A Bayesian state-space model for estimating wild boar dynamic population in a hunting estate

This paper deals with a Bayesian state-space model to estimate wild boar abundance and demographic parameters such as survival rates, intrinsic growth rates and hunting efficiency for a Mediterranean hunting estate. Based on annual wild boar captures from 1995 to 2008, a Bayesian model is built where population abundance is estimated by a reverse-time multinomial Cormack-Jolly- Seber model. In this model, monthly maximum and minimum temperature average, also drought- constrained, frost-constrained and non-constrained bioclimatic intensity are used as predictive factors of the population birth-rate and intrinsic growth. As a result, a wild board population dynamic model was obtained which simulates hunting activities and estimates hidden demographic parameters. These outcomes are useful to develop better hunting management plans, where future captures are determined according to actual climatic features.