Waples Robin S
Northwest Fisheries Science Center National Marine Fisheries Service, National Oceanic and Atmospheric Administration Seattle Washington USA.
Ecol Evol. 2023 Nov 20;13(11):e10647. doi: 10.1002/ece3.10647. eCollection 2023 Nov.
Variance in reproductive success (, with = number of offspring) plays a large role in determining the rate of genetic drift and the scope within which selection acts. Various frameworks have been proposed to parse factors that contribute to , but none has focused on age-specific values of , which indicate the degree to which reproductive skew is overdispersed (compared to the random Poisson expectation) among individuals of the same age and sex. Instead, within-age effects are generally lumped with residual variance and treated as "noise." Here, an ANOVA sums-of-squares framework is used to partition variance in annual and lifetime reproductive success into between-group and within-group components. For annual reproduction, the between-age effect depends on age-specific fecundity ( ), but relatively few empirical data are available on the within-age effect, which depends on . By defining groups by age-at-death rather than age, the same ANOVA framework can be used to partition variance in lifetime reproductive success (LRS) into between-group and within-group components. Analytical methods are used to develop null-model expectations for random contributions to within-group and between-group components. For analysis of LRS, random variation in longevity appears as part of the between-group variance, and effects (if any) of skip breeding and persistent individual differences contribute to the within-group variance. Simulations are used to show that the methods for variance partitioning are asymptotically unbiased. Practical application is illustrated with empirical data for annual reproduction in American black bears and lifetime reproduction in Dutch great tits. Results show that overdispersed within-age variance (1) dominates annual in both male and female black bears, (2) is the primary factor that reduces annual effective size to a fraction of the number of adults, and (3) represents most of the opportunity for selection. In contrast, about a quarter of the variance in LRS in great tits can be attributed to random variation in longevity, and most of the rest is due to modest differences in fecundity with age estimated for a single cohort of females. R code is provided that reads generic input files for annual and lifetime reproductive success and allows users to conduct variance partitioning with their own data.
繁殖成功率的方差（，其中=后代数量）在决定遗传漂变的速率以及选择作用的范围方面起着很大的作用。已经提出了各种框架来剖析导致的因素，但没有一个框架关注特定年龄的 值，该值表明繁殖偏态在同一年龄和性别的个体中过度分散（与随机泊松期望相比）的程度。相反，年龄内效应通常与剩余方差合并，并被视为“噪声”。在这里，使用方差分析平方和框架将年度和终生繁殖成功率的方差划分为组间和组内成分。对于年度繁殖，年龄间效应取决于特定年龄的繁殖力（），但关于年龄内效应的实证数据相对较少，年龄内效应取决于 。通过按死亡年龄而非年龄定义组，可以使用相同的方差分析框架将终生繁殖成功率（LRS）的方差划分为组间和组内成分。使用分析方法来建立组内和组间成分随机贡献的零模型期望。对于LRS分析，寿命的随机变化表现为组间方差的一部分，跳过繁殖的影响（如果有的话）和持续的个体差异会导致组内方差。模拟结果表明，方差划分方法是渐近无偏的。通过美国黑熊年度繁殖和荷兰大山雀终生繁殖的实证数据说明了实际应用。结果表明，过度分散的年龄内方差（1）在雄性和雌性黑熊的年度中占主导地位，（2）是将年度有效规模降低到成年个体数量一小部分的主要因素，（3）代表了大部分选择机会。相比之下，大山雀LRS方差的约四分之一可归因于寿命的随机变化，其余大部分归因于对单一组雌性估计的繁殖力随年龄的适度差异。提供了R代码，该代码读取年度和终生繁殖成功率的通用输入文件，并允许用户使用自己的数据进行方差划分。
