Assessing model sensitivity in ancestral area reconstruction using Lagrange: a case study using the Colchicaceae family
Assessing model sensitivity in ancestral area reconstruction using Lagrange: a case study using the Colchicaceae family
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DOI：
10.1111/jbi.12301
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发表时间：
2014-07-01
影响因子：
3.9
通讯作者：
Renner, Susanne S.
中科院分区：
文献类型：
--
作者：
Chacon, Juliana;Renner, Susanne S.
目的祖先范围的似然分析需要一个参数化模型，该模型由时间校准的系统发育、允许或禁止区域连接的邻接矩阵和具有离散时间段概率的区域分散矩阵组成。该方法在拉格朗日软件中实现。因为它可以包含过去大陆位置的信息，这种方法已经被用于相对古老的进化枝的历史生物地理学研究。令人惊讶的是，没有研究评估这些输入矩阵之间的相互作用。本文利用秋水仙科百合和人工数据研究了输入矩阵对最终估算值的相对影响。地理位置：非洲，澳大利亚，欧亚大陆，北美和南美。方法利用秋水仙科约280种（代表所有属和整个地理范围）和相关外群中的85种的8个质体、线粒体和核DNA区域，获得了用分子钟确定的系统发育图谱。然后，我们将物种分配到6个地理分布，并进行了22次拉格朗日运行，其中我们修改了邻接和扩散矩阵，后者具有0、2或4个时间段和1、3或5个扩散概率。对于第二个数据集，通过洗牌物种分布来修改经验树中深度节点的面积。模型基于全局对数似然进行比较。结果邻接矩阵在很大程度上决定了结果，而时间片和分散概率类别的影响较小。在大多数节点重建的祖先区域不受不同输入矩阵的影响。秋水仙科可能起源于白垩纪东冈瓦纳，最初在澳大利亚（约67Ma）多样化，在古新世-始新世期间到达非洲南部，并从那里扩展到东南亚（可能通过阿拉伯半岛），然后扩展到北美（通过白令陆桥）。至少在小数据集中，拉格朗日模型应该用灵敏度分析进行测试，集中在有约束的和无约束的邻接矩阵上，报告两个输入矩阵的设置，而不仅仅是分散矩阵，这是一个很好的实践，这是两个中不那么决定性的。
Aim Likelihood analyses of ancestral ranges require a parameterized model that consists of a time-calibrated phylogeny, an adjacency matrix' of allowed or forbidden area connections, and an area-dispersal' matrix with probabilities for discrete periods of time. The approach is implemented in the software Lagrange. Because it can incorporate information about past continental positions, the approach has been used in historical biogeographical studies of relatively old clades. Surprisingly, no study has evaluated the interactions among these input matrices. Here we use the lily family Colchicaceae and artificial data to study the relative effect of the input matrices on final estimates. Location Africa, Australia, Eurasia, North America and South America. Methods Using eight plastid, mitochondrial and nuclear DNA regions from 85 of the c. 280 species of Colchicaceae (representing all genera and the entire geographical range) and relevant outgroups, we obtained a well-resolved phylogeny dated with a molecular clock. We then assigned species to six geographical distributions and carried out 22 Lagrange runs in which we modified the adjacency and dispersal matrices, the latter with zero, two or four time periods and one, three or five dispersal probabilities. For a second data set, the areas at deep nodes in the empirical tree were modified by shuffling species distributions. Models were compared based on global log-likelihoods. Results The adjacency matrix strongly determined the outcome, while time slices and dispersal probability categories had minor effects. Ancestral areas reconstructed at most nodes were unaffected by the different input matrices. Colchicaceae are likely to have originated in Cretaceous East Gondwana, initially diversified in Australia (c. 67Ma), reached southern Africa during the Palaeocene-Eocene, and from there extended their range to Southeast Asia (probably through Arabia) and then North America (through Beringia). Main conclusions At least in small data sets, Lagrange models should be tested with sensitivity analyses as carried out here, concentrating on constrained versus unconstrained adjacency matrices, and it should be good practice to report the set-up of both input matrices, not just the dispersal matrix, which is the less decisive of the two.