混合模型方法可以利用数据库信息来改善鱼群中尺寸调整的污染物度的估计
Emily Smenderovac1, Brian W Kielstra2, Calvin Kluke3
1Great Lakes Forestry Centre, Natural Resources Canada, Sault Ste. Marie P6A 2E5, Canada.
Environmental science & technology
|March 5, 2025
概括
鱼类中污染物度的大小标准化对于准确的研究至关重要. 混合效应模型 (MEMs) 为采样事件回归 (SERs) 提供了一个更有效的替代方案,特别是对于小样本大小,INLA表现最好.
科学领域:
- 环境科学 环境科学
- 生态毒理学 生态毒理学
- 渔业 科学 渔业 科学
背景情况:
- 鱼类中的生物累积污染物度与大小和年龄相关,使研究和监测复杂化.
- 尺寸标准化是解决污染物数据中尺寸共变的常见做法.
- 采样事件回归 (SER) 经常用于大小标准化,但需要大样本大小.
研究的目的:
- 用三种混合效应模型 (MEM) 对鱼类中污染物度尺寸标准化的SERs的有效性进行比较.
- 评估MEM,包括限制最大概率,贝叶斯推理 (MCMC) 和近似贝叶斯推理 (INLA),用于污染物分析.
- 确定最适合的MEM方法用于渔业污染物监测,特别是在样本大小限制下.
主要方法:
- 与使用 (Hg) 和 (As) 数据的三种MEM方法 (REML,MCMC,INLA) 的SER比较.
- 基于剩余和根平均平方误差的模型性能评估.
- 在各种样本大小场景中评估计算强度和性能.
主要成果:
- 对于小人群或缺乏大小范围的SERs,MEM方法成功生成了尺寸标准化的污染物度.
- MEMs显示了与SER估计的可比的余值和根平均平方误差.
- 在大多数场景中,INLA表现一致,在计算上不那么密集,被认为是最好的方法.
结论:
- 混合效应模型提供了一种可行且往往优于SERs的替代方案,用于大小标准化的鱼类污染物数据,特别是在有限的样本大小的情况下.
- 建议使用INLA方法,因为它在渔业污染物研究和监测中具有高效和强大的性能.
- 提供了示例R-INLA代码,以促进这种方法在实际应用中的采用.
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