通过扩展兰德适应度函数构建的性状演化新模型：奥恩斯坦-乌伦贝克过程与小王子的蟒蛇相遇
Novel models of trait evolution via an expansion of Lande's fitness function: The Ornstein-Uhlenbeck process meets the Little Prince's boa
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中文总结 AI 辅助
该研究扩展兰德适应度函数，结合OU模型构建性状演化新随机微分方程模型，解释非高斯适应度函数机制，为相关演化假说检验及生态与演化动态研究提供了新途径。
中文摘要 AI 辅助
适应性地形是我们理解演化变化的重要基础。兰德1976年关于表型适应性地形的开创性论文表明，该概念既存在于表型演化模型，也存在于遗传演化模型中，且可结合数据用于检验演化假说。本文重新审视并推广了兰德最初推导的、类似赖特基因型适应性地形的方程，将其扩展到包含两个适应度组分的情况。引入两个适应度组分后，可对适应性地形的形状及其机制基础产生新的预测：更新后的适应度函数的最优点是两个适应度组分最优点的加权平均，权重由各组分上稳定选择的相对强度决定。各适应度组分选择强度的时间或空间异质性，会使整体适应度函数呈现新的形状（不对称性、双峰性或无此类特征），这一可能性已通过已发表文献中的案例研究得到验证。最后，将本文的适应度公式与兰德构建平均表型演化的奥恩斯坦-乌伦贝克（OU）模型的方法结合，可得到此前未被识别的一类用于性状演化的随机微分方程模型。这些结果为自然系统中常见的非高斯适应度函数提供了机制性解释，为检验产生非高斯适应度函数的替代模型提供了途径，并为未来研究生态与演化动态的相互作用（如演化救援研究）铺平了道路。
英文摘要
Adaptive topographies form the foundation for much of our understanding of evolutionary change. Lande's 1976 influential paper on the adaptive topography of phenotypes demonstrated how the concept is inherent in both phenotypic and genetic models of evolution, and how the concept can be used to test evolutionary hypotheses given data. Here, we revisit and generalize Lande's original derivation of an equation analogous to Wright's genotypic adaptive topography to the case of two fitness components. A move to two fitness components yields novel predictions about the shape and mechanistic underpinnings of the adaptive topography. The optimum of this updated fitness function is a weighted average of the optima of the two fitness components, with weights given by the relative strengths of stabilizing selection on each component. Temporal or spatial heterogeneity in the strengths of selection for each fitness component create novel shapes (asymmetry, bi-modality, or lack thereof) of the overall fitness function, a possibility demonstrated with a case-study from the published literature. Finally, when combined with Lande's approach to generate an Ornstein-Uhlenbeck (OU) model for the evolution of the average phenotype, our fitness formulation leads to a previously unrecognized family of stochastic differential equation models of trait evolution. These results provide mechanistic justification for non-Gaussian fitness functions (often observed in natural systems), provide a path for testing alternative models generating non-Gaussian fitness functions, and pave the way for future study of the interplay of ecological and evolutionary dynamics, such as in the study of evolutionary rescue.
