Hakoyama Hiroshi, Iwasa Yoh
National Research Institute of Fisheries Science, Yokohama 236-8648, Japan.
J Theor Biol. 2005 Jan 21;232(2):203-16. doi: 10.1016/j.jtbi.2004.08.008.
Aggregation of variables of a complex mathematical model with realistic structure gives a simplified model which is more suitable than the original one when the amount of data for parameter estimation is limited. Here we explore use of a formula derived for a single unstructured population (canonical model) in predicting the extinction time for a population living in multiple habitats. In particular we focus multiple populations each following logistic growth with demographic and environmental stochasticities, and examine how the mean extinction time depends on the migration and environmental correlation. When migration rate and/or environmental correlation are very large or very small, we may express the mean extinction time exactly using the formula with properly modified parameters. When parameters are of intermediate magnitude, we generate a Monte Carlo time series of the population size for the realistic structured model, estimate the "effective parameters" by fitting the time series to the canonical model, and then calculate the mean extinction time using the formula for a single population. The mean extinction time predicted by the formula was close to those obtained from direct computer simulation of structured models. We conclude that the formula for an unstructured single-population model has good approximation capability and can be applicable in estimating the extinction risk of the structured meta-population model for a limited data set.
对具有现实结构的复杂数学模型的变量进行聚合,会得到一个简化模型。当用于参数估计的数据量有限时,该简化模型比原始模型更适用。在此,我们探讨使用为单一非结构化种群(典型模型)推导的公式来预测生活在多个栖息地的种群的灭绝时间。特别地,我们关注多个遵循逻辑斯谛增长且具有人口统计学和环境随机性的种群,并研究平均灭绝时间如何依赖于迁移和环境相关性。当迁移率和/或环境相关性非常大或非常小时,我们可以使用经过适当修改参数的公式精确地表示平均灭绝时间。当参数处于中等大小时,我们为现实的结构化模型生成种群大小的蒙特卡罗时间序列,通过将时间序列拟合到典型模型来估计“有效参数”,然后使用单一种群的公式计算平均灭绝时间。该公式预测的平均灭绝时间与从结构化模型的直接计算机模拟获得的结果相近。我们得出结论,非结构化单一种群模型的公式具有良好的近似能力,并且可适用于在数据集有限的情况下估计结构化集合种群模型的灭绝风险。