如何使用中立社区模型从社区丰富数据中量化移民
Ramis Rafay1, Eric W Jones2,3,4, David A Sivak4
1Department of Biological Sciences, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.
概括
分散连接着生物群落,但很难衡量. 新的方法准确估计复杂社区的移民率 (NTm),指导可靠生物多样性研究的采样.
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:
- 生物群落通过分散相互连接,影响当地和地区的多样性.
- 直接测量分散是具有挑战性的,阻碍了对其生态影响的理解.
- 中立社区模型 (NCM) 提供了一种使用丰富数据估计移民率 (NTm) 的方法.
研究的目的:
- 评估基于NCM的移民率 (NTm) 估计的准确性.
- 介绍和比较NTm的新型推理方法.
- 确定最佳的采样策略,以在不同社区准确估计NTm.
主要方法:
- 引入了两种新的推理方法:基于方差的和迪里克莱特多项式日志概率 (DM-LL).
- 通过已建立的基于占用率的推断方法来补充这些.
- 使用模拟活性污泥微生物组和现实数据集 (废水,热带树木,珊瑚礁) 的验证方法.
主要成果:
- 所有测试方法在模拟中估计NTm在基准真实值的10%以内.
- 基于差异的和DM-LL的方法需要更少的采样努力来准确估计.
- 准确的NTm推断需要读取的深度超过每个样本的移民率.
结论:
- 开发并验证了使用NCM量化社区移民的可靠方法.
- 确定了可靠NTm估计的关键采样要求 (读取深度).
- 为研究复杂,多样化的生物群落中的分散提供了实际指导方针.
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