Estimation of deer population dynamics using a bayesian state-space model with multiple abundance indices

Abstract

Deer population control is important in wildlife management, because overabundance of deer is a problem worldwide. For practical deer population control, deer population dynamics and the factors that influence them need to be evaluated in low-cost and time-efficient ways. However, in traditional methods of estimation, such as cohort analysis, large numbers of deer need to be caught for many years, and the ages of the deer must be determined. We estimated deer population dynamics by using a Bayesian state-space model with multiple deer abundance indices (seen deer per unit effort, pellet group count, and block count) and numbers of deer hunted and culled in Yamanashi Prefecture, central Japan. In the state process of our state-space model, latent deer abundance at year t in location m (Dt,m), with m being each cell of a grid mesh covering Yamanashi Prefecture, was assumed to decrease annually through hunting and culling, to increase with the population growth rate of each mesh (rm; which was determined from the percentages of forest, evergreen forest, and artificial grassland), and to fluctuate stochastically. In the observation process, Dt,m was assumed to be correlated with the deer abundance indices and a Gaussian white noise in the deer abundance indices. The estimated Dt,m was correlated with each deer abundance index, but the correlation coefficient was the greatest for pellet group density. The percentage of hunted and culled deer needed to reach 30% to reduce the annual growth rate (Dt,m/Dt−1,m). Increasing the percentage of artificial grassland increased rm. Our results showed that 1) deer abundance could be estimated by using only deer abundance indices in addition to population growth rate and the percentage of hunted and culled deer; and 2) preventing the intrusion of deer onto artificial grassland and intensive culling on artificial grassland were important to decrease deer abundance. © 2013 The Wildlife Society.