Tree damage risk across gradients in tree species richness and stand age: Implications for adaptive forest management

Abstract

Forest disturbances are expected to increase in severity with climate change and intensified land use, threatening future delivery of several ecosystem services, including the climate-mitigating potential of forests. Alleviating these consequences through adaptive forest management demands a greater understanding of what drives the impacts of disturbances on forests, which, in turn, requires collection of high-quality data through large-scale and long-term monitoring programs. The Swedish National Forest Inventory has been recording “damages” on living trees across a forest area of 230,000 km², in addition to a wide range of stand characteristics and environmental conditions. Using 15 years of these data, we investigated the frequency of different types of tree damages and the causes of these damages and modeled damage risk among tree species and across gradients in stand attributes and environmental conditions. We found that 94% of all surveyed trees had some type of damage, but for 65% of these, the underlying cause was not identified. Nevertheless, for all damage types and causes, we found that damage risk varied considerably among tree species and across gradients in tree species richness, tree height, and stand age. For a few damages, stand age or tree species richness interacted with climate to influence risks. Among identified causes of damage, “wind and snow” was most common (11.9% of surveyed trees), followed by “forestry” (6.9%). Further, for most causes of damage where stand age was significant, the risk was highest in young or the youngest stands. As such, our results indicate that there is great potential for reducing the risk of tree damages via adaptive management, such as altered tree species composition and increased rotation length. However, for a greater understanding of what is driving the frequency and magnitude of forest damages, and to be able to provide specific, useful information to stakeholders, collection of higher-quality data must be prioritized by monitoring programs.