使用公民科学数据进行深度学习,可以对大陆范围的物种多样性和组成进行估计
Courtney L Davis1, Yiwei Bai2, Di Chen2
1Cornell Laboratory of Ornithology, Cornell University, Ithaca, New York, USA.
Ecology
|October 2, 2023
概括
深度学习模型为保护提供准确,高分辨率的生物多样性数据. 这项研究使用深度多变量试验网络 (DMVP-DRNets) 来绘制北美地区物种多样性的地图,帮助保护工作.
科学领域:
- 生态生态学 生态生态学
- 计算生物学 计算生物学
- 保护科学 保护科学
背景情况:
- 有效的生物多样性保护取决于准确,可扩展的物种分布数据.
- 目前的数据和方法的局限性阻碍了全面的生态评估.
- 了解物种在相关尺度上的分布对于保护规划至关重要.
研究的目的:
- 开发和应用一个深度学习框架来估计景观规模的物种多样性和组成.
- 为生态研究和保护决策提供准确,高分辨率的生物多样性信息.
- 确定生物多样性保护的关键区域,重点关注北美树木.
主要方法:
- 采用深度推理网络 (DMVP-DRNets) 实现深度多变量实证模型.
- 使用了超过900万个eBird检查清单和72个环境共变量的大型数据集.
- 在大陆范围内对北美鸟类进行全年分析.
主要成果:
- 成功估计了北美各地的景观规模物种多样性和组成.
- 确定了北美树木的高物种多样性的关键区域.
- 捕捉到物种环境关联和物种间相互作用的时空变化.
结论:
- 像DMVP-DRNets这样的深度学习方法可以生成保护所需的准确,高分辨率的生物多样性数据.
- 开发的框架有效地整合了大量的观测和环境数据集.
- 这种方法支持在多个尺度上知情地进行生态研究和保护决策.
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