周期性波动环境中结构化种群的生态进化动力学:一种 G 函数方法。

IF 1.3 4区 生物学 Q3 BIOLOGY
Anuraag Bukkuri
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引用次数: 0

摘要

了解种群的生态和进化动态对于各种生物领域的基础和应用都至关重要。尽管目前已经开发了多个模拟生态进化动态的建模框架,但涉及如何模拟结构化种群的建模框架却少之又少。在之前的一篇论文中,我们首次提出了在 G 函数建模框架下模拟结构种群生态进化动态的建模方法。然而,这种方法无法在波动的环境条件下进行精确模拟。为了解决这一局限性,我们借鉴了周期微分方程的研究,提出了一种改进的方法,即使用更适合波动环境的不同适存度定义。我们用一个简单的生命史权衡玩具模型来说明这种方法。这种方法的通用性使其可用于各种生物环境。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Eco-evolutionary dynamics of structured populations in periodically fluctuating environments: a G function approach.

Eco-evolutionary dynamics of structured populations in periodically fluctuating environments: a G function approach.

Understanding the ecological and evolutionary dynamics of populations is critical for both basic and applied purposes in a variety of biological contexts. Although several modeling frameworks have been developed to simulate eco-evolutionary dynamics, many fewer address how to model structured populations. In a prior paper, we put forth the first modeling approach to simulate eco-evolutionary dynamics in structured populations under the G function modeling framework. However, this approach does not allow for accurate simulation under fluctuating environmental conditions. To address this limitation, we draw on the study of periodic differential equations to propose a modified approach that uses a different definition of fitness more suitable for fluctuating environments. We illustrate this method with a simple toy model of life history trade-offs. The generality of this approach allows it to be used in a variety of biological contexts.

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来源期刊
Theory in Biosciences
Theory in Biosciences 生物-生物学
CiteScore
2.70
自引率
9.10%
发文量
21
审稿时长
3 months
期刊介绍: Theory in Biosciences focuses on new concepts in theoretical biology. It also includes analytical and modelling approaches as well as philosophical and historical issues. Central topics are: Artificial Life; Bioinformatics with a focus on novel methods, phenomena, and interpretations; Bioinspired Modeling; Complexity, Robustness, and Resilience; Embodied Cognition; Evolutionary Biology; Evo-Devo; Game Theoretic Modeling; Genetics; History of Biology; Language Evolution; Mathematical Biology; Origin of Life; Philosophy of Biology; Population Biology; Systems Biology; Theoretical Ecology; Theoretical Molecular Biology; Theoretical Neuroscience & Cognition.
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