Predicting forest damage using relative abundance of multiple deer species and national forest inventory data

Deer populations are growing unsustainably in many human modified landscapes throughout the world. Deer, both native and non-native, at high densities can damage forest ecosystems impacting biodiversity and ecological functioning at multiple levels and inflict large ecological and economic costs. The ecological drivers of forest damage and the roles of single and multiple co-occurring deer species is not well understood due to a lack of coordinated high resolution deer distribution, deer abundance and forest damage data. Here, we aim to disentangle the relationship between forest damage, forest characteristics and the roles deer play in damaging forest ecosystems. To achieve this, we adopted a novel approach integrating recent high resolution deer distribution data for multiple deer species (native and non-native) and combining it with forest inventory data to provide risk scenario predictions for practitioners to use on a national scale. We found bark stripping damage was more common in mature monocultures with higher levels of stocking number, moss cover and sika relative abundance. Fraying, like bark stripping, was more common in areas with higher levels of stocking number, moss cover and sika relative abundance, while the interaction between red and fallow deer also influenced the likelihood of fraying damage with damage more common in areas of higher densities of either red or fallow but not in areas with high densities
of both species. In contrast, browsing damage was more common in young natural woodlands with higher levels of grass cover and species richness. The interactions between the native red and other non-native species also has an influence on the likelihood of browsing damage with damage more common in areas of higher densities of either red or fallow and red or sika but not in areas with high densities of co-occurring species. Finally, we produced risk scenarios of forest damage by co-occurring deer species and precisely predicted where damage is likely to occur on a national scale. We predicted high levels of damage in sika and/or red deer hotspots, matching areas of highly concentrated deer distributions. This study highlights the ecological drivers and the role that co-occurring native and non-native deer species have on forest damage within a large spatial scale. By combining reliable species distribution models with the national forest inventory data, we can now provide a useful tool for practitioners to
help alleviate and mitigate forest damage and human wildlife conflicts.