没有任何物种被遗弃:借鉴力量来绘制数据缺陷物种的地图
Shubhi Sharma1, Kevin Winner1, Laura J Pollock2
1Ecology and Evolutionary Biology Department, Yale University, 165 Prospect Street, New Haven, CT 06520, USA; Center for Biodiversity and Global Change, Yale University, New Haven, CT, USA.
Trends in ecology & evolution
|June 26, 2025
概括
充分研究物种的数据可以改善生物多样性预测对于那些数据有限的人来说. 这种方法有助于解决数据缺口,并有助于对研究不足的物种进行保护计划.
科学领域:
- 生态生态学 生态生态学
- 保护生物学 保护生物学
- 生物多样性科学 生物多样性科学
背景情况:
- 大多数物种存在显著的数据缺口,阻碍了生物多样性变化检测和保护工作.
- 跨物种数据分布的不平等阻碍了对生物多样性模式的全面理解.
- 华莱士缺陷,即关于物种分布的知识差距,仍然是保护的重大挑战.
研究的目的:
- 审查统计方法,特别是多种和联合物种分布模型,这些模型利用辅助信息来改善对数据不足物种的预测.
- 突出从数据丰富的物种中"借鉴力量"的潜力,以增强对未经研究的种类的生态利基建模.
- 评估如何结合特征和族系可以帮助克服物种发生数据的局限性.
主要方法:
- 复杂物种和联合物种分布模型的审查.
- 分析结合辅助信息 (物种特征和族系) 的方法.
- 对空间预测的"借贷力"方法的评估.
主要成果:
- 结合辅助信息的多种和联合物种模型显示出改善数据不足物种预测的前景.
- 辅助信息,当指标的利基相似性,可以显著提高分布估计.
- 这些方法提供了一种可行的策略,可以部分解决华莱士人的缺口.
结论:
- 统计方法,特别是利用辅助信息的方法,可以大大改善对数据不足物种的生物多样性预测.
- 虽然这些方法不能代替数据收集,但对于弥合生物多样性科学中的知识差距至关重要.
- 在面对数据限制的情况下,审查的方法代表了理解和管理生物多样性的重大进步.
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