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Data from: A scalable integrated population model for estimating abundance for gamebird management

Winter, Veronica12; Diefenbach, Duane2; Lamp, Kevin2; Casalena, Mary Jo3; Smelter, Kyle1; Mowen, Andrew1; Buderman, Frances1

Published Mar 31, 2026 on Dryad. https://doi.org/10.5061/dryad.47d7wm3vg

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Abstract

Understanding population dynamics is critical for informed wildlife management decisions. However, the data required to estimate demographic parameters can be costly for agencies to acquire, and issues with model scalability often hinder efforts to gain insights into broad-scale population dynamics. In this study, we develop two Bayesian integrated population models (IPMs) to estimate demographic parameters for wild turkey populations (Meleagris gallopavo silvestris) in Pennsylvania—a valuable game species for which understanding trends in abundance is essential for determining sustainable harvest limits.

The first model, termed the Research IPM (R-IPM), uses data from a short-term, high-intensity project, incorporating telemetry data, band recoveries, and harvest information from specific Wildlife Management Units (WMUs). The second model, the Operational IPM (O-IPM), operates at a broader regional scale using routinely collected statewide data, with informed priors derived from the R-IPM posteriors. This approach allows us to estimate population parameters beyond the spatial and temporal boundaries of our intensive data collection.

Both the R-IPM and O-IPMs produced biologically reasonable estimates that align with previous research. The O-IPM achieved precision comparable to the R-IPM despite using less intensive data collection procedures, particularly when informed by priors from the R-IPM. Our comparison with a version of the O-IPM, using vague as opposed to informed priors, demonstrated that incorporating prior information substantially improved parameter precision, especially for juvenile females where data were limited.

Synthesis and application: Our O-IPM presents an efficient and practical approach for estimating wildlife population demographics, particularly in situations where data collection is limited. This study demonstrates how information from intensive, localized research can be leveraged to inform broader-scale management through strategic use of prior information. Our findings emphasize the importance of balancing model complexity with the scale of management interest. Achieving this balance enables wildlife managers to obtain reliable population estimates and make cost-effective management decisions across larger spatial extents than would be possible with intensive data collection alone.