LMdist:局部多面距离准确地测量了生态梯度中的β多样性
Susan L Hoops1, Dan Knights1,2
1Department of Computer Science and Engineering, College of Science and Engineering, University of Minnesota, Minneapolis, MN 55455, United States.
Bioinformatics (Oxford, England)
|December 7, 2023
概括
局部多重距离 (LMdist) 是一种新的算法,可以改进生态距离的测量. 它纠正过度和的对距离,使环境梯度和生态变化的更好地检测.
科学领域:
- 生态生态学 生态生态学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 生态社区的差异化是一个高层次的问题.
- 像排序这样的标准方法可以产生几何扭曲 (例如,形效应) 由于过度和的对距离,掩盖环境影响.
- 这些扭曲妨碍了对生态梯度的准确解释.
研究的目的:
- 引入局部多重距离 (LMdist),这是一个无监督的算法,旨在改进生态距离的测量.
- 在分析具有高物种周转率的长环境梯度时,解决β多样性测量中边界动态范围的局限性.
- 改进样本之间的真实生态距离的表示,以便进行更准确的生态分析.
主要方法:
- LMdist使用图形结构将生态距离对对投射到一个分流体上.
- 它穿过多路面来调整双向距离,特别是那些位于β多样性测量的动态范围的上端的距离.
- 算法选择性地只调整那些因过度和而可能被低估的对值值,从而保持对其他距离的准确表示.
主要成果:
- 在真实和模拟数据集中,LMdist有效地恢复了沿着已知的环境梯度的生态距离.
- 该算法成功地揭示了复杂的多重结构,正如瑞士卷数据集所示的那样.
- 从LMdist调整的距离可以进行更强大的梯度效应统计测试,并发现与初级梯度直角的变化.
结论:
- 通过提供更准确的距离测量,LMdist增强了对生态社区结构的分析.
- 该算法在排序中减轻了几何文物,从而使生态模式的可视化和解释更加清晰.
- LMdist促进了强大的统计推断和发现新的生态变异,改善了我们对生态系统动态的理解.
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