This study uses evolutionary simulations to explore the strategies that emerge to enable populations to cope with random environmental changes in situations where lifetime learning approaches are not available to accommodate them. In particular, it investigates how the average magnitude of change per unit time and the persistence of the changes (and hence the resulting autocorrelation of the environmental time series) affect the change tolerances, population diversities, and extinction timescales that emerge. Although it is the change persistence (often discussed in terms of environmental noise color) that has received most attention in the recent literature, other factors, particularly the average change magnitude, interact with this and can be more important drivers of the adaptive strategies that emerge. Moreover, when running simulations, the choice of change representation and normalization can also affect the outcomes. Detailed simulations are presented that are designed to explore all these issues. They also reveal significant dependences on the associated mutation rates and the extent to which they can evolve, and they clarify how evolution often leads populations into strategies with higher risks of extinction. Overall, this study shows how modeling the effect of environmental change requires more care than may have previously been realized.

这项研究使用进化模拟来探索新出现的策略，使人们能够在终身学习方法无法适应的情况下应对随机环境变化。特别是，它研究了每单位时间变化的平均幅度和变化的持续性（以及由此产生的环境时间序列的自相关）如何影响出现的变化容忍度、种群多样性和灭绝时间尺度。尽管在最近的文献中最受关注的是变化的持久性（通常用环境噪声颜色来讨论），但其他因素，特别是平均变化幅度，与此相互作用，并且可能是出现的自适应策略的更重要的驱动因素。此外，在运行模拟时，变化表示和标准化的选择也会影响结果。详细的模拟旨在探索所有这些问题。他们还揭示了对相关突变率及其进化程度的显着依赖性，并阐明了进化通常如何导致种群采取具有更高灭绝风险的策略。总体而言，这项研究表明，对环境变化的影响进行建模需要比以前意识到的更多的关注。