Dynamic Population Models with Temporal Preferential Sampling to Infer Phenology

Michael R. Schwob (), Mevin B. Hooten and Travis McDevitt-Galles
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Michael R. Schwob: The University of Texas at Austin
Mevin B. Hooten: The University of Texas at Austin
Travis McDevitt-Galles: United States Geological Survey

Journal of Agricultural, Biological and Environmental Statistics, 2023, vol. 28, issue 4, No 11, 774-791

Abstract: Abstract 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.

Keywords: Abundance; Ignorability; Mechanistic modeling; Phenometrics; Time series (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s13253-023-00552-3

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