通过应用贝叶斯统计状态空间模型,在百年级上估计各种开花阶段的真实日期
Nagai Shin1, Hakuryu Fujiwara2, Shinjiro Sugiyama3
1Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology, Yokosuka, Japan.
PloS one
|February 5, 2025
概括
长期桃开花数据揭示了气候变化的影响. 贝叶斯模型使用历史空气温度准确预测开花日期,有助于理解春季现象学变化.
科学领域:
- 生态生态学 生态生态学
- 气候科学 气候科学
- 植物学 植物学
背景情况:
- 了解长期桃开花的现象学对于评估气候变化敏感性至关重要.
- 文化生态系统服务受到春季现象学变化的重大影响.
- 在日本岐的Neodani Usuzumi-zakura (Cerasus itosakura) 提供了一个有价值的案例研究.
研究的目的:
- 从1924年到2024年,估计桃开花日期 (第一个开花,第一个完全开花,最后一个完全开花,最后一个开花) 的年复一年变化.
- 开发一个贝叶斯统计状态空间模型,将现象学与空气温度联系起来.
- 通过历史记录来验证模型的准确性.
主要方法:
- 利用了来自互联网的十年花期表象学数据.
- 应用了贝叶斯统计状态空间模型,结合了空气温度数据.
- 经过验证的模型估计与文学和私人收藏的开花记录.
主要成果:
- 该模型使用冬季最低和初春平均空气温度准确估计了第一次开花 (FFL) 和第一次完全开花 (FFB) 的真实日期.
- 使用4月初的平均空气温度,可以预测最后一次全面开花 (LFB) 和最后一次开花 (LFL) 的日期.
- 无论天气站距离 (1公里与29公里) 差不多,模型性能都是一致的.
结论:
- 提出的贝叶斯模型有效地估计了百年规模的桃开花现象学.
- 该模型整合了历史空气温度数据,即使具有粗略的时间分辨率,也可以准确地进行现象学预测.
- 这种方法提高了对气候变化对植物表象学和相关生态系统服务的影响的理解.
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