通过超级SDM (机器学习,开放式大数据和云) 实现更全面的全球松鼠热点和冷点
Moriz Steiner1,2,3, F Huettmann4, N Bryans5
1IUCN Small Mammal Specialist Group (SMSG), IUCN, Rue Mauverney 28, 1196, Gland, Switzerland. moriz.steiner.work@gmail.com.
Scientific reports
|March 3, 2024
概括
超级物种分布模型 (SDM) 利用人工智能和大数据进行增强的全球推断,这对于边缘化和危的松鼠物种至关重要. 这种方法改善了保护评估,特别是在数据不足的热带地区.
科学领域:
- 生态与保护生物学 生态与保护生物学
- 计算生物学和生物信息学
- 人工智能和机器学习
背景情况:
- 物种分布模型 (SDM) 对于理解物种息地联系和为保护政策提供信息至关重要.
- 目前的SDM应用程序,特别是那些使用机器学习 (ML) 和人工智能 (AI) 的应用程序,往往无法充分利用其潜力,特别是在政策制定方面.
- 许多全球物种,特别是松鼠,被边缘化,危或数据不足,需要先进的建模技术来有效保护.
研究的目的:
- 引入超级物种分布模型 (超级SDM),集成ML,开放式访问大数据和云计算,以获得卓越的推断.
- 解决当前SDM的局限性,包括与maxent.com等算法相关的"浅层学习"问题.
- 为全球物种热点和冷点评估提供多种大数据SDM框架,促进包容性保护战略.
主要方法:
- 开发一种使用ML,开放式访问大数据和云基础设施的新工作流程.
- 将超级SDM框架应用于全球300多种松鼠物种.
- 对比分析强调了传统SDM (如maxent) 与拟议的大数据方法的共同问题.
主要成果:
- 展示现有SDM方法的共同挑战和局限性,称为"浅层学习".
- 成功实施适用于多种物种的大数据SDM模板.
- 为整体建模和全球物种分布评估的进步奠定基础.
结论:
- 超级SDM提供了一个强大的,包容性的生态推理方法,超越了传统方法的功能.
- 开发的框架对于评估边缘化,危和缺乏数据的物种特别有价值,特别是在热带生态系统中.
- 这项工作为全球生物多样性评估和保护计划的未来研究和政策应用提供了可扩展的模板.
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