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Biodiversity is declining at alarming rates worldwide, including for largewild mammals. It is therefore imperative to develop effective populationconservation and recovery strategies. Population dynamics models can provideinsights into processes driving declines of particular populations of a speciesand their relative importance. We develop an integrated Bayesian state-spacepopulation dynamics model for wildlife populations and illustrate it using atopi population inhabiting the Masai Mara Ecosystem in Kenya. The model isgeneral and integrates ground demographic survey with aerial survey monitoringdata. It incorporates population age- and sex-structure and life-history traitsand relates birth rates, age-specific survival rates and sex ratio withmeteorological covariates, prior population density, environmental seasonalityand predation risk. The model runs on a monthly time step, enabling accuratecharacterization of reproductive seasonality, phenology, synchrony andprolificacy of births and juvenile recruitment. Model performance is evaluatedusing balanced bootstrap sampling and comparing predictions with aerialpopulation size estimates. The model is implemented using MCMC methods andreproduces several well-known features of the Mara topi population, includingstriking and persistent population decline, seasonality of births and juvenilerecruitment. It can be readily adapted for other wildlife species and extendedto incorporate several additional useful features.

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