Am Nat. 2023 Apr;201(4):557-573. doi: 10.1086/723211. Epub 2023 Feb 3.
AbstractA method for analyzing long-term demographic data on density-dependent stage-structured populations in a stochastic environment is derived to facilitate comparison of populations and species with different life histories. We assume that a weighted sum of stage abundances, , exerts density dependence on stage-specific vital rates of survival and reproduction and that has a small or moderate coefficient of variation. The dynamics of are approximated as a univariate stochastic process governed by three key parameters: the density-independent growth rate, the net density dependence, and environmental variance in the life history. We show how to estimate the relative weighs of stages in and the key parameters. Life history evolution represents a stochastic maximization of a simple function of the key parameters. The long-term selection gradient on the life history can be expressed as a vector of sensitivities of this function with respect to density-independent, density-dependent, and stochastic components of the vital rates. To illustrate the method, we analyze 38 years of demographic data on a great tit population, estimating the key parameters, which accurately predict the observed mean, coefficient of variation, and fluctuation rate of ; we also evaluate the long-term selection gradient on the life history.
摘要本文提出了一种分析随机环境下具有密度依赖阶段结构种群的长期人口统计数据的方法，以方便比较具有不同生活史的种群和物种。我们假设，阶段丰度的加权和对生存和繁殖的特定阶段的重要率具有密度依赖性，并且具有小或中等的变异系数。的动态被近似为一个由三个关键参数控制的单变量随机过程：密度独立增长率、净密度依赖性和生命史中的环境方差。我们展示了如何估计和中的阶段相对权重和关键参数。生活史进化代表了对关键参数的简单函数的随机最大化。生命史上的长期选择梯度可以表示为该函数相对于重要率的密度独立、密度依赖和随机分量的敏感性向量。为了说明该方法，我们分析了一个大山雀种群的 38 年人口统计数据，估计了关键参数，这些参数准确地预测了的观察平均值、变异系数和波动率；我们还评估了生命史上的长期选择梯度。
