探索气候对美国鸟类迁徙的时空影响随机森林
I Avery Bick1,2, Vegar Bakkestuen3, Marius Pedersen4
1Norwegian Institute for Nature Research, Oslo, Norway. i.avery.bick@gmail.com.
Scientific reports
|July 2, 2025
概括
全球变暖扰乱了鸟类迁徙的时间. 这项研究使用机器学习模型根据气候数据预测鸟类的出现情况,帮助保护候鸟的保护工作.
科学领域:
- 生态生态学 生态生态学
- 气候变化生物学 气候变化生物学
- 计算生物学 计算生物学
背景情况:
- 迁徙鸟类依靠气候周期来确定季节性时间,但人类引起的全球变暖正在改变这些模式.
- 了解气候对鸟类迁徙的影响对于在不断变化的环境中制定有效的保护战略至关重要.
研究的目的:
- 通过模拟方法,探索气候如何影响鸟类的出现.
- 评估机器学习模型在基于气候数据的迁徙鸟类分布预测方面的有效性.
主要方法:
- 在北美和南美气候数据上训练有素的多变量和多响应随机森林模型.
- 在美国东北部生态区域 (2008-2018) 预测了41种迁徙流动物的eBird调查发生率.
- 利用滞后的气候特征来预测秋季迁徙期间的鸟类发生情况.
主要成果:
- 仅在10月份使用落后的气候特征就实现了更准确的鸟类发生预测.
- 证明机器学习模型指标可以识别影响迁徙行为的时空气候线索.
- 探索随机森林的预测能力和局限性,用于未来的鸟类发生预测.
结论:
- 气候变化对迁徙鸟类的发生模式产生重大影响.
- 机器学习,特别是随机森林,为了解和预测鸟类迁徙中气候驱动的变化提供了宝贵的工具.
- 结果为未来气候情景下的保护计划提供了洞察力.
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