Predicting the growth of a small introduced muskox population using population prediction intervals

1. A key issue in ecology is the prediction of future population fluctuations. Such population predictions are fundamental for population-viability analysis and are essential for assessing the implications of various management actions. Development of reliable population predictions is however, difficult because it requires estimation and modelling of the separate effects of the deterministic components of the population dynamics as well as the stochastic influences on the population fluctuations. Here we model the stochastic dynamics of an introduced population of muskox Ovibos moschatus in the Dovrefjell mountains of central Norway, using a simple model without density regulation. Our aim is to examine quantitatively factors affecting the accuracy of the population projections by applying the concept of Population Prediction Interval (PPI). 2. The long-term growth rate was (s) over cap = 0.0511, assuming no density dependence. The environmental variance was relatively large ((sigma) over cap (2)(e)= 0.0159). This gives a deterministic growth rate of r = 0.0591. However, accounting for losses due to various kinds of human activities resulted in a nearly doubling of s (% = 0.0980). 3. Autumn temperature and late winter snow depth were each able to explain a significant proportion of the annual variation in population growth rates. 4. The impact of environmental stochasticity made the PPI wide after only a few years. Uncertainties in the estimates of the population parameters were quite small and had a minor impact on the PPI. 5. A sensitivity analysis showed that ignoring demographic stochasticity led to an overestimate of the environmental variance sigma(2)(e), but that the impact on the width of the PPI was small. 6. This study shows that reliable projections of future population growth, even based on simple population models without density regulation, are dependent on assessment of the accuracy in the population predictions that must be based on estimating and modelling the stochastic influences on the population dynamics.