Bayesian state-space modelling of moose population dynamics
Speaker: Tuomas Kukko (Univ. of Jyväskylä, Finland)
Moose (Alces alces) is not only the most important game animal but also one of the leading causes of economic forest damages in Finland. Also, hundreds of moose-vehicle collisions take place every year. Hence, strict controlling of moose density is required. Knowledge of local moose stocks is essential in order to successfully manage the population. Our main goal is to construct a reliable statistical model for estimating the size and structure of local moose populations.
First, we present a concise binomial model for daily moose observations. We modify and compare the best estimation methods for binomial distribution’s parameters suggested in the literature. Finally, we apply the best method, a hierarchical Bayesian model, to Finnish moose hunting data. The observation data is collected annually by hunters during the hunting season.
Our primary approach is a more comprehensive Bayesian model for moose population dynamics. Several independent sources of data containing information of the population size and structure are combined within a state-space model framework. The data consists of daily observations and harvest records, harvest structure indices, estimated sex ratio and fecundity parameters, catch and observations per effort indices, and hunters’ estimates of post-harvest stock sizes. We apply the model to the hunting data from 1995—2010 resulting in annual estimates of population size and age-sex structure.