将生物多样性数据中的差距和偏见视为缺失数据问题
Diana E Bowler1, Robin J Boyd1, Corey T Callaghan2
1UK Centre for Ecology & Hydrology, Maclean Building, Benson Lane, Wallingford, OX10 8BB, UK.
Biological reviews of the Cambridge Philosophical Society
|August 8, 2024
概括
生物多样性数据缺口可能会影响物种趋势分析. 解决这些差距需要了解导致缺失数据的因素,加权方法显示有望减少生态监测中的偏见和不确定性.
科学领域:
- 生态生态学 生态生态学
- 生物多样性科学 生物多样性科学
- 数据科学数据科学数据科学
背景情况:
- 大量的生物多样性数据集对于监测物种种群和随时间的分布至关重要.
- 空间和时间数据缺口可能会限制生物多样性数据的代表性,阻碍大规模推断和保护工作.
研究的目的:
- 将生物多样性数据缺口概念化为缺失数据问题,为分析提供统一的框架.
- 探索数据缺口对推断物种趋势和影响物种发生和丰度的因素的影响.
主要方法:
- 将数据缺口描述为不同类别的缺失数据.
- 应用缺失数据理论来分析物种趋势模型中的偏差.
- 审查经验研究和进行模拟研究,以比较处理数据差距的方法 (亚样本,权重,归算).
主要成果:
- 当影响数据采样/可用性的因素与影响物种的因素重叠时,可能会出现偏见.
- 标准的物种趋势模型往往无法解释导致数据缺失的因素,从而增加了对偏差的敏感性.
- 亚样本,权重和归算可以减少偏差,但可能会增加不确定性;权重方法显示了减少偏差和差异的潜力.
结论:
- 有效的偏差减少关键取决于对导致数据差距的因素的理解和数据.
- 权重技术在生态学中未得到充分利用,但为改善生物多样性数据分析提供了巨大的潜力.
- 必须在数据收集和分析工作流程中整合数据缺口的考虑,以便进行可靠的生态监测.
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