结合多个数据源,在人口动态的状态空间模型中具有不同偏差的多个数据源
Leo Polansky1, Lara Mitchell2, Ken B Newman3,4
1U.S. Fish and Wildlife Service Sacramento California USA.
Ecology and evolution
|June 12, 2023
概括
在动物种群模型中考虑未知的观察偏差至关重要. 状态空间模型 (SSM) 可以解决这些偏差,改善对人口动态的推断准确度,特别是当使用多个数据集时.
科学领域:
- 生态生态学 生态生态学
- 人口动态 人口动态
- 统计建模 统计建模
背景情况:
- 准确的动物种群建模需要高分辨率的数据,通常来自多个生命阶段,使季节动态描述成为可能.
- 在模型中使用的丰度估计可能会遭受随机和系统错误,特别是未知的观察偏差.
- 状态空间模型 (SSM) 提供了一个框架来区分过程变化和观察误差,允许在数据集中包含不同的偏差.
研究的目的:
- 调查将未知偏差参数纳入或排除在顺序生命阶段人口动态SSMs中的后果.
- 评估偏差参数对人口流程如招募和生存的推断的影响.
- 探索解决参数冗余和在存在偏差时表征过程不确定性的策略.
主要方法:
- 使用了一种顺序生命阶段人口动态状态空间模型 (SSM).
- 采用理论分析,模拟实验和经验案例研究的组合.
- 与有偏差参数和没有偏差参数的模型性能进行比较,包括具有固定偏差参数的场景.
主要成果:
- 在无偏见的数据中排除偏差参数会导致更高的精度.
- 当数据有偏差,偏差没有估计时,招募,生存和过程方差估计是不准确的.
- 包括偏差参数大大减少了估计问题,即使一个参数被错误固定.
- 带有偏差参数的模型可能会表现出参数冗余,从而带来推断挑战.
结论:
- 通过偏差参数组合多个数据集进行重新缩放,可以显著提高人口模型推断和诊断.
- 需要仔细考虑和策略来管理由偏差参数引起的过程不确定性.
- 估计偏差参数是特定于数据集的,可能需要比在生态数据中通常可用的更高的精度.
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