Quantifying fixed individual heterogeneity in demographic parameters: Performance of correlated random effects for Bernoulli variables
Quantifying fixed individual heterogeneity in demographic parameters: Performance of correlated random effects for Bernoulli variables
复制标题
DOI：
10.1111/2041-210x.13728
复制
发表时间：
2021-10-27
影响因子：
6.6
通讯作者：
Saether, Bernt-Erik
中科院分区：
文献类型：
--
作者：
Fay, Remi;Authier, Matthieu;Saether, Bernt-Erik
越来越多的实证研究旨在量化人口参数的个体差异，因为这些模式是进化和生态过程的关键。估计个体异质性的先进方法现在使用具有相关个体随机效应的多元正态分布来解释个体内发生的不同人口统计参数之间的潜在相关性。尽管经常使用多元混合模型，我们缺乏评估其可靠性时，适用于伯努利变量。使用模拟，我们估计了多变量混合效应模型的可靠性，用于估计相关的固定个体异质性的人口统计学参数与伯努利分布建模。我们评估了一系列情景中估计值的偏差和精度，这些情景调查了生活史策略的影响，个体异质性水平以及时间变化和状态依赖性的存在。我们还比较了不同抽样设计的估计值，以评估研究持续时间、监测人数和检测概率的重要性。在许多模拟场景中，相关随机效应的估计值存在偏倚且不精确，这突出了估计伯努利变量相关随机效应的挑战。固定的个体异质性的数量经常被高估，随机效应之间的相关性的绝对值几乎总是被低估。模拟还显示了根据所考虑的情况的混合模型的对比性能。一般而言，生活节奏越慢，固定个体异质性越大，样本量越大，估计偏差越小，精度越高。我们提供的个人异质性的经验调查使用相关的随机效应，根据物种的生活史策略，以及，研究人员可用的数据的数量和结构的指导方针。在解释用伯努利分布建模的人口统计学参数中相关个体随机效应的结果时，需要谨慎。由于偏差随取样设计和生活史而变化，物种间个体异质性的比较具有挑战性。这里讨论的问题并不是特定于人口统计学的，这一警告与所有研究领域都相关，包括行为和进化研究。
An increasing number of empirical studies aim to quantify individual variation in demographic parameters because these patterns are key for evolutionary and ecological processes. Advanced approaches to estimate individual heterogeneity are now using a multivariate normal distribution with correlated individual random effects to account for the latent correlations among different demographic parameters occurring within individuals. Despite the frequent use of multivariate mixed models, we lack an assessment of their reliability when applied to Bernoulli variables. Using simulations, we estimated the reliability of multivariate mixed effect models for estimating correlated fixed individual heterogeneity in demographic parameters modelled with a Bernoulli distribution. We evaluated both bias and precision of the estimates across a range of scenarios that investigate the effects of life-history strategy, levels of individual heterogeneity and presence of temporal variation and state dependence. We also compared estimates across different sampling designs to assess the importance of study duration, number of individuals monitored and detection probability. In many simulated scenarios, the estimates for the correlated random effects were biased and imprecise, which highlight the challenge in estimating correlated random effects for Bernoulli variables. The amount of fixed among-individual heterogeneity was frequently overestimated, and the absolute value of the correlation between random effects was almost always underestimated. Simulations also showed contrasting performances of mixed models depending on the scenario considered. Generally, estimation bias decreases and precision increases with slower pace of life, large fixed individual heterogeneity and large sample size. We provide guidelines for the empirical investigation of individual heterogeneity using correlated random effects according to the life-history strategy of the species, as well as, the volume and structure of the data available to the researcher. Caution is warranted when interpreting results regarding correlated individual random effects in demographic parameters modelled with a Bernoulli distribution. Because bias varies with sampling design and life history, comparisons of individual heterogeneity among species is challenging. The issue addressed here is not specific to demography, making this warning relevant for all research areas, including behavioural and evolutionary studies.