...
首页> 外文期刊>Biological Journal of the Linnean Society >A morphometric assessment of species boundaries in a widespread anole lizard (Squamata: Dactyloidae)
【24h】

A morphometric assessment of species boundaries in a widespread anole lizard (Squamata: Dactyloidae)

机译:普遍抗旱蜥蜥蜴(Squamata:dactyloidae)的物种边界的形态学评估

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       

摘要

Cryptic species - genetically distinct species that are morphologically difficult to distinguish - present challenges to systematists. Operationally, cryptic species are very difficult to identify and sole use of genetic data or morphological data can fail to recognize evolutionarily isolated lineages. We use morphometric data to test species boundaries hypothesized with genetic data in the North Caribbean bark anole (Anolis distichus), a suspected species complex. We use univariate and multivariate analyses to test if candidate species based on genetic data can be accurately diagnosed. We also test alternative species delimitation scenarios with a model fitting approach that evaluates normal mixture models capable of identifying morphological clusters. Our analyses reject the hypothesis that the candidate species are diagnosable. Neither uni- nor multivariate morphometric data distinguish candidate species. The best-supported model included two morphological clusters; however, these clusters were uneven and did not align with a plausible species divergence scenario. After removing two related traits driving this result, only one cluster was supported. Despite substantial differentiation revealed by genetic data, we recover no new evidence to delimit species and refrain from taxonomic revision. This study highlights the importance of considering other types of data along with molecular data when delimiting species.
机译:神秘物种——在形态上难以区分的基因不同的物种——给系统学家带来了挑战。在操作上,隐秘物种很难识别,仅使用遗传数据或形态学数据可能无法识别进化上孤立的谱系。我们使用形态计量学数据来测试在北加勒比树皮anole(Anolis distichus)中假设的物种边界,这是一个疑似物种复合体。我们使用单变量和多变量分析来测试基于遗传数据的候选物种是否能够被准确诊断。我们还使用模型拟合方法测试替代物种定界方案,该方法评估能够识别形态簇的正常混合模型。我们的分析否定了候选物种可诊断的假设。无论是单变量还是多变量形态测量数据都无法区分候选物种。最佳支持模型包括两个形态学簇;然而,这些集群是不均匀的,不符合一个合理的物种分化情况。在删除两个驱动该结果的相关特征后,只支持一个聚类。尽管遗传数据显示了实质性的差异,但我们没有找到新的证据来划分物种,也没有进行分类修订。这项研究强调了在划分物种时,考虑其他类型的数据以及分子数据的重要性。

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