首页> 外文期刊>Evolution: International Journal of Organic Evolution >Detecting local diversity-dependence in diversification
【24h】

Detecting local diversity-dependence in diversification

机译:在多样化中检测局部分集依赖性

获取原文
获取原文并翻译 | 示例
           

摘要

Whether there are ecological limits to species diversification is a hotly debated topic. Molecular phylogenies show slowdowns in lineage accumulation, suggesting that speciation rates decline with increasing diversity. A maximum-likelihood (ML) method to detect diversity-dependent (DD) diversification from phylogenetic branching times exists, but it assumes that diversity-dependence is a global phenomenon and therefore ignores that the underlying species interactions are mostly local, and not all species in the phylogeny co-occur locally. Here, we explore whether this ML method based on the nonspatial diversity-dependence model can detect local diversity-dependence, by applying it to phylogenies, simulated with a spatial stochastic model of local DD speciation, extinction, and dispersal between two local communities. We find that type I errors (falsely detecting diversity-dependence) are low, and the power to detect diversity-dependence is high when dispersal rates are not too low. Interestingly, when dispersal is high the power to detect diversity-dependence is even higher than in the nonspatial model. Moreover, estimates of intrinsic speciation rate, extinction rate, and ecological limit strongly depend on dispersal rate. We conclude that the nonspatial DD approach can be used to detect diversity-dependence in clades of species that live in not too disconnected areas, but parameter estimates must be interpreted cautiously.
机译:物种多样化是否存在生态限制是一个热门辩论的主题。分子系统发育显示谱系积累的放缓,表明样品率随着多样性的增加而下降。以存在从系统发育分支时间检测分集依赖性(DD)多样化的最大可能性(ML)方法,但它假设多样性依赖性是全球性现象,因此忽略了潜在的物种相互作用大多是局部的,而不是所有物种在局部地区发生系统。在这里,我们探讨基于非缺点分集依赖模型的该ML方法可以通过将其施加到文学发育,利用局部DD格纹的空间随机模型模拟,灭绝和两个当地社区之间的空间随机模型来检测局部分集依赖性。我们发现I型错误(虚假检测分集依赖性)低,并且当分散速率不太低时检测分集依赖性的功率很高。有趣的是,当分散的功率高,检测分集的力量依赖性甚至高于非缺陷模型。此外,强烈依赖于脱离率的内在物质率,消失率和生态极限​​的估计。我们得出结论,非缺课DD方法可用于检测生活在不太断开区域的物种的片状中的分集依赖性,但必须谨慎地解释参数估计。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号