Bark Thickness in Coast Redwood (Sequoia sempervirens (D.Don) Endl.) Varies According to Tree- and Crown Size, Stand Structure, Latitude and Genotype

Death from hunger or thirst? Phloem death, rather than xylem hydraulic failure, as a driver of fire-induced conifer mortality

Disruption of photosynthesis and carbon transport due to damage to the tree crown and stem cambial cells, respectively, can cause tree mortality. It has recently been proposed that fire-induced dysfunction of xylem plays an important role in tree mortality. Here, we simultaneously tested the impact of a lethal fire dose on nonstructural carbohydrates (NSCs) and xylem hydraulics in Pinus ponderosa saplings. Saplings were burned with a known lethal fire dose. Nonstructural carbohydrates were assessed in needles, main stems, roots and whole plants, and xylem hydraulic conductivity was measured in the main stems up to 29 d postfire. Photosynthesis and whole plant NSCs declined postfire. Additionally, all burned saplings showed 100% phloem/cambium necrosis, and roots of burned saplings had reduced NSCs compared to unburned and defoliated saplings. We further show that, contrary to patterns observed with NSCs, water transport was unchanged by fire and there was no evidence of xylem deformation in saplings that experienced a lethal dose of heat from fire. We conclude that phloem and cambium mortality, and not hydraulic failure, were probably the causes of death in these saplings. These findings advance our understanding of the physiological response to fire-induced injuries in conifer trees.

Modelling bark thickness for Scots pine (Pinus sylvestris L.) and common oak (Quercus robur L.) with recurrent neural networks

Variation of the bark depends on tree age, origin, geographic location, or site conditions like temperature and water availability. Most of these variables are characterized by very high variability but above of all are also affected by climate changes. This requires the construction of improved bark thickness models that take this complexity into account. We propose a new approach based on time series. We used a recurrent neural network (ANN) to build the bark thickness model and compare it with stem taper curves adjusted to predict double bark thickness. The data includes 750 felled trees from common oak and 144 Scots pine—trees representing dominant forest-forming tree species in Europe. The trees were selected across stands varied in terms of age and site conditions. Based on the data, we built recurrent ANN and calculated bark thickness along the stem. We tested different network structures with one- and two-time window delay and three learning algorithms—Bayesian Regularization, Levenberg-Marquardt, and Scaled Conjugate Gradient. The evaluation criteria of the models were: coefficient of determination, root mean square error, mean absolute error as well as graphical analysis of observed and estimated values. The results show that recurrent ANN is a universal approach that offers the most precise estimation of bark thickness at a particular stem height. The ANN recursive model had an advantage in estimating trees that were atypical for height, as well as upper and lower parts on the stem.

Modeling Bark Thickness and Bark Biomass on Stems of Four Broadleaved Tree Species

Considering the surface of individual tree compartments, it is obvious that the main portion of bark, i.e., the largest area and the greatest bulk mass, is located on the stem. We focused on basic bark properties, specifically thickness, surface area, biomass, and specific surface mass (expressed as dry weight per square unit) on stems of four broadleaved species: common aspen (Populus tremula L.), goat willow (Salix caprea L.), rowan (Sorbus aucuparia L.), and sycamore (Acer pseudoplatanus L.). Based on the previous work from mature forests, we hypothesize that bark properties of young trees are also species-specific and change along the stem profile. Thus, across the regions of Slovakia, we selected 27 forest stands composed of one of the target broadleaved species with ages up to 12 years. From the selected forests, 600 sample trees were felled and stem bark properties were determined by measuring bark thickness, weighing bark mass after its separation from the stem, and drying to achieve a constant weight. Since the bark originated from trees of varying stem diameters and from different places along the stem (sections from the stem base 0-50, 51-100, 101-150, 151-200, and 201-250 cm), we could create regression models of stem characteristics based on the two mentioned variables. Our results confirmed that bark thickness, thus also specific surface mass, increased with stem diameter and decreased with distance from the stem base. While common aspen had the thickest stem bark (4.5 mm on the stem base of the largest trees) the thinnest bark from the analyzed species was found for sycamore (nearly three times thinner than the bark of aspen). Since all four tree species are very attractive to large wild herbivores as forage, besides other uses, we might consider our bark mass models also in terms of estimating forage potential and quantity of bark mass consumed by the herbivory.

Bark thickness models for oak forests being converted from coppice to high forests in Northwestern Turkey

The research was carried out in the coppice-originated pure oak stands that are being converted to high forests in northwest Turkey. The main goal of the research was to determine the bark thickness (BT) based on tree variables, such as tree diameter at breast height (DBH), total tree height (H), crown diameter (CD), and age (AGE) of the stem sections taken from a total of 350 trees that were destructively sampled from different sites, different oak species (Quercus petraea, Quercus frainetto, Quercus cerris), and different development stages. Models were developed with stepwise multiple regression analysis to predict BT based on the variables. For all oak species, all models obtained by stepwise multiple regression analysis were found to be significant at p = 0.001 level. In Quercus petraea, only the DBH-dependent model explained the variation in BT at a rate of 73%, estimating with an absolute error rate of 21%. The fit statistics of the models (based on DBH and DBH-H explanatory variables) obtained for Quercus frainetto are very close to each other, and they explained the variation in BT at a rate of 69% and estimated with an error rate of 26%. Models (based on DBH and DBH-H explanatory variables) explain the variation in BT in Turkey oak at a rate of 91%, indicating species-specific results. The models based on only DBH can be used with high accuracy to estimate BT.