一种建模方法,用于预测元人口中的当地人口趋势
Thierry Chambert1, Christophe Barbraud2, Emmanuelle Cam3
1CEFE, Univ Montpellier, CNRS, EPHE-PSL University, IRD, Montpellier, France.
Ecology
|November 4, 2024
概括
预测动物种群动态是至关重要的. 一个新的双尺度模型通过考虑超级人口增长来准确预测子人口轨迹,在不需要分散数据的情况下优于经典模型.
科学领域:
- 生态生态学 生态生态学
- 人口动态 人口动态
- 保护生物学 保护生物学
背景情况:
- 经典的人口学模型假设封闭的人口,导致对超群体内相互连接的子群体的不准确预测.
- 在野生种群中估计分散率是具有挑战性的,阻碍了分种群连接的明确建模.
- 准确预测当地人口轨迹对于有效的野生动物管理和保护至关重要.
研究的目的:
- 开发和评估一种新型的人口学模型 (二级模型),用于预测连接的子群体中的当地人口轨迹.
- 用模拟和现实世界的数据来比较两级模型与经典的封闭人口模型的性能.
- 在缺乏分散数据的情况下,为预测人口动态提供更准确,更节的工具.
主要方法:
- 开发了一种两级人口模型,使用比例参数将超人口动态 (大规模) 与亚人口再分配 (小规模) 分开.
- 进行了现实的模拟,以比较两级模型的预测准确度和偏差与30年的经典封闭人口模型.
- 将这两种模型应用于欧洲 (Gulosus aristotelis) 的真实数据集,以评估它们在经验数据上的表现.
主要成果:
- 两级模型在模拟中显示出明显较低的偏差 (<3%) 和预测错误 (<20%) 与经典模型 (30%偏差,>500%错误) 相比.
- 应用到欧洲的Shag数据显示,二级模型预测了现实的人口增长 (高达三倍),而经典模型预测了不现实的增长 (高达200倍).
- 两级模型有效地通过总体的超级人口增长率来限制当地亚人口的增长,避免不切实际的轨迹.
结论:
- 双尺度模型提供了一个有效的解决方案,用于预测连接子群体的本地人口结构,而不需要分散率数据.
- 它提供了一个更准确,更节的替代方案,以经典的封闭型人口模型和完全分散型模型为人口生存分析.
- 该模型依赖于简单的计数数据,使其高度适用于大型野生动物监测计划.
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