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

Abstract

AbstractHuman modification of landscape and natural resources have facilitated deer population irruptions across the world resulting in widespread human-wildlife conflicts. These conflicts occur across the field of natural resource management and negatively affect both the public and vested stakeholders when their livelihoods are placed at risk, for instance, the forestry sector. Deer, both native and non-native, at high densities can damage forest ecosystems impacting biodiversity and ecological functioning at multiple levels and can 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 adopt a novel approach integrating recent high resolution deer distribution data for multiple deer species (native and non-native) and combining them with forest inventory data collected in 1,681 sampling stations across Ireland to provide risk scenario predictions for practitioners to use on a national scale. Forest characteristics played a key role in the severity and type of damage risk that deer posed. We found all damage types were more prevalent in forests with greater tree densities where deer are more likely to find refuge from human disturbance. Bark stripping damage was more prevalent in mature forests with high tree diversity and ground level flora (e.g., bryophytes, herbs, and shrubs). Similarly, browsing damage was more prevalent in forests with greater tree richness but with understorey vegetation dominated by grass and ferns. Fraying damage was more common in mixed woodlands with understory dominated by bryophytes and grass. Crucially, we found that type and severity of forest damage were shaped by the interaction of multiple deer species occurring simultaneously, particularly at high densities, suggesting subtle inter-species competition and exclusion/partition dynamics that require further investigation to understand the ecological mechanism. Finally, we produce risk scenarios of forest damage by co-occurring deer species and precisely predict where damage is likely to occur on a national scale. We predict 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.