Fuel load, stand structure, and understory species composition following prescribed fire in an old-growth coast redwood (Sequoia sempervirens) forest

Background With the prevalence of catastrophic wildfire increasing in response to widespread fire suppression and climate change, land managers have sought methods to increase the resiliency of landscapes to fire. The application of prescribed burning in ecosystems adapted to fire can reduce fuel load and fire potential while minimizing impacts to the ecosystem as a whole. Coast redwood forests have historically experienced fire from both natural and anthropogenic sources, and are likely to respond favorably to its reintroduction. Results Random sampling was conducted in three burned sites and in three unburned sites, in an old-growth coast redwood (Sequoia sempervirens [D. Don] Endl.) forest. Data were collected on fuel, forest structure, and understory species composition and compared between treatments. Downed woody fuel, duff depth, litter depth, and density of live woody fuels were found to be significantly lower on sites treated with fire compared to unburned sites. Density of the dominant overstory canopy species, coast redwood and Douglas-fir (Pseudotsuga menziesii var. menziesii [Mirb.] Franco), remained consistent between treatments, and the abundance of herbaceous understory plant species was not significantly altered by burning. In addition, both downed woody fuel and live fuel measures were positively correlated with time since last burn, with the lowest measures on the most recently burned sites. Conclusions Our results indicated that the use of prescribed burning in old-growth redwood forests can provide beneficial reductions in live and dead surface fuels with minimal impacts to overstory trees and understory herbaceous species.

Shade, light, and stream temperature responses to riparian thinning in second-growth redwood forests of northern California

Resource managers in the Pacific Northwest (USA) actively thin second-growth forests to accelerate the development of late-successional conditions and seek to expand these restoration thinning treatments into riparian zones. Riparian forest thinning, however, may impact stream temperatures–a key water quality parameter often regulated to protect stream habitat and aquatic organisms. To better understand the effects of riparian thinning on shade, light, and stream temperature, we employed a manipulative field experiment following a replicated Before-After-Control-Impact (BACI) design in three watersheds in the redwood forests of northern California, USA. Thinning treatments were intended to reduce canopy closure or basal area within the riparian zone by up to 50% on both sides of the stream channel along a 100–200 m stream reach. We found that responses to thinning ranged widely depending on the intensity of thinning treatments. In the watersheds with more intensive treatments, thinning reduced shade, increased light, and altered stream thermal regimes in thinned and downstream reaches. Thinning shifted thermal regimes by increasing maximum temperatures, thermal variability, and the frequency and duration of elevated temperatures. These thermal responses occurred primarily during summer but also extended into spring and fall. Longitudinal profiles indicated that increases in temperature associated with thinning frequently persisted downstream, but downstream effects depended on the magnitude of upstream temperature increases. Model selection analyses indicated that local changes in shade as well as upstream thermal conditions and proximity to upstream treatments explained variation in stream temperature responses to thinning. In contrast, in the study watershed with less intensive thinning, smaller changes in shade and light resulted in minimal stream temperature responses. Collectively, our data shed new light on the stream thermal responses to riparian thinning. These results provide relevant information for managers considering thinning as a viable restoration strategy for second-growth riparian forests.

High-Resolution Mapping of Redwood (Sequoia sempervirens) Distributions in Three Californian Forests

High-resolution maps of redwood distributions could enable strategic land management to satisfy diverse conservation goals, but the currently-available maps of redwood distributions are low in spatial resolution and biotic detail. Classification of airborne imaging spectroscopy data provides a potential avenue for mapping redwoods over large areas and with high confidence. We used airborne imaging spectroscopy data collected over three redwood forests by the Carnegie Airborne Observatory, in combination with field training data and application of a gradient boosted regression tree (GBRT) machine learning algorithm, to map the distribution of redwoods at 2-m spatial resolution. Training data collected from the three sites showed that redwoods have spectral signatures distinct from the other common tree species found in redwood forests. We optimized a gradient boosted regression model for high performance and computational efficiency, and the resulting model was demonstrably accurate (81–98% true positive rate and 90–98% overall accuracy) in mapping redwoods in each of the study sites. The resulting maps showed marked variation in redwood abundance (0–70%) within a 1 square kilometer aggregation block, which match the spatial resolution of currently-available redwood distribution maps. Our resulting high-resolution mapping approach will facilitate improved research, conservation, and management of redwood trees in California.