序的优缺点。所产生的估计和预测的效用/灵活性;易于模型选择;以及估算方差成分的能力。然后使用MARK和SAS对示例数据集进行分析,以证明该方法在包含嵌套,组(或块)和特定时间协变量的各种模型中的实现。最后,我们讨论了改进措施,如果有这些改进措施,将有助于人们更好地了解巢穴存活率。

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