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Dynamic Population Models with Temporal Preferential Sampling to Infer Phenology

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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.

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Acknowledgements

This research was supported by the National Science Foundation (NSF) Graduate Research Fellowship Program and was conducted as part of the “Forecasting Mosquito Phenology in a Shifting Climate: Synthesizing Continental-scale Monitoring Data” Working Group, which is supported by the John Wesley Powell Center for Analysis and Synthesis and funded by the U.S. Geological Survey. Data were collected by the National Ecological Observatory Network (NEON) and the Oregon State PRISM Climate Group. NEON is a program sponsored by the NSF and operated under cooperative agreement by Battelle. This material is based in part upon work supported by the NSF through the NEON Program. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

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Authors and Affiliations

  1. Department of Statistics and Data Sciences, The University of Texas at Austin, Austin, TX, USA

    Michael R. Schwob & Mevin B. Hooten

  2. National Wildlife Health Center, United States Geological Survey, Madison, WI, USA

    Travis McDevitt-Galles

Authors
  1. Michael R. Schwob
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  2. Mevin B. Hooten
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  3. Travis McDevitt-Galles
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Correspondence to Michael R. Schwob.

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Data Availability

The datasets analyzed during this study are publicly available from the National Ecological Observatory Network (DOI: dp1.10043.001) and PRISM climate group (https://prism.oregonstate.edu/). The code and data used for the case studies can be found at https://github.com/michaelschwob/DPMwTPS.

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Schwob, M.R., Hooten, M.B. & McDevitt-Galles, T. Dynamic Population Models with Temporal Preferential Sampling to Infer Phenology. JABES 28, 774–791 (2023). https://doi.org/10.1007/s13253-023-00552-3

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  • DOI: https://doi.org/10.1007/s13253-023-00552-3

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