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Performance in three shell functions predicts the phenotypic distribution of hard-shelled turtles

机译:三个壳体功能中的性能预测了硬壳龟的表型分布

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Adaptive landscapes have served as fruitful guides to evolutionary research for nearly a century. Current methods guided by landscape frameworks mostly utilize evolutionary modeling (e.g., fitting data to Ornstein-Uhlenbeck models) to make inferences about adaptive peaks. Recent alternative methods utilize known relationships between phenotypes and functional performance to derive information about adaptive landscapes; this information can then help explain the distribution of species in phenotypic space and help infer the relative importance of various functions for guiding diversification. Here, data on performance for three turtle shell functions-strength, hydrodynamic efficiency, and self-righting ability-are used to develop a set of predicted performance optima in shell shape space. The distribution of performance optima shows significant similarity to the distribution of existing turtle species and helps explain the absence of shells in otherwise anomalously empty regions of morphospace. The method outperforms a modeling-based approach in inferring the location of reasonable adaptive peaks and in explaining the shape of the phenotypic distributions of turtle shells. Performance surface-based methods allow researchers to more directly connect functional performance with macroevolutionary diversification, and to explain the distribution of species (including presences and absences) across phenotypic space.
机译:适应性景观曾担任近一个世纪的进化研究的富有成效的指南。景观框架引导的当前方法主要利用进化建模(例如,拟合数据到Ornstein-Uhlenbeck模型),以便对自适应峰进行推断。最近的替代方法利用表型和功能性能之间的已知关系来推导有关适应性景观的信息;然后,该信息可以帮助解释在表型空间中物种的分布,并有助于推断各种功能对引导多样化的相对重要性。这里,有关三个龟壳功能的性能的数据 - 强度,流体动力学效率和自职能力 - 用于在壳体形状空间中开发一组预测性能最佳。性能优化的分布显示了与现有龟类的分布有关的显着相似性,有助于解释其他异常空旷的形态学区域的壳体的缺失。该方法在推断合理自适应峰的位置和解释龟壳的表型分布的形状方面优于推断基于建模的方法。基于性能的方法,允许研究人员更直接地通过显像多样化连接功能性能,并在表型空间中解释物种(包括遗址)的分布。

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