Northern and high-latitude alpine treelines are generally thought to be limited by available warmth. Most studies of tree-growth-climate interaction at treeline as well as climate reconstructions using dendrochronology report positive growth response of treeline trees to warmer temperatures. However, population-wide responses of treeline trees to climate remain largely unexamined. We systematically sampled 1558 white spruce at 13 treeline sites in the Brooks Range and Alaska Range. Our findings of both positive and negative growth responses to climate warming at treeline challenge the widespread assumption that arctic treeline trees grow better with warming climate. High mean temperatures in July decreased the growth of 40% of white spruce at treeline areas in Alaska, whereas warm springs enhance growth of additional 36% of trees and 24% show no significant correlation with climate. Even though these opposing growth responses are present in all sampled sites, their relative proportion varies between sites and there is no overall clear relationship between growth response and landscape position within a site. Growth increases and decreases appear in our sample above specific temperature index values (temperature thresholds), which occurred more frequently in the late 20th century. Contrary to previous findings, temperature explained more variability in radial growth after 1950. Without accounting for these opposite responses and temperature thresholds, climate reconstructions based on ring width will miscalibrate past climate, and biogeochemical and dynamic vegetation models will overestimate carbon uptake and treeline advance under future warming scenarios.

Thinning and prescribed fire are widely used to restore fire-suppressed forests, yet there are few studies of their effectiveness in Sierran mixed-conifer forest. We compared stand conditions in replicated plots before and after a combination of thinning and burning treatments against a reconstruction of the same forest in 1865. The historical forest had 67 stems/ha (trees ≥5 cm DBH), equal percentages of shade-tolerant and -intolerant tree species, stems randomly distributed at the stand scale, and a flat diameter distribution across size classes. The pretreatment forest averaged 469 stems/ha, which comprised 84% shade-tolerant and 14% shade-intolerant species, were highly clustered, and had a reverse-J-shaped diameter distribution. Thinning treatments failed to approximate historical composition, spatial pattern, or diameter distribution. Treatments left too many small trees, removed too many intermediate-sized trees (50–75 cm DBH), and retained a reverse-J-shaped diameter distribution. Current old growth comprises fewer large trees than historical conditions, suggesting that treatments should retain more intermediate-sized trees to provide for future large-tree recruitment. Understory thinning with prescribed fire significantly reduced stem density and produced a spatial pattern closest to historical conditions. Mixed-conifer restoration needs thinning prescriptions that vary by species and flexible rather than rigid upper diameter limits to retain some trees in all size classes.

Many studies have examined how fuels, topography, climate, and fire weather influence fire severity. Less is known about how different forest management practices influence fire severity in multi-owner landscapes, despite costly and controversial suppression of wildfires that do not acknowledge ownership boundaries. In 2013, the Douglas Complex burned over 19,000 ha of Oregon & California Railroad (O&C) lands in Southwestern Oregon, USA. O&C lands are composed of a checkerboard of private industrial and federal forestland (Bureau of Land Management, BLM) with contrasting management objectives, providing a unique experimental landscape to understand how different management practices influence wildfire severity. Leveraging Landsat based estimates of fire severity (Relative differenced Normalized Burn Ratio, RdNBR) and geospatial data on fire progression, weather, topography, pre-fire forest conditions, and land ownership, we asked (1) what is the relative importance of different variables driving fire severity, and (2) is intensive plantation forestry associated with higher fire severity? Using Random Forest ensemble machine learning, we found daily fire weather was the most important predictor of fire severity, followed by stand age and ownership, followed by topographic features. Estimates of pre-fire forest biomass were not an important predictor of fire severity. Adjusting for all other predictor variables in a general least squares model incorporating spatial autocorrelation, mean predicted RdNBR was higher on private industrial forests (RdNBR 521.85 ± 18.67 [mean ± SE]) vs. BLM forests (398.87 ± 18.23) with a much greater proportion of older forests. Our findings suggest intensive plantation forestry characterized by young forests and spatially homogenized fuels, rather than pre-fire biomass, were significant drivers of wildfire severity. This has implications for perceptions of wildfire risk, shared fire management responsibilities, and developing fire resilience for multiple objectives in multi-owner landscapes.

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