Bayesian species delimitation can be robust to guide-tree inference errors.
Bayesian species delimitation can be robust to guide-tree inference errors.
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DOI：
10.1093/sysbio/syu052
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发表时间：
2014-11
影响因子：
6.5
通讯作者：
Yang Z
中科院分区：
文献类型：
--
作者：
Zhang C;Rannala B;Yang Z
传统上，物种的界限是基于形态、行为和生态特征来确定的。近年来，由于测序技术的进步和数据分析统计方法的发展，遗传序列数据越来越多地被用来界定物种(Wiens 2007；Fujita等人)。2012年)。早期的方法依赖于重建的基因树中的相互单性、假定物种之间固定的序列差异，或者简单地切断假定物种之间的迁移率或遗传距离(Sites和Matt 2004)。最近的方法是基于多物种结合模型(Rannala和Yang，2003)，避免任意截断(Knowles和Carstens，2007)。在最近的方法中，Yang和Rannala(2010)的贝叶斯方法比其竞争对手(Fujita和Leaché2011)具有许多优势。贝叶斯方法使用贝叶斯模型选择来比较多物种合并框架中不同物种的划界模型，并使用可逆跳跃马尔可夫链蒙特卡罗(RjMCMC)来估计不同划界模型的后验概率。该方法适应多个基因座，且不需要推断基因树的单亲互易。潜在的多物种融合模型解释了由于祖先多态而导致的不完全谱系排序和物种-树-基因树冲突。序列比对的似然计算使该方法能够充分利用数据中的信息，同时考虑到基因树拓扑结构和分支长度的不确定性。与传统的基于形态的分类学实践相比，贝叶斯方法从系谱和种群遗传学的角度推断物种地位，并且可以说更客观(Fujita和Leaché2011；Fujita等人，2011)。在计算机模拟中，贝叶斯方法被发现具有良好的统计特性(Leaché和Fujita 2010；Zhang et al.2011年；Camargo等人。假阳性(将一个物种一分为二的错误)和假阴性(无法识别不同的物种的错误)。模拟还表明，该方法在存在少量基因流动的情况下具有很好的识别不同物种的能力，并且当迁移率较高时不会误导将地理种群推断为不同物种(Zhang等人。2011年)。
Species limits are traditionally determined based on morphological, behavioral, and ecological traits. In recent years, genetic sequence data have increasingly been used to delimit species due to the advancement of sequencing technologies and development of statistical methods of data analysis (Wiens 2007; Fujita et al. 2012). Early methods relied on reciprocal monophyly in the reconstructed gene trees, fixed sequence differences between putative species, or simple cut-offs on migration rates or genetic distances between putative species (Sites and Marshall 2004). More recent methods are based on the multispecies coalescent model (Rannala and Yang 2003) and avoid arbitrary cut-offs (Knowles and Carstens 2007). Among the recent methods, the Bayesian method of Yang and Rannala (2010) has a number of advantages over its competitors (Fujita and Leaché 2011). The Bayesian method uses Bayesian model selection to compare different speciesdelimitation models in the multispecies coalescent framework, and uses reversible-jump Markov chain Monte Carlo (rjMCMC) to estimate the posterior probabilities for different delimitation models. The method accommodates multiple loci, and does not require reciprocal monophyly of inferred gene trees. The underlying multispecies coalescent model accounts for incomplete lineage sorting and species-tree–gene tree conflicts due to ancestral polymorphism. The likelihood calculation on sequence alignments allows the method to make a full use of the information in the data while accounting for the uncertainties in the gene tree topologies and branch lengths. Compared with traditional morphologybased taxonomic practice, which varies widely across taxonomic groups, the Bayesian method infers species status from a genealogical and population genetic perspective and is arguably more objective (Fujita and Leaché 2011; Fujita et al. 2012).In computer simulations, the Bayesian method was found to have good statistical properties (Leaché and Fujita 2010; Zhang et al. 2011; Camargo et al. 2012), with low false positives (the error of splitting one species into two) and false negatives (the error of failing to recognize distinct species). Simulations also suggest that the method has good power in identifying distinct species in the presence of small amounts of gene flow, and is not misled to infer geographical populations as distinct species when the migration rate is high (Zhang et al. 2011).
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影响因子：
7
作者：
Martin SH;Dasmahapatra KK;Nadeau NJ;Salazar C;Walters JR;Simpson F;Blaxter M;Manica A;Mallet J;Jiggins CD
通讯作者：
Jiggins CD
影响因子：
3.4
作者：
Hambäck PA;Weingartner E;Ericson L;Fors L;Cassel-Lundhagen A;Stenberg JA;Bergsten J
通讯作者：
Bergsten J
影响因子：
6.5
作者：
Huelsenbeck, JP;Rannala, B
通讯作者：
Rannala, B
影响因子：
6.5
作者：
Knowles, L. Lacey;Carstens, Bryan C.
通讯作者：
Carstens, Bryan C.
影响因子：
3.3
作者：
Rannala, Bruce;Yang, Ziheng
通讯作者：
Yang, Ziheng