Reconstructing Rapid Saiga Population Recovery with Diagnostic Bayesian Integrated Population Models

The rapid recovery of wildlife populations can outpace the assumptions of integrated population models and make visually plausible reconstructions difficult to distinguish from diagnostically supported inference. We evaluated this problem for Kazakhstan saiga antelope (Saiga tatarica) using a Bayesian model-ladder sensitivity audit for the Betpak-Dala, Ustyurt, and Ural populations from 1980 to 2025. We fitted a climate-aware, age- and sex-structured baseline model family M0 (climate-aware static-K baseline comparator) and compared it with dynamic carrying-capacity, observation-regime, hierarchical-prior, joint-hierarchical, non-centred, and sampling-optimised development families using predefined-convergence, effective-sample-size, posterior-predictive, and observed-to-latent mismatch gates. The baseline reconstruction captured the broad collapse–recovery dynamics but systematically underpredicted the observed abundance in 2021–2025 across all three populations. The strongest development family M4b R2 (sampling-optimised joint-hierarchical development family) improved the R-hat values and late-series alignment, with 2025 observed-to-latent ratios of approximately 1.77, 1.43, and 1.49 for Betpak-Dala, Ustyurt, and Ural, respectively, but the effective sample sizes remained below the gate of 400. The study therefore provides a transparent diagnostic reconstruction and model-development audit rather than management-ready Bayesian integrated population model (IPM) inference, identifying where the current baseline fails and which structural directions require further validation.