Ketz Alison C, Johnson Therese L, Hooten Mevin B, Hobbs N Thompson
Natural Resource Ecology Lab Department of Ecosystem Science and Sustainability, and Graduate Degree Program in Ecology Colorado State University Fort Collins Colorado.
National Park Service Rocky Mountain National Park Estes Park Colorado.
Ecol Evol. 2019 Mar 2;9(6):3130-3140. doi: 10.1002/ece3.4927. eCollection 2019 Mar.
Ecologists use classifications of individuals in categories to understand composition of populations and communities. These categories might be defined by demographics, functional traits, or species. Assignment of categories is often imperfect, but frequently treated as observations without error. When individuals are observed but not classified, these "partial" observations must be modified to include the missing data mechanism to avoid spurious inference.We developed two hierarchical Bayesian models to overcome the assumption of perfect assignment to mutually exclusive categories in the multinomial distribution of categorical counts, when classifications are missing. These models incorporate auxiliary information to adjust the posterior distributions of the proportions of membership in categories. In one model, we use an empirical Bayes approach, where a subset of data from one year serves as a prior for the missing data the next. In the other approach, we use a small random sample of data within a year to inform the distribution of the missing data.We performed a simulation to show the bias that occurs when partial observations were ignored and demonstrated the altered inference for the estimation of demographic ratios. We applied our models to demographic classifications of elk () to demonstrate improved inference for the proportions of sex and stage classes.We developed multiple modeling approaches using a generalizable nested multinomial structure to account for partially observed data that were missing not at random for classification counts. Accounting for classification uncertainty is important to accurately understand the composition of populations and communities in ecological studies.
生态学家使用将个体分类的方式来理解种群和群落的组成。这些类别可能由人口统计学特征、功能性状或物种来定义。类别分配往往并不完美，但通常被当作无误差的观测值。当个体被观测到但未被分类时，这些“不完整”的观测值必须经过修正，以纳入缺失数据机制，从而避免错误推断。我们开发了两种层次贝叶斯模型，以克服在分类计数的多项分布中，当分类缺失时，对相互排斥类别进行完美分配的假设。这些模型纳入辅助信息，以调整类别成员比例的后验分布。在一个模型中，我们采用经验贝叶斯方法，将某一年的数据子集用作下一年缺失数据的先验。在另一种方法中，我们使用一年内的数据小随机样本，来为缺失数据的分布提供信息。我们进行了一项模拟，以展示忽略不完整观测值时出现的偏差，并证明在估计人口统计比率时推断的改变。我们将模型应用于麋鹿（）的人口统计分类，以证明在性别和阶段类别比例的推断上有所改进。我们使用可推广的嵌套多项结构开发了多种建模方法，以处理分类计数中并非随机缺失的部分观测数据。在生态研究中，考虑分类不确定性对于准确理解种群和群落的组成很重要。
