Biodiversity benefits of prescribed fire and mechanical thinning in Yosemite Valley
摘要
Widespread tree mortality in the Sierra Nevada driven by drought, beetle outbreaks, fire, and other stressors has increased the need for effective forest management to mitigate hazardous fuel loads and restore ecosystem function. In Yosemite National Park, where resource-intensive restoration projects are underway, understanding how different treatments affect biodiversity and fuel reduction is essential. We compared four treatment histories in Yosemite Valley—prescribed fire, mechanical thinning, combined treatments, and areas with no treatment in the past 10 years—to compare their effects on understory plant diversity and evaluate the structural and compositional forest characteristics influencing Shannon diversity, as a metric of biodiversity, to help guide management.
ResultsUsing both field-measured data and terrestrial lidar scan (TLS) metrics, we found that all fuel treatments significantly reduced fuel cover and increased understory plant species richness relative to untreated areas, and that the combination of both treatments increased Shannon diversity of understory plant species. Together, post-treatment forest structure metrics from TLS and field-based vegetation assessments, including midstory canopy metrics, individual tree measurements, and measures of greenness, accounted for most of the variation in species diversity.
ConclusionsThese findings can guide future management practices in similar forests of the western USA where fuel loads are high and biodiversity protection is critical. Treatments that effectively reduced fuels also increased plant species diversity, likely through effects on canopy openness, tree diameter diversity, understory composition, and understory plant cover. Thus, we conclude that restoration projects using prescribed fire, mechanical thinning, and their combination are effective for protecting plant diversity and reducing fuel as compared to no treatment. Additionally, this work also highlights the value of integrating TLS with field methods for monitoring vegetation structure and composition.