Functional tradeoffs are often viewed as constraints on phenotypic evolution, but they can also facilitate evolution across the suboptimal valleys separating performance peaks. I explore this process by reviewing a previously published model of how disruptive selection from competing functional demands defines an intermediate performance optimum for morphological systems that cannot simultaneously be optimized for all of the functional roles they must play. Because of the inherent tradeoffs in such a system, its optimal morphology in any particular environmental context will usually be intermediate between the performance peaks of the competing functions. The proportional contribution of each functional demand can be estimated by maximum likelihood from empirically observed morphologies, including complex ones measured with multivariate geometric morphometrics, using this model. The resulting tradeoff weight can be mapped onto a phylogenetic tree to study how the performance optimum has shifted across a functional landscape circumscribed by the function-specific performance peaks. This model of tradeoff evolution is sharply different from one in which a multipeak OU model is applied to a set of morphologies and a phylogenetic tree to estimate how many separate performance optima exist. The multi-peak OU approach assumes that each branch is pushed toward one of two or more performance peaks that exist simultaneously and are separated by valleys of poor performance, whereas the model discussed here assumes that each branch tracks a single optimal performance peak that wanders through morphospace as the balance of functional demands shifts. That the movements of this net performance peak emerge from changing frequencies of selection events from opposing functional demands is illustrated using a series of computational simulations. These simulations show how functional tradeoffs can carry evolution across putative performance valleys: even though intermediate morphologies may not perform optimally for any one function, they may represent the optimal solution in any environment in which an organism experiences competing functional demands.

中文翻译：

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功能权衡携带表型穿越死亡阴影之谷

功能权衡通常被视为对表型进化的限制，但它们也可以促进跨越分离性能峰值的次优谷的进化。我通过审查先前发布的模型来探索这个过程，该模型从竞争性功能需求中进行破坏性选择如何定义形态系统的中间性能优化，这些系统无法同时针对它们必须扮演的所有功能角色进行优化。由于此类系统的固有权衡，其在任何特定环境背景下的最佳形态通常介于竞争功能的性能峰值之间。每个功能需求的比例贡献可以通过经验观察形态的最大似然估计，包括使用这个模型用多元几何形态测量学测量的复杂的。由此产生的权衡权重可以映射到系统发育树上，以研究性能优化如何跨越由功能特定的性能峰值限制的功能景观。这种权衡演化模型与将多峰 OU 模型应用于一组形态和系统发育树以估计存在多少个单独的性能最优值的模型截然不同。多峰值 OU 方法假设每个分支都被推向同时存在的两个或多个性能峰值之一，并被性能不佳的谷隔开，而这里讨论的模型假设每个分支都跟踪一个单一的最佳性能峰值，随着功能需求的平衡变化，该峰值在形态空间中徘徊。使用一系列计算模拟说明了这一净性能峰值的运动是由于来自相反功能需求的选择事件频率的变化而出现的。这些模拟显示了功能权衡如何在假定的性能谷中进行进化：即使中间形态可能无法对任何一个功能实现最佳性能，但它们可能代表在有机体经历竞争性功能需求的任何环境中的最佳解决方案。使用一系列计算模拟说明了这一净性能峰值的运动是由于来自相反功能需求的选择事件频率的变化而出现的。这些模拟显示了功能权衡如何在假定的性能谷中进行进化：即使中间形态可能无法对任何一个功能实现最佳性能，但它们可能代表在有机体经历竞争性功能需求的任何环境中的最佳解决方案。使用一系列计算模拟说明了这一净性能峰值的运动是由于来自相反功能需求的选择事件频率的变化而出现的。这些模拟显示了功能权衡如何在假定的性能谷中进行进化：即使中间形态可能无法对任何一个功能实现最佳性能，但它们可能代表在有机体经历竞争性功能需求的任何环境中的最佳解决方案。