使用公民科学数据来预测不同地区生态现象的时间
César Capinha1,2, Ana Ceia-Hasse3,4, Sergio de-Miguel5,6
1Centre of Geographical Studies, Institute of Geography and Spatial Planning of the University of Lisbon, Lisbon, Portugal.
Bioscience
|July 26, 2024
概括
公民科学数据,包括照片,现在可以使用新的机器学习框架预测生态事件. 这种方法模拟观测以了解环境条件,改善大面积的生态预测.
科学领域:
- 生态生态学 生态生态学
- 计算生物学 计算生物学
- 数据科学数据科学数据科学
背景情况:
- 有限的长期观测数据阻碍了对年内生态变化的准确预测.
- 越来越多的时间标记的公民科学记录,包括摄影证据,为生态监测提供了新的机会.
- 传统的统计和机器学习方法与稀疏的,长期的生态数据集作斗争.
研究的目的:
- 开发一个新的框架,利用公民科学数据预测生态现象.
- 利用机器学习算法,基于时间观测记录来建模生态事件.
- 为了解决时间偏差在生态预测的公民科学数据的挑战.
主要方法:
- 根据相对现象学利基的概念开发了一个框架.
- 利用机器学习算法,将公民科学观测模型作为环境条件的时间样本.
- 验证了在地理尺度上预测生态事件的时间动态的方法.
主要成果:
- 新的框架准确地预测了生态事件的时间动态.
- 该方法证明了对记录努力的时间偏差的稳定性.
- 通过使用公民科学数据,成功地模拟了跨大地理尺度的生态现象.
结论:
- 公民科学数据具有巨大的潜力,可以在空间和近实时预测生态现象.
- 开发的框架很容易适用于使用预测建模的生态学家和从业人员.
- 这种方法通过整合多样化,可访问的数据源来增强生态预测能力.
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