应用深度学习来量化瑞典海洋保护区的长期生态变化驱动因素
Christian L Nilsson1,2, Søren Faurby2,3, Emil Burman1,2
1Department of Marine Sciences University of Gothenburg Gothenburg Sweden.
Ecology and evolution
|September 4, 2025
概括
人工智能模型分析了科斯特哈维特国家公园的海洋生物, 变暖的气温正在推动变化,
科学领域:
- 海洋生态
- 生态中的人工智能
- 保护科学
背景情况:
- 瑞典的科斯特哈维特国家公园是一个海洋保护区,
- 地无脊动物群体是海洋生态系统健康的关键指标.
- 了解长期生态变化需要强大的数据收集和分析方法.
研究的目的:
- 开发和应用一个物体检测模型来对地无脊椎动物进行归类.
- 从1997年到2023年为17种盆地无脊椎动物类型生成一个全面的发生记录数据集.
- 分析谷底分类和功能群的深度分布和丰度趋势,以确定生态变化.
主要方法:
- 训练具有0.738的平均精度的物体检测模型来分类17种地无脊椎动物.
- 将受过训练的模型应用于存档的视频录像 (1997-2023年),以创建72,369个事件记录的数据集.
- 量化深度分布和时间丰度趋势,并将其与温度等环境因素相关联.
主要成果:
- 在深度≥45米的17种类型中,已确定了15种类型的深度分布,并提出了6种类型的扩展.
- 在8种类型中观察到大量增加,在5种类型中观察到减少.
- 社区结构转向更小,耐热的悬浮养动物,大型,热敏的种群迁移到更深的水域.
结论:
- 温度偏好和生物体大小是盆地无脊椎动物中观察到的丰度趋势的关键驱动因素.
- 科斯特哈维特国家公园的保护措施是有效的,
- 建议将渔业法规扩展到更深的水域,并建立新的深水海洋保护区,以保护冷水物种.
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