Are the numbers adding up? Exploiting discrepancies among complementary population models.

Area of Science:

• Ecology
• Conservation Biology
• Statistical Modeling

Background:

• Monitoring large carnivores is challenging due to sparse distribution, human sensitivity, and complex life histories, leading to data uncertainty.
• Integrated population models (IPMs) can improve inference by jointly analyzing diverse data sets, but unobserved processes remain a challenge.
• Existing methods struggle to quantify bias and uncertainty in population data for species with complex dynamics.

Purpose of the Study:

• To develop a flexible Bayesian hierarchical modeling approach for integrated population models (IPMs).
• To reconcile annual population counts with demographic data (recruitment, survival) while accounting for unobserved processes.
• To apply the model to gray wolf populations in Wisconsin to assess population dynamics in relation to policy changes.

Main Methods:

• Developed a Bayesian hierarchical modeling framework for IPMs.
• Reconciled annual population counts with recruitment and survival data.
• Fitted density-dependent responses for each demographic process and estimated discrepancies representing unobserved additions/removals.

Main Results:

• The model successfully reconciled population counts and demographic data for gray wolves.
• Estimated annual mortality rate discrepancies of 0% (1980-1995), -2% (1996-2002), and 4% (2003-2011) in Wisconsin.
• Observed increases in mortality discrepancies in later years may relate to density dependence, illegal killing, and management shifts.

Conclusions:

• Integrated population models, enhanced by Bayesian hierarchical approaches, provide crucial insights into unobserved ecological processes.
• The developed modeling framework is generalizable for analyzing population dynamics and identifying impacts of external drivers like policy changes.
• This approach improves the understanding of population trends and viability for challenging-to-monitor species.