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Dynamic population models with temporal preferential sampling to infer phenology

June 10, 2023

To study population dynamics, ecologists and wildlife biologists typically use relative abundance data, which may be subject to temporal preferential sampling. Temporal preferential sampling occurs when the times at which observations are made and the latent process of interest are conditionally dependent. To account for preferential sampling, we specify a Bayesian hierarchical abundance model that considers the dependence between observation times and the ecological process of interest. The proposed model improves relative abundance estimates during periods of infrequent observation and accounts for temporal preferential sampling in discrete time. Additionally, our model facilitates posterior inference for population growth rates and mechanistic phenometrics. We apply our model to analyze both simulated data and mosquito count data collected by the National Ecological Observatory Network. In the second case study, we characterize the population growth rate and relative abundance of several mosquito species in the Aedes genus. Supplementary materials accompanying this paper appear on-line.

Publication Year 2023
Title Dynamic population models with temporal preferential sampling to infer phenology
DOI 10.1007/s13253-023-00552-3
Authors Michael Schwob, Mevin B. Hooten, Travis Mcdevitt-Galles
Publication Type Article
Publication Subtype Journal Article
Series Title Journal of Agricultural, Biological, and Environmental Statistics
Index ID 70246237
Record Source USGS Publications Warehouse
USGS Organization National Wildlife Health Center