Successful by chance? the power of mixed models and neutral simulations for the detection of individual fixed heterogeneity in fitness components

Heterogeneity in fitness components consists of fixed heterogeneity due to latent differences fixed throughout life (e.g. genetic variation), and dynamic heterogeneity generated by stochastic variation. Their relative magnitude is crucial for evolutionary processes, as only the former may allow for adaptation. However, the importance of fixed heterogeneity in small populations has recently been questioned. Using neutral simulations (NS), several studies failed to detect fixed heterogeneity, thus challenging previous results from mixed models (MM). To understand the causes of this discrepancy, we estimate the statistical power and false positive rate of both methods, and apply them to empirical data from a wild rodent population. While MM show high false positive rates if confounding factors are not accounted for, they have high statistical power to detect real fixed heterogeneity. In contrast, NS are also subject to high false positive rates, but have always low power. Indeed, MM analyses of the rodent population data show significant fixed heterogeneity in reproductive success, whereas NS analyses do not. We suggest that fixed heterogeneity may be more common than is suggested by NS, and that NS are useful only if more powerful methods are not applicable and if they are complemented by a power analysis.

适应度组分的异质性可分为两类：一类是由终身固定的潜在差异（例如遗传变异）导致的固定异质性，另一类是由随机变异产生的动态异质性。二者的相对强度对进化过程至关重要，因为仅固定异质性可促成适应性演化。然而，近期有研究对小种群中固定异质性的重要性提出了质疑。多项研究采用中性模拟（Neutral Simulations, NS）未能检测到固定异质性，这对混合模型（Mixed Models, MM）的既往研究结果构成了挑战。为厘清这一矛盾的成因，我们评估了两种方法的统计效力与假阳性率，并将其应用于野生啮齿动物种群的实测数据。若未考虑混杂因素，混合模型会出现较高的假阳性率，但该方法具备检测真实固定异质性的高统计效力。与之相反，中性模拟同样存在较高的假阳性率，且统计效力始终偏低。事实上，对啮齿动物种群数据的混合模型分析显示，其繁殖成功率存在显著的固定异质性，而中性模拟分析则未检测到该现象。我们认为，固定异质性的普遍性可能高于中性模拟所揭示的结果；仅当无法使用效力更强的方法，且中性模拟辅以效力分析时，该方法才具备应用价值。