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.

中文翻译：

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形态学评估物种边界在广泛的蜥蜴蜥蜴（鳞片：Dactyloidae）

隐性物种-在形态上难以区分的遗传上不同的物种-对系统主义者提出了挑战。从操作上讲，隐物种很难识别，仅使用遗传数据或形态数据可能无法识别出进化上孤立的谱系。我们使用形态计量学数据来测试以北加勒比树皮anole（Anolis distichus），可疑物种复杂。我们使用单变量和多变量分析来测试是否可以准确诊断基于遗传数据的候选物种。我们还使用模型拟合方法测试了其他物种划定方案，该方法评估了能够识别形态簇的正常混合物模型。我们的分析驳斥了候选物种可诊断的假设。单变量或多变量形态计量学数据都不能区分候选物种。最佳支持的模型包括两个形态学聚类。然而，这些集群是不均衡的，并且与合理的物种差异情景不符。删除驱动此结果的两个相关特征后，仅支持一个聚类。尽管遗传数据显示出很大的差异，我们没有找到新的证据来划定物种并避免进行分类修改。这项研究凸显了在划定物种时考虑其他类型数据以及分子数据的重要性。