Resting metabolic rate (RMR) is a potentially important axis of physiological adaptation to the thermal environment. However, our understanding of the causes and consequences of individual variation in RMR in the wild is hampered by a lack of data, as well as analytical challenges. RMR measurements in the wild are generally characterized by large measurement errors and a strong dependency on mass. The latter is problematic when assessing the ability of RMR to evolve independently of mass. Mixed models provide a powerful and flexible tool to tackle these challenges, but they have rarely been used to estimate repeatability of mass‐independent RMR from field data. We used respirometry to obtain repeated measurements of RMR in a long‐term study population of snow voles (Chionomys nivalis) inhabiting an environment subject to large circadian and seasonal fluctuations in temperature. Using both uni‐ and bivariate mixed models, we quantify individual repeatability in RMR and decompose repeatability into mass‐dependent and mass‐independent components, while accounting for measurement error. RMR varies among individuals, that is, is repeatable (R = .46) and strongly co‐varies with BM. Indeed, much of the repeatability of RMR is attributable to individual variation in BM, and the repeatability of mass‐independent RMR is reduced by 41% to R = .27. These empirical results suggest that the evolutionary potential of RMR independent of mass may be severely constrained. This study illustrates how to leverage bivariate mixed models to model field data for metabolic traits, correct for measurement error and decompose the relative importance of mass‐dependent and mass‐independent physiological variation.

中文翻译：

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雪田鼠野生种群静息代谢率的有限质量独立个体变异（Chionomys nivalis）

静息代谢率 (RMR) 是生理适应热环境的一个潜在的重要轴。然而，由于缺乏数据以及分析挑战，我们对野外 RMR 个体变异的原因和后果的理解受到阻碍。野外 RMR 测量通常具有测量误差大和对质量的强烈依赖性的特点。在评估 RMR 独立于质量进化的能力时，后者是有问题的。混合模型提供了一种强大而灵活的工具来应对这些挑战，但它们很少用于从现场数据估计质量独立 RMR 的可重复性。我们使用呼吸测量法在长期研究的雪田鼠 (Chionomys nivalis) 种群中获得重复测量的 RMR，这些种群生活在一个昼夜节律和季节性温度波动较大的环境中。使用单变量和双变量混合模型，我们量化 RMR 中的单个重复性，并将重复性分解为质量相关和质量无关的组件，同时考虑测量误差。RMR 因人而异，即是可重复的 (R = .46) 并且与 BM 强烈协变。事实上，RMR 的大部分重复性归因于 BM 的个体差异，而与质量无关的 RMR 的重复性降低了 41%，R = .27。这些经验结果表明，与质量无关的 RMR 的进化潜力可能受到严重限制。