重新评估法国繁殖鸟种群大小,使用公民科学和对物种检测能力的会计
Jean Nabias1,2, Luc Barbaro1,3, Benoît Fontaine1,4
1Muséum National d'Histoire Naturelle, Centre d'Ecologie et des Sciences de la Conservation, Paris, France.
PeerJ
|September 2, 2024
概括
公民科学 (CS) 有效地监测普通鸟类种群,通过计算可检测性来提高对专家估计的准确性. 结构化CS数据和层次距离采样提供了可靠的种群大小评估,用于保护.
科学领域:
- 生态与保护生物学 生态与保护生物学
- 鸟类学 鸟类学是一门学科.
- 公民科学 数据分析 数据分析
背景情况:
- 准确的物种种群规模估计对于有效的生物多样性保护至关重要,特别是对于常见和广泛的物种,这些物种在监测工作中经常被忽视.
- 公民科学 (CS) 为长期监测常见物种提供了强大的,可扩展的解决方案,克服了传统方法的局限性.
- 在CS数据中的可检测性问题可能会导致人口估计偏差;需要重复观察的结构化方案来建模和纠正这一点.
研究的目的:
- 用结构化公民科学数据和层次距离采样 (HDS) 来估计法国63种常见鸟类的种群规模.
- 将HDS衍生的人口估计与以前基于专家的地图估计进行比较,评估可检测性对先前评估的影响.
- 倡导在长期生物多样性监测中整合结构化的公民科学和强大的统计方法.
主要方法:
- 使用了法国大型结构化公民科学数据集 (EPOC-ODF),其中包含来自3,873个地点的27,156个检查清单 (2021-2023繁殖季).
- 应用分层距离采样 (HDS) 来建模检测概率和估计63种常见鸟类种群规模.
- 将HDS估计与以往基于专家的法国繁殖鸟类图谱估计进行了比较.
主要成果:
- 层次距离采样 (HDS) 对65%的研究鸟类种类产生了比以前基于专家的地图估计更高的种群大小估计.
- 这种差异归因于基于专家的评估,由于未经纠正的检测性问题和保守的估计,可能低估了人口.
- 与HDS模型相比,具有远距离歌曲的物种 (例如,常见,欧亚黄,欧亚黑鸟) 显示了较高的先前地图集估计,这表明了特定的偏差.
结论:
- 结构化公民科学,当用像HDS这样的健全统计方法分析时,为常见物种提供更可靠的种群规模估计.
- 以往基于专家的估计可能低估了人口规模,因为尚未解决可检测性挑战.
- 越来越多地依赖结构化的公民科学对于准确的,长期的生物多样性监测和明智的保护战略至关重要.
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