We studied bark thickness in the mixed-conifer forest type throughout California. Sampling included eight conifer species and covered latitude and elevation gradients. The thickness of tree bark at 1.37 m correlated with diameter at breast height (DBH) and varied among species. Trees exhibiting more rapid growth had slightly thinner bark for a given DBH. Variability in bark thickness obscured differences between sample locations. Model predictions for 50 cm DBH trees of each species indicated that bark thickness was ranked Calocedrus decurrens > Pinus jeffreyi > Pinus lambertiana > Abies concolor > Pseudotsuga menziesii > Abies magnifica > Pinus monticola > Pinus contorta. We failed to find reasonable agreement between our bark thickness data and existing bark thickness regressions used in models predicting fire-induced mortality in the mixed-conifer forest type in California. The fire effects software systems generally underpredicted bark thickness for most species, which could lead to an overprediction in fire-caused tree mortality in California. A model for conifers in Oregon predicted that bark was 49% thinner in Abies concolor and 37% thicker in Pseudotsuga menziesii than our samples from across California, suggesting that more data are needed to validate and refine bark thickness equations within existing fire effects models.

No abstract

In many fire-prone woodlands and forests, fire exclusion has resulted in substantial litter and duff accumulations capable of long-duration smoldering once fire is reintroduced. While previous research has shown that the soil heating from smoldering fires resulted in short-term reductions in coarse root non-structural carbohydrates and latewood growth, information on the long-term effects of smoldering fire is lacking. Our study compared the effects of three smoldering fire treatments (root only, stem only, root and stem) and two control treatments (no fire and surface fire only) on longleaf pine (Pinus palustris) growth and defense ten years after treatments. We cored 17 to 29 similar sized trees per treatment and measured growth as basal area increment and defense as resin duct properties (e.g., resin duct size, % resin duct area). We

No abstract

Research Highlights: Bark thickness (BT) in coastal redwood (Sequoia sempervirens (D.Don) Endl.) varies in accordance with tree size, crown ratio, position within the canopy, height along the tree stem, genetic identity and latitude. However, current BT predictive equations do not account for such variability, leading to inaccurate BT estimations. We present improved BT models to increase the accuracy of BT estimates for coastal redwood in northern California. Background and Objectives: BT is an important metric that has many practical applications in forest management. However, BT varies substantially across species and environments, as well as across individuals and populations. Our objectives were to investigate BT along various gradients of change, with factors accounting for genetics, tapering of BT along the tree bole, differences in BT according to tree crown position within the stand, and the latitude. Materials and Methods: We collected BT data throughout most of redwood’s natural range along a north–south latitudinal gradient. Subsets of these data were used to examine the influence of particular variables on BT while holding the other variables constant. Results: Regionally, the bark was thicker among more xeric southern redwoods and thinner among more mesic northern redwoods. We found that the BT of codominant, intermediate and suppressed trees was around 8%, 14%, and 18% thicker, respectively, than bark of the same size dominant tree. Redwood trees growing in the partial shade of an overstory had thicker bark than trees growing in even-aged stands and incorporating genetic identity yielded major improvements in the BT model estimates, suggesting that BT is under genetic control. Bark thickness decreased with increasing height along the tree stem, with notable differences in the BT above and below breast height. Conclusions: We recommend utilizing the best available BT equations (over standard ‘bark factors’) in forest science, modeling and management applications. We also recommend the adoption of our drilling method for BT measurement on larger trees due to the potential for error associated with traditional bark gauge measurements.