在生态学中,用于混合动力系统的弱形式推理
Daniel Messenger1, Greg Dwyer2, Vanja Dukic1
1Department of Applied Mathematics, University of Colorado, Boulder, CO, USA.
Journal of the Royal Society, Interface
|December 17, 2024
概括
这项研究引入了一种新的数据驱动方法来建模人口动态,包括短期的连续变化和长期的离散变化. 它有助于通过理解多层次生态效应来预测人口繁荣和衰退.
科学领域:
- 生态建模 生态建模
- 人口动态 人口动态
- 数学生物学的数学生物学
背景情况:
- 由于掠食和环境因素,物种表现出种群波动.
- 预测这些人口的繁荣和衰退是具有挑战性的,因为复杂的,多个规模的动态.
研究的目的:
- 开发一种数据驱动的方法来提取人口动态的混合治理方程.
- 为了估计结合的短期连续和长期离散人口模型的参数.
- 评估生态系统中不同时间尺度之间的相互依赖.
主要方法:
- 使用弱形式方程学习来提取混合动态系统.
- 将短期的连续动态与长期的离散更新结合起来.
- 使用稀疏,间歇测量人口变量的参数估计.
主要成果:
- 成功提取了由长期变量参数化的短期连续动态系统方程.
- 由短期变量参数化的衍生长期离散方程.
- 在各种生态场景中证明了该方法的实用性,包括北美海绵病.
结论:
- 开发的方法有效地模拟了多个规模的人口动态.
- 它允许直接评估短期和长期生态因素之间的相互依存关系.
- 提供了一个可靠的框架,用于预测在可变环境下的人口繁荣和衰退.
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