将声学调查和公民科学数据结合起来,可以为热带雨林鸟类提供增强的物种分布模型
Reid Rumelt1, Carla Mere Roncal2, Arianna Basto2
1Department of Biology, University of Miami, Coral Gables, Florida, United States of America.
PloS one
|July 8, 2025
概括
结合声学监测和公民科学数据,改善了亚马逊秘密鸟类的物种分布模型. 这种混合方法增强了对热带物种的生态理解和保护工作.
科学领域:
- 生态生态学 生态生态学
- 保护生物学 保护生物学
- 生物声学是一种生物声学.
背景情况:
- 物种分布模型 (SDM) 对于生态研究和保护至关重要.
- 结合结构化和非结构化数据源可以提高SDM的准确性,但这在热带地区的探索较少.
- 传统方法在生物多样性的热带地区经常与数据限制作斗争.
研究的目的:
- 适应数据聚合方法,以创建准确的物种分布模型,以隐藏亚马逊西南部的潜海鸟类.
- 用声学监测和公民科学数据评估混合建模方法的性能.
- 为热带物种分布建模提供灵活和可重复的方法.
主要方法:
- 集成的自动声学监控检测与eBird公民科学数据.
- 利用高分辨率的土地覆盖数据集来捕捉关键的生态梯度.
- 开发了对分泌性下层鸟类的聚合物种分布模型.
主要成果:
- 与仅使用eBird数据预测完整森林中细粒度息地反应的模型相比,聚合模型表现优越.
- 混合方法成功地结合了公民科学数据,捕捉了非植被和人类扰乱地区的发生模式.
- 这些模型提供了对受息地梯度影响的物种分布的宝贵见解.
结论:
- 结合声学和公民科学数据的混合方法为热带地区物种分布建模提供了强大,灵活和可重复的方法.
- 这种方法克服了单一数据源方法的局限性,特别是对于隐秘的物种.
- 这些发现支持加强对亚马逊生物多样性的保护战略.
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