Conference Agenda

Biodiversity underpins life on Earth, yet it is declining at an accelerating pace, sharpening the need for interventions that can slow, halt, or reverse these losses. Designing such interventions requires clear insight into the processes driving population declines in particular species—and into the relative importance of those processes—insight most directly generated by population dynamics models. Yet appropriate population dynamics models for quantifying declines and guiding conservation management of wild herbivore populations remain scarce, leaving a critical gap in both evidence and practice. To address this gap, we develop an integrated Bayesian state-space population dynamics model, using the Mara-Serengeti hartebeest population as a case study. The model extends and generalizes an earlier framework we developed and illustrated for the Mara-Serengeti topi (Mukhopadhyay et al. 2024), adding multiple features designed to improve realism, inference, and management relevance.

The model fuses ground demographic surveys with aerial monitoring data, explicitly representing population age–sex structure and key life-history traits and strategies. It links birth rates, age-specific survival rates, and sex ratios to meteorological covariates, prior population density, environmental seasonality, predation risk, and several environmental and anthropogenic covariates. Operating on a monthly time step, it enables fine-grained estimation of reproductive seasonality, phenology, synchrony, and birth prolificacy, as well as juvenile and adult recruitment dynamics. We evaluate performance using balanced bootstrap sampling and by comparing model predictions with empirical aerial estimates of population size. We perform detailed assessment of model robustness, including by checking for parameter redundancy, estimability and identifiability, performing sensitivity analysis of the priors and running multiple MCMC chains. Implemented as a hierarchical Bayesian model using MCMC methods for parameter estimation, prediction, and inference, the model reproduces several well-established features of the hartebeest population, including a steep and persistent decline, weakly seasonal births, and juvenile and adult recruitment patterns. The framework is general and flexible and easily adaptable for other species.

References Mukhopadhyay, S., Piepho, H. P., Bhattacharya, S., Dublin, H. T., & Ogutu, J. O. (2024). Hierarchical Bayesian integrated modeling of age-and sex-structured wildlife population dynamics. Journal of Agricultural, Biological and Environmental Statistics, 1-26.

Joseph O. Ogutu, Hans-Peter Piepho et al.

University of Hohenheim, Institute of Crop Science, Biostatistics Unit, Fruwirthstrasse 23, 70599 Stuttgart, Germany