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

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.