利用社交媒体数据来追踪物种范围的变化
Shawan Chowdhury1, Niloy Hawladar2, Ripon C Roy2
1School of Biological Sciences, Monash University, Melbourne, Victoria, Australia.
概括
社交媒体数据显著提高了对物种范围转移的理解,特别是在记录不足的地区. 这种方法改善了生物多样性监测和气候变化影响评估.
科学领域:
- 生态生态学 生态生态学
- 保护生物学 保护生物学
- 生物多样性信息学 生物多样性信息学
背景情况:
- 生物多样性监测数据往往偏向全球北方,在巨大多样性国家创造了显著的数据差距.
- 公民科学数据虽然有价值,但具有类似的地理偏见,限制了全面的生物多样性评估.
- 了解范围转移物种的动态对于预测和减轻气候变化影响至关重要.
研究的目的:
- 评估社交媒体数据在改善对迅速变化的物种范围动态的理解方面的有效性.
- 评估将社交媒体数据纳入是否可以解决生物多样性记录中的现有数据缺口,特别是在调查不足的地区.
- 为了比较只使用全球生物多样性信息设施 (GBIF) 数据的物种分布模型 (SDM) 的表现,与结合GBIF和社交媒体数据进行比较.
主要方法:
- 从社交媒体平台 (Flickr,Facebook) 和GBIF.收集的地区数据.
- 开发和校准的物种分布模型 (SDM) 使用两个数据集:仅GBIF数据,和结合GBIF和社交媒体数据.
- 进行了利基评估,以比较每个数据集所捕获的生态区域.
主要成果:
- 社交媒体数据增加了35%的事件记录,GBIF代表性有限的国家的比例更高.
- 在代表性不足和代表性强的国家 (例如澳大利亚,马来西亚) 发现了新的分布信息.
- 与仅使用GBIF模型相比,结合社交媒体数据的SDM显示了更高的范围扩展率,并捕捉了采样不足的环境条件 (低温,低降水量,高海拔).
结论:
- 社交媒体数据为传统的生物多样性记录提供了有价值的,补充数据源,有效地填补了数据缺口.
- 利用社交媒体数据可以显著改善对生物多样性的快速再分配的跟踪,这对于气候变化适应战略至关重要.
- 这种方法增强了我们监测物种范围变化的能力,特别是在数据不足的地区,这导致了更强大的保护计划.
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