Many theoretical models predict when genetic evolution and phenotypic plasticity allow adaptation to changing environmental conditions. These models generally assume stabilizing selection around some optimal phenotype. We however often ignore how optimal phenotypes change with the environment, which limit our understanding of the adaptive value of phenotypic plasticity. Here, we propose an approach based on our knowledge of the causal relationships between climate, adaptive traits, and fitness to further these questions. This approach relies on a sensitivity analysis of the process‐based model Phenofit, which mathematically formalizes these causal relationships, to predict fitness landscapes and optimal budburst dates along elevation gradients in three major European tree species. Variation in the overall shape of the fitness landscape and resulting directional selection gradients were found to be mainly driven by temperature variation. The optimal budburst date was delayed with elevation, while the range of dates allowing high fitness narrowed and the maximal fitness at the optimum decreased. We also found that the plasticity of the budburst date should allow tracking the spatial variation in the optimal date, but with variable mismatch depending on the species, ranging from negligible mismatch in fir, moderate in beech, to large in oak. Phenotypic plasticity would therefore be more adaptive in fir and beech than in oak. In all species, we predicted stronger directional selection for earlier budburst date at higher elevation. The weak selection on budburst date in fir should result in the evolution of negligible genetic divergence, while beech and oak would evolve counter‐gradient variation, where genetic and environmental effects are in opposite directions. Our study suggests that theoretical models should consider how whole fitness landscapes change with the environment. The approach introduced here has the potential to be developed for other traits and species to explore how populations will adapt to climate change.

中文翻译：

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最佳位置在哪里？预测选择沿气候梯度的变化以及可塑性的适应性值。树木物候学案例研究

许多理论模型预测何时遗传进化和表型可塑性允许适应不断变化的环境条件。这些模型通常假设围绕某个最佳表型进行稳定选择。但是，我们经常忽略最佳表型如何随环境变化，这限制了我们对表型可塑性适应性价值的理解。在这里，我们基于对气候，适应性状和适应性之间因果关系的了解，提出了一种方法，以进一步解决这些问题。这种方法依赖于基于过程的模型Phenofit的敏感性分析，从数学上形式化了这些因果关系，以预测三种主要欧洲树种的健身景观和最佳海拔高度的萌芽日期。发现健身景观整体形状的变化以及所产生的方向选择梯度主要由温度变化驱动。最佳芽期随着高度的升高而延迟，而允许高适应性的日期范围变窄，最佳适应性的最大日期降低。我们还发现，芽期的可塑性应该可以跟踪最佳日期的空间变化，但是根据种类的不同，其失配范围也从可变，可以忽略不计，包括冷杉的失配，山毛榉的中等失配到橡木的失配。因此，杉木和山毛榉的表型可塑性比橡树的表型可塑性更好。在所有物种中 我们预测在更高海拔的较早芽期更强的方向选择。冷杉芽期的选择不当应导致遗传差异可忽略不计，而山毛榉和橡树则将产生反梯度变异，其中遗传和环境影响方向相反。我们的研究表明，理论模型应考虑整个健身景观如何随环境变化。本文介绍的方法有可能被开发用于其他性状和物种，以探索人口将如何适应气候变化。遗传和环境影响方向相反。我们的研究表明，理论模型应考虑整个健身景观如何随环境变化。本文介绍的方法有可能被开发用于其他性状和物种，以探索人口将如何适应气候变化。遗传和环境影响方向相反。我们的研究表明，理论模型应考虑整个健身景观如何随环境变化。本文介绍的方法有可能被开发用于其他性状和物种，以探索人口将如何适应气候变化。