利用大量机会主义收集的数据集来研究物种社区在空间和时间
Maxime Fajgenblat1,2,3,4, Robby Wijns1, Geert De Knijf5
1Laboratory of Freshwater Ecology, Evolution and Conservation, KU Leuven, Leuven, Belgium.
Ecology letters
|March 14, 2025
概括
这项研究引入了一种新的统计模型来分析自然学家收集的生物多样性数据. 该模型克服了检测方面的偏见,提供了对生态社区及其动态的高分辨率洞察力.
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
- 统计建模 统计建模
背景情况:
- 在线门户网站使自然学家能够广泛收集生物多样性数据.
- 机会主义数据对生态推理提出了挑战,原因是检测偏差.
- 现有的方法很难充分利用异质的,广泛可用的生物多样性数据集.
研究的目的:
- 开发一种新的统计方法来分析机会性生物多样性数据.
- 在生态数据集中克服可变检测概率和观察者行为的局限性.
- 从大规模的自然学家收集的数据提供对生物群落的高分辨率洞察力.
主要方法:
- 在场地占用框架内嵌入一个时空空间联合物种分布模型.
- 建模现象学模式,以解决不同访问和物种的可变检测概率.
- 利用潜变量来描述观察者特定的检测和报告行为.
主要成果:
- 该模型成功地分析了弗兰德斯超过10万个机会性记录的眼镜.
- 获得了对社区现象学,跨年趋势和环境协会的高分辨率洞察力.
- 揭示了社区组成中的时空共分布模式.
结论:
- 这种新的统计方法有效地利用了生态研究的机会性生物多样性数据.
- 该模型增强了我们对生物群落的理解,包括它们的动态和环境驱动因素.
- 这种方法为利用大规模的公民科学生物多样性数据集开辟了新的途径.
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