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Asymptotic Distribution of Density-Dependent Stage-Grouped Population Dynamics Models
Acta Biotheoretica ( IF 1.3 ) Pub Date : 2008-03-29 , DOI: 10.1007/s10441-008-9034-0
Mélanie Zetlaoui 1 , Nicolas Picard , Avner Bar-Hen
Affiliation  

Matrix models are widely used in biology to predict the temporal evolution of stage-structured populations. One issue related to matrix models that is often disregarded is the sampling variability. As the sample used to estimate the vital rates of the models are of finite size, a sampling error is attached to parameter estimation, which has in turn repercussions on all the predictions of the model. In this study, we address the question of building confidence bounds around the predictions of matrix models due to sampling variability. We focus on a density-dependent Usher model, the maximum likelihood estimator of parameters, and the predicted stationary stage vector. The asymptotic distribution of the stationary stage vector is specified, assuming that the parameters of the model remain in a set of the parameter space where the model admits one unique equilibrium point. Tests for density-dependence are also incidentally provided. The model is applied to a tropical rain forest in French Guiana.

中文翻译:

密度相关阶段分组人口动力学模型的渐近分布

矩阵模型在生物学中被广泛用于预测阶段结构种群的时间演化。与矩阵模型相关的一个经常被忽视的问题是抽样变异性。由于用于估计模型生命率的样本大小有限,因此参数估计会附加抽样误差,这反过来又会影响模型的所有预测。在这项研究中,我们解决了由于采样可变性而围绕矩阵模型的预测建立置信界限的问题。我们专注于密度相关的 Usher 模型、参数的最大似然估计量和预测的平稳阶段向量。指定平稳阶段向量的渐近分布,假设模型的参数保留在模型允许一个唯一平衡点的一组参数空间中。还附带提供了密度依赖性测试。该模型应用于法属圭亚那的热带雨林。
更新日期:2008-03-29
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