Understanding the scales at which environmental variability affects populations is critical for projecting population dynamics and species distributions in rapidly changing environments. Here we used a multilevel Bayesian analysis of range‐wide survey data for Adélie penguins to characterize multidecadal and annual effects of sea ice on population growth. We found that mean sea ice concentration at breeding colonies (i.e., “prevailing” environmental conditions) had robust nonlinear effects on multidecadal population trends and explained over 85% of the variance in mean population growth rates among sites. In contrast, despite considerable year‐to‐year fluctuations in abundance at most breeding colonies, annual sea ice fluctuations often explained less than 10% of the temporal variance in population growth rates. Our study provides an understanding of the spatially and temporally dynamic environmental factors that define the range limits of Adélie penguins, further establishing this iconic marine predator as a true sea ice obligate and providing a firm basis for projection under scenarios of future climate change. Yet, given the weak effects of annual sea ice relative to the large unexplained variance in year‐to‐year growth rates, the ability to generate useful short‐term forecasts of Adélie penguin breeding abundance will be extremely limited. Our approach provides a powerful framework for linking short‐ and longer term population processes to environmental conditions that can be applied to any species, facilitating a richer understanding of ecological predictability and sensitivity to global change.

中文翻译：

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海冰预测了阿德利企鹅种群增长的长期趋势，而不是年度波动：一项广泛的多尺度分析的结果。

了解环境可变性影响种群的规模对于预测快速变化的环境中的种群动态和物种分布至关重要。在这里，我们使用了多级贝叶斯分析法对阿德利企鹅进行范围广泛的调查，以表征海冰对人口增长的十年和年度影响。我们发现，繁殖群体的平均海冰浓度（即“普遍”的环境条件）对数十年人口趋势具有强烈的非线性影响，并解释了站点间平均人口增长率的85％以上的变化。相比之下，尽管大多数繁殖群体的丰度逐年出现较大波动，但每年的海冰波动通常只能解释不到人口增长率随时间变化的10％。我们的研究提供了对定义Adélie企鹅范围限制的时空动态环境因素的理解，进一步确立了这种标志性海洋捕食者为真正的海冰专长，并为未来气候变化场景下的预测提供了坚实的基础。但是，鉴于每年海冰的影响相对于无法解释的逐年较大的变化所产生的影响较弱，因此生成有用的短期Adélie企鹅繁殖数量预报的能力将非常有限。我们的方法提供了一个强大的框架，可将短期和长期的种群过程与可应用于任何物种的环境条件联系起来，从而有助于人们对生态学的可预测性和对全球变化的敏感性有更深入的了解。