用公民科学数据进行实时生物多样性预测的数字双胞胎
Otso Ovaskainen1, Steven Winter2, Gleb Tikhonov3
1Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, Finland. otso.t.ovaskainen@jyu.fi.
Nature ecology & evolution
|January 27, 2026
概括
公民科学现在可以使用AI和智能手机音频预测鸟类分布,即使没有专家识别技能. 这种数字双胞胎方法加速了生物多样性数据收集,以实时进行生态监测.
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:
- 公民科学产生了大量的生物多样性数据,但在数据质量和参与者专业知识方面面临挑战.
- 弥合原始数据收集和可操作的研究成果之间的差距对于有效的生物多样性监测至关重要.
研究的目的:
- 展示公民科学如何与数字双胞胎和机器学习相结合,可以实现准确的实时鸟类分布预测.
- 克服公民科学家在物种识别技能方面的局限性,提高数据质量.
主要方法:
- 利用智能手机应用程序从芬兰各地的公民科学家收集原始音频数据.
- 开发了一个集成机器学习的数字双胞胎,用于自动化鸟类声音分类,验证和重新分类.
- 实施间隔记录和永久点数网络,以减轻时空采样偏差.
主要成果:
- 在两年内产生了超过1500万个鸟类检测.
- 与独立测试数据相比,数字双胞胎信息模型在预测鸟类的时空分布方面显示出更高的准确性.
- 机器学习分类器通过持续的数据验证和重新分类来不断改进.
结论:
- 这种可扩展的方法提高了公民科学的包容性,因为它使没有专家识别技能的个人能够贡献.
- 数字双胞胎方法加速生成可靠的生物多样性信息,特别是在研究不足的地区.
- 该系统具有实时生物监测和推进生态研究的巨大潜力.
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