用贝叶斯统计数据进行国家监测:朝着更知情的决策
Katherine Cure1, Diego R Barneche2,3, Martial Depczynski2,3
1Australian Institute of Marine Science, Indian Ocean Marine Research Centre, The University of Western Australia (MO96), Entrance 4, Fairway, Crawley, WA, 6009, Australia. k.cure@aims.gov.au.
Ambio
|February 14, 2024
概括
在海洋监测中结合传统生态知识和西方科学,改善了沿海管理. 将不确定性估计纳入数据分析揭示了局限性,并加强了对生态变化检测的决策.
科学领域:
- 海洋生态海洋生态学
- 环境科学环境科学
- 保护生物学 保护生物学
背景情况:
- 全球伙伴关系正在将传统生态知识 (TEK) 与西方科学融合为海洋监测.
- 关于监测限制的有效沟通对于将发现纳入沿海管理决策至关重要.
研究的目的:
- 展示贝叶斯模型中的不确定性如何被整合到海洋监测管理指标中.
- 评估纳入可信度估计对理解生态变化和管理绩效的影响.
主要方法:
- 在澳大利亚西北部共同开发了一项为期5年的鱼类监测案例研究.
- 应用贝叶斯模型来估计监测数据中的不确定性.
- 模拟方法将不确定性转化为健康绩效指标.
主要成果:
- 在单个监测年内识别直接的生态变化时,发现了很高的不确定性.
- 纳入可信度估计提供了大量关于监测趋势的额外信息.
- 该研究强调了为管理绩效指标选择适当的评估方法的重要性.
结论:
- 整合不确定性估计提高了海洋监测数据的解释.
- 通过考虑可信度估计,可以更深入地了解监控限制.
- 精心选择管理绩效指标对于有效的沿海管理和决策至关重要.
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