Quantitative estimates of tree species selectivity by moose (Alces alces) in a forest landscape

Abstract:
An extensive literature is available on browsing preference for certain tree species. However, useful predictive tools for estimating the impact of deer on forests production and biodiversity can still be improved. A step in that direction is not only to rank preference among tree species but also to quantify the relative risk of being browsed. The foraging selectivity of moose was evaluated using three different statistical methods developed to study habitat utilization. The general pattern for the three methods was consistent. From the results, groups of forage species were clustered and a quantitative index of selectivity was calculated for the groups. The selectivity index showed that rowan (Sorbus aucuparia), willow (Salix ssp.) and aspen (Populus tremula) had a 14 times higher probability of being browsed than a group consisting of Scots pine (Pinus sylvestris) and downy birch (Betula pubescens), while juniper (Juniperus communis) and silver birch (Betula pendula) had a 3.5 times higher probability than Scots pine and downy birch. Since the most preferred species were the least abundant, one should be cautious about the generality of the index between areas, as it may indicate that preference depends on plant species composition. The method used can easily be applied in forest management. Information on quantitative selectivity indices may improve the possibility of managing moose in accordance with acceptable browsing damage.