10.5061/DRYAD.8H36T01
Ketz, Alison C.
Colorado State University
Johnson, Therese L.
National Park Service
Hooten, Mevin B.
Colorado State University
Hobbs, M. Thompson
Colorado State University
Data from: A hierarchical Bayesian approach for handling missing
classification data
Dryad
dataset
2019
N-mixture model
elk
Cervus elaphus nelsoni
Hierarchical Bayesian Statistics
Population size
2019-03-22T14:00:52Z
2019-03-22T14:00:52Z
en
https://doi.org/10.1002/ece3.4927
197591 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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 (Cervus elaphus nelsoni) 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.
Data from: A hierarchical Bayesian approach for handling missing
classification dataDatasets consist of classifications and counts of elk
in Rocky Mountain National Park, and Estes Valley, CO. Data are separated
into CSV files by each year of the study, except aggregated ground counts
for the entire study. Other datasets include year separated transect level
group classification counts, and the auxiliary data from yearling and
adult female groups isolated from the overall transect level group
counts.Data_Ketz.zip
Southwest US