社会结构人口的人口统计过程

Maria Paniw, G. Cozzi, S. Sommer, A. Ozgul
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引用次数: 2

摘要

在社会结构的动物种群中,生存和繁殖等重要比率受到不同社会等级的个体和社会群体之间复杂的相互作用的影响。由于这种复杂性,机械方法来模拟生命率可能比常用的结构化人口模型更可取。然而,机械方法的代价是增加建模复杂性、计算需求和对模拟指标的依赖,而结构化人口模型在分析上是可处理的。本章比较了不同的方法来模拟社会结构人口的人口动态。首先基于假设的合作繁殖者的生命周期模拟基于个体的数据,然后使用矩阵种群模型(MPM)、积分投影模型(IPM)和基于个体的模型(IBM)预测种群动态。作者证明,在预测人口规模或结构时,相对简单的MPM可以优于IPM和IBM。然而,需要在更复杂的IBM中参数化的机械细节来准确地规划社会群体中的交互。本章中的R脚本提供了一个路线图,既可以模拟最能描述社会结构系统的数据,又可以评估捕获系统动态所需的模型复杂性水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Demographic processes in socially structured populations
In socially structured animal populations, vital rates such as survival and reproduction, are affected by complex interactions among individuals of different social ranks and among social groups. Due to this complexity, mechanistic approaches to model vital rates may be preferred over commonly used structured population models. However, mechanistic approaches come at a cost of increased modelling complexity, computational requirements, and reliance on simulated metrics, while structured population models are analytically tractable. This chapter compares different approaches to modelling population dynamics of socially structured populations. It first simulates individual-based data based on the life cycle of a hypothetical cooperative breeder and then projects population dynamics using a matrix population model (MPM), an integral projection model (IPM), and an individual-based model (IBM). The authors demonstrate that, when projecting population size or structure, the relatively simpler MPM can outperform both the IPM and IBM. However, mechanistic details parametrised in the more complex IBM are required to accurately project interactions within social groups. The R scripts in this chapter provide a roadmap to both simulate data that best describe a socially structured system and assess the level of model complexity needed to capture the dynamics of the system.
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