Fadai Nabil T, Johnston Stuart T, Simpson Matthew J
School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, UK.
Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Parkville, Victoria 3010, Australia.
Proc Math Phys Eng Sci. 2020 Sep;476(2241):20200350. doi: 10.1098/rspa.2020.0350. Epub 2020 Sep 16.
We present a solid theoretical foundation for interpreting the origin of Allee effects by providing the missing link in understanding how local individual-based mechanisms translate to global population dynamics. Allee effects were originally proposed to describe population dynamics that cannot be explained by exponential and logistic growth models. However, standard methods often calibrate Allee effect models to match observed global population dynamics without providing any mechanistic insight. By introducing a stochastic individual-based model, with proliferation, death and motility rates that depend on local density, we present a modelling framework that translates particular global Allee effects to specific individual-based mechanisms. Using data from ecology and cell biology, we unpack individual-level mechanisms implicit in an Allee effect model and provide simulation tools for others to repeat this analysis.
我们通过提供理解局部个体机制如何转化为全球种群动态过程中缺失的环节,为解释阿利效应的起源奠定了坚实的理论基础。阿利效应最初是为描述无法用指数增长和逻辑斯谛增长模型解释的种群动态而提出的。然而,标准方法通常校准阿利效应模型以匹配观测到的全球种群动态,却未提供任何机制性见解。通过引入一个基于个体的随机模型,其增殖、死亡和迁移率取决于局部密度,我们提出了一个建模框架,将特定的全球阿利效应转化为特定的基于个体的机制。利用来自生态学和细胞生物学的数据,我们剖析了阿利效应模型中隐含的个体水平机制,并为其他人提供了重复此分析的模拟工具。