期刊
SYSTEMATIC BIOLOGY
卷 61, 期 2, 页码 314-336出版社
OXFORD UNIV PRESS
DOI: 10.1093/sysbio/syr085
关键词
Character evolution; comparative methods; compound eye; Cylindroleberididae; deep sea; dispersal; Dollo's Law; Ostracoda
资金
- National Science Foundation
Complex organs such as eyes are commonly lost during evolution, but the timescale on which lost phenotypes could be reactivated is a matter of long-standing debate, with important implications for the molecular mechanisms of trait loss. Two phylogenetic approaches have been used to test whether regain of traits has occurred. One way is by comparison of nested, continuous-time Markov models of trait evolution, approaches that we term tree-based tests. A second way to demonstrate statistical support for trait regain is through use of node-based tests that employ explicit estimation of ancestral node states. Here, we estimate new molecular and morphological phylogenies and use them to examine the possibility of eye regain and dispersal between abyssal and shallow seas during the history of cylindroleberidid ostracods, a family of about 200 species, comprising both eyeless and sighted species. First, we confirmed that eye presence/absence is correlated with habitat depth. Parameter estimates from a phylogenetic model indicate that speciation is more rapid in deep-sea eyeless clades compared with shallow-water sighted clades. In addition, we found that tree-based statistical tests usually indicated reversals, including both transitions from deep to shallow seas and regain of eyes. In contrast, node-based statistical tests usually failed to show significant support for reversals. These results also hold for simulated phylogenies, indicating that they are not unique to the current data set. We recommend that both tree-based and node-based tests should be examined before making conclusions about character reversal and that ideally, alternative character histories should be tested using additional data, besides just the phylogenetic distribution of presence/absence of the characters.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据