Mechanical tradeoffs in performance are predicted to sculpt macroevolutionary patterns of morphological diversity across environmental gradients. Water depth shapes the amount of wave energy organisms experience, which should result in evolutionary tradeoffs between speed and maneuverability in fish swimming morphology. Here, we tested whether morphological evolution would reflect functional tradeoffs in swimming performance in 131 species of wrasses and parrotfish (Family: Labridae) across a water depth gradient. We found that maximum water depth predicts variation in pectoral fin aspect ratio (AR) in wrasses, but not in parrotfish. Shallow-water wrasses exhibit wing-like pectoral fins that help with 'flapping', which allows more efficient swimming at faster speeds. Deeper water species, in contrast, exhibit more paddle-like pectoral fins associated with enhanced maneuverability at slower speeds. Functional morphology responds to a number of different, potentially contrasting selective pressures. Furthermore, many-to-one mapping may release some traits from selection on performance at the expense of others. As such, deciphering the signatures of mechanical tradeoffs on phenotypic evolution will require integrating multiple aspects of ecological and morphological variation. As the field of evolutionary biomechanics moves into the era of big data, we will be uniquely poised to disentangle the intrinsic and extrinsic predictors of functional diversity.

中文翻译：

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隆头鱼和鹦嘴鱼游泳形态与水深之间的弱关系相信对形态功能进化的多重选择要求

预计性能的机械权衡将塑造跨环境梯度的形态多样性的宏观进化模式。水深决定了生物体经历的波浪能的数量，这将导致鱼类游泳形态的速度和机动性之间的进化权衡。在这里，我们测试了形态进化是否会反映 131 种隆头鱼和鹦嘴鱼（科：Labridae）跨越水深梯度的游泳性能的功能权衡。我们发现最大水深预测了濑鱼胸鳍纵横比 (AR) 的变化，但不是鹦鹉鱼。浅水濑鱼具有翅膀状的胸鳍，有助于“拍打”，从而以更快的速度更有效地游泳。相比之下，深水物种 表现出更多的桨状胸鳍，在较慢的速度下具有增强的机动性。功能形态对许多不同的、可能形成对比的选择压力作出反应。此外，多对一映射可能会以牺牲其他特性为代价从性能选择中释放一些特性。因此，破译表型进化的机械权衡特征将需要整合生态和形态变异的多个方面。随着进化生物力学领域进入大数据时代，我们将准备好解开功能多样性的内在和外在预测因素。多对一映射可能会以牺牲其他特性为代价从性能选择中释放一些特性。因此，破译表型进化的机械权衡特征将需要整合生态和形态变异的多个方面。随着进化生物力学领域进入大数据时代，我们将准备好解开功能多样性的内在和外在预测因素。多对一映射可能会以牺牲其他特性为代价从性能选择中释放一些特性。因此，破译表型进化的机械权衡特征将需要整合生态和形态变异的多个方面。随着进化生物力学领域进入大数据时代，我们将准备好解开功能多样性的内在和外在预测因素。