作为现象分布的被动声学数据:发现时间生态的信号
Mary K Clapp1, Morgan W Tingley2, Damon B Lesmeister3
1The Institute for Bird Populations Petaluma California USA.
Ecology and evolution
|February 5, 2026
概括
通过人工智能的被动声学监测 (PAM) 推动了鸟类声学现象学研究. 这种新方法量化了鸟类的声音模式,揭示了生态洞察力,并支持长期的环境监测.
科学领域:
- 生态生态学 生态生态学
- 生物声学是一种生物声学.
- 计算生物学 计算生物学
背景情况:
- 被动声学监测 (PAM) 是一种可扩展的生态声音数据收集方法.
- 分析PAM数据以了解动物声学表现现现的现有方法是不发达的,缺乏标准化.
- 人工智能 (AI) 提供了处理大型声学数据集的潜力,但需要验证的生态连接.
研究的目的:
- 开发和介绍一种可复制的方法管道,用于使用PAM数据量化鸟类声学表现.
- 测试与现象事件和环境因素有关的鸟类声活动模式的假设.
- 建立一个标准化的标准化.
- 主要_方法 方法
- 主要_结果
- 结论 结论 结论 结论
主要方法:
- 在奥林匹克国家公园的185个地点对18568小时的音频数据应用了方法管道.
- 使用人工智能物种分类器 (BirdNET) 和专家审查的精度值来过数据.
- 采用分层通用添加模型 (HGAM) 来估计25种物种的声乐活动概率和提取表态.
主要成果:
- 衍生现象学在很大程度上支持了生态预期,寄居物种比迁徙物种表现出更早,更长的声周期.
- 与较低的高度相比,在中等高度观察到延迟和缩短的声学现象.
- 在许多物种中确定了双模声格,表明与鸟类繁殖周期过渡和其他生态事件的联系.
结论:
- 开发的PAM管道提供了一种灵活和可重复的方法来量化鸟类声学现象学.
- 结果表明PAM和AI在生态监测和检测环境变化导致的现象学转变方面的潜力.
- 建议进一步与现场观测进行整合,以加强声学现象学与生物状态之间的联系.
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