Hierarchical Bayesian state-space modeling of age- and sex-structured wildlife population dynamics

Abstract: Biodiversity is declining at alarming rates worldwide, including for large wild mammals. It is therefore imperative to develop effective population conservation and recovery strategies. Population dynamics models can provide insights into processes driving declines of particular populations of a species and their relative importance. We develop an integrated Bayesian state-space population dynamics model for wildlife populations and illustrate it using a topi population inhabiting the Masai Mara Ecosystem in Kenya. The model is general and integrates ground demographic survey with aerial survey monitoring data. It incorporates population age- and sex-structure and life-history traits and relates birth rates, age-specific survival rates and sex ratio with meteorological covariates, prior population density, environmental seasonality and predation risk. The model runs on a monthly time step, enabling accurate characterization of reproductive seasonality, phenology, synchrony and prolificacy of births and juvenile recruitment. Model performance is evaluated using balanced bootstrap sampling and comparing predictions with aerial population size estimates. The model is implemented using MCMC methods and reproduces several well-known features of the Mara topi population, including striking and persistent population decline, seasonality of births and juvenile recruitment. It can be readily adapted for other wildlife species and extended to incorporate several additional useful features.