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首页> 外文期刊>Ecology letters >The importance of individual developmental variation in stage-structured population models
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The importance of individual developmental variation in stage-structured population models

机译：阶段结构人口模型中个体发展差异的重要性

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摘要

Population stage structure is fundamental to ecology, and models of this structure have proven useful in many different systems. Many ecological variables other than stage, such as habitat type, site occupancy and metapopulation status are also modelled using transitions among discrete states. Transitions among life stages can be characterised by the distribution of time spent in each stage, including the mean and variance of each stage duration and within-individual correlations among multiple stage durations. Three modelling traditions represent stage durations differently. Matrix models can be derived as a long-run approximation from any distribution of stage durations, but they are often interpreted directly as a Markov model for stage transitions. Statistical stage-duration distribution models accommodate the variation typical of cohort development data, but such realism has rarely been incorporated in population theory or statistical population models. Delay-differential equation models include lags but no variation, except in limited cases. We synthesise these models in one framework and illustrate how individual variation and correlations in development can impact population growth. Furthermore, different development models can yield the same long-term matrix transition rates but different sensitivities and elasticities. Finally, we discuss future directions for estimating realistic stage duration models from data.
机译：人口阶段结构是生态学的基础，这种结构的模型已在许多不同的系统中被证明是有用的。除阶段外，许多生态变量，例如栖息地类型，场地占用和人口迁移状态，也使用离散状态之间的转换来建模。生命阶段之间的过渡可以通过每个阶段花费的时间分布来表征，包括每个阶段持续时间的均值和方差以及多个阶段持续时间之间的个体内相关性。三种建模传统表示阶段持续时间的方式有所不同。矩阵模型可以从阶段持续时间的任何分布中得出，作为长期近似值，但是对于阶段转换，它们通常直接解释为马尔可夫模型。统计阶段持续时间分布模型适应了队列开发数据的典型变化，但是这种现实主义很少被纳入人口理论或统计人口模型中。时滞-微分方程模型包括滞后，但没有变化，除了少数情况。我们在一个框架中综合了这些模型，并说明了发展中的个体差异和相关性如何影响人口增长。此外，不同的开发模型可以产生相同的长期基质转化率，但具有不同的敏感性和弹性。最后，我们讨论了从数据估计现实阶段持续时间模型的未来方向。

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