Weissman Maya, Yin Zheng, Raynes Yevgeniy, Weinreich Daniel
Am Nat. 2025 Jun;205(6):572-589. doi: 10.1086/735690. Epub 2025 May 1.
AbstractBet hedging is a ubiquitous strategy for risk reduction in environments that change unpredictably, where a lineage lowers its variance in fitness across environments at the expense of also lowering its arithmetic mean fitness. Classically, the benefit of bet hedging has been quantified using geometric mean fitness (GMF); bet hedging is expected to evolve if and only if it has a higher GMF than the wild type. We build on previous research on the effect of incorporating stochasticity in phenotypic distribution, environment, and reproduction to investigate the extent to which these sources of stochasticity impact the evolution of real-world bet-hedging traits. We demonstrate that modeling stochasticity can alter the sign of selection for bet hedging compared with deterministic predictions. Bet hedging can be deleterious at small population sizes and beneficial at larger population sizes. This phenomenon occurs across parameter space for conservative and diversified bet hedgers. We apply our model to published data to show that incorporating stochasticity is necessary to explain the evolution of real-world bet-hedging traits, including variable germination phenology, antibiotic persistence, and seed banking in . Our results suggest that GMF is not enough to predict when bet hedging is adaptive in a wide range of scenarios.
摘要
在环境变化不可预测的情况下，风险对冲是一种普遍存在的降低风险的策略，即一个谱系以降低其在不同环境下的平均适应度为代价，降低其适应度的方差。传统上，风险对冲的益处是用几何平均适应度（GMF）来量化的；当且仅当风险对冲的GMF高于野生型时，它才有望进化。我们基于之前关于在表型分布、环境和繁殖中纳入随机性的影响的研究，来探究这些随机性来源在多大程度上影响现实世界中风险对冲性状的进化。我们证明，与确定性预测相比，对随机性进行建模可以改变风险对冲选择的符号。在小种群规模下，风险对冲可能是有害的，而在大种群规模下则是有益的。这种现象在保守型和多样化风险对冲者的参数空间中都存在。我们将我们的模型应用于已发表的数据，以表明纳入随机性对于解释现实世界中风险对冲性状的进化是必要的，包括可变的萌发物候、抗生素耐受性和种子库。我们的结果表明，在广泛的情景中，GMF不足以预测风险对冲何时具有适应性。
