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Melded Integrated Population Models

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Abstract

Integrated population models provide a framework for assimilating multiple datasets to understand population dynamics. Understanding drivers of demography is key to improving wildlife management, and integrated population models have informed conservation practices for many species of conservation concern. Motivated by multiple surveys of lesser prairie-chicken (Tympanuchus pallidicinctus), we developed a flexible integrated population modeling framework for assimilating demographic data with multiple surveys of abundance. Measurements of abundance are derived from aerial and ground surveys that vary in their observational uncertainty, sampling design, temporal coverage, and survey effort. Our proposed integrated population model draws from the strengths of each survey and prevents their sampling biases from compromising inference. We facilitate posterior inference for our integrated population model using chained Markov melding, which induces the joint distribution for all data sources by linking inference across several submodels. Using Markov melding, we extend the modeling framework previously proposed for analyzing the individual data sources while still obtaining joint Bayesian inference. We fit the melded model with a multistage Markov chain Monte Carlo algorithm that decreases run time and improves mixing. We assimilate data from several state and federal wildlife agencies and over a dozen independent researchers to infer lesser prairie-chicken abundance and vital rates across its entire range over the last 18 years. Supplementary materials accompanying this paper appear online.

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Acknowledgements

We thank C. Rewa and the USDA NRCS for support and funding. We thank landowners in Colorado, Kansas, Oklahoma, Texas, and New Mexico for allowing access on their properties to conduct the ground surveys and demographic surveys. We thank B. Cooper and Oklahoma Department of Wildlife Conservation (ODWC); J. Reitz, T. Verquer, J. Yost, and Colorado Parks and Wildlife (CPW); E. Teige, D. Peterson, and Kansas Department Wildlife and Parks (KDWP); Texas Parks and Wildlife Department (TPWD); Natural Heritage New Mexico and New Mexico Department of Game and Fish (NMDGF) for contributing the ground data. We thank S. Carleton, C. Strong, A. Meyers, Alex Kunkel, New Mexico State University, and the Nature Conservancy; S. Harryman and Texas Tech University; Jonathan Lautenbach, Joseph Lautenbach, N. Parker, S. Robinson and Kansas State University for providing demographic data. We thank an anonymous reviewer and editor for providing suggestions to improve the manuscript. We acknowledge the assistance of the WEST crew members and pilots. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Funding

The aerial and ground surveys have been financed, in part, with Federal aide from the U.S. Fish and Wildlife Service, a division of the U.S. Department of Interior, and administered by KDWP, ODWC, TPWD, and NMDGF. The contents and opinions, however, do not necessarily reflect the views or policies of the U.S. Department of Interior or the five state wildlife agencies, but does represent the views of the U.S. Geological Survey. Funding for methodological aspects of this research was also provided by the National Science Foundation (NSF 2222525 and NSF 1927177 ).

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Authors and Affiliations

  1. Department of Statistics, Colorado State University, Fort Collins, CO, USA

    Justin J. Van Ee & Matthew D. Koslovsky

  2. Department of Fisheries, Wildlife and Conservation Sciences, Oregon State University, Corvallis, OR, USA

    Christian A. Hagen

  3. Bird Conservancy of the Rockies, Fort Collins, CO, USA

    David C. Pavlacky Jr.

  4. Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, CO, USA

    David C. Pavlacky Jr.

  5. U.S. Geological Survey, Kansas Cooperative Fish and Wildlife Research Unit, Division of Biology, Kansas State University, Manhattan, KS, USA

    David A. Haukos

  6. Department of Biology, New Mexico State University, Las Cruces, NM, USA

    Andrew J. Lawrence

  7. Caesar Kleberg Wildlife Research Institute, Texas A &M University-Kingsville, Kingsville, TX, USA

    Ashley M. Tanner

  8. Department of Natural Resource Management, Texas Tech University, Lubbock, TX, USA

    Blake A. Grisham

  9. Kansas Department of Wildlife and Parks, Emporia, KS, USA

    Kent A. Fricke

  10. Colorado Parks and Wildlife, Steamboat Springs, CO, USA

    Liza G. Rossi

  11. New Mexico Department of Game and Fish, Portales, NM, USA

    Grant M. Beauprez

  12. Oklahoma Department of Wildlife Conservation, Oklahoma City, OK, USA

    Kurt E. Kuklinski

  13. Texas Parks and Wildlife Department, Canyon, TX, USA

    Russell L. Martin

  14. Western EcoSystems Technology, Inc., Cheyenne, WY, USA

    Troy B. Rintz

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

    Mevin B. Hooten

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  1. Justin J. Van Ee
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  2. Christian A. Hagen
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  3. David C. Pavlacky Jr.
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Correspondence to Justin J. Van Ee.

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Van Ee, J.J., Hagen, C.A., Pavlacky, D.C. et al. Melded Integrated Population Models. JABES (2024). https://doi.org/10.1007/s13253-024-00620-2

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