Long-term precommercial thinning effects on Larix occidentalis (western larch) tree and stand characteristics

Abstract

Precommercial thinning (PCT) is used to increase tree size and shorten harvest rotation time. Short-term results from PCT studies often show a trade-off between individual-tree growth and net stand yield, while longer-term effects of PCT on tree growth and stand yield are less well documented. We used a 54-year-old PCT study to test long-term effects of forest density and thinning schedules on stand yield and tree-level characteristics in even-aged western larch (Larix occidentalis Nutt.) stands. The study has three target densities (494, 890, and 1680 trees·ha−1) crossed with three thinning schedules (target density achieved through one, two, or four entries). Analysis of variance (ANOVA) and linear contrasts were used to test the effects of density and number of entries on tree- and stand-level attributes. Thinning before stand age 10 years leads to long-term constant yield (219.0–269.5 m3·ha−1; P > 0.05) across the tested densities. We also found constant volume growth across stand densities during the most recent measurement interval (5.42–6.41 m3·ha−1·year−1; P > 0.05). Number of entries did not affect any tree- or stand-level attribute. The primary effect of early PCT is to control whether wood volume and growth are concentrated on few large, stable trees or spread over many small, unstable trees.

Wildfire and drought moderate the spatial elements of tree mortality

“…The ecological mechanisms that give rise to spatio‐temporal patterns in tree mortality may be broadly grouped into three categories: density dependence, distance dependence, and environmental heterogeneity. Density‐dependent mortality emerges when tree neighborhoods mediate mortality risk (Kenkel 1988, Larson et al 2015), and is evident as elevated susceptibility to competition (Gray and He 2009, Das et al 2011), biotic mortality agents (Janzen 1970, Packer and Clay 2000, Johnson et al 2014), and abiotic mechanisms (King 1986, Das et al 2008, Yu et al 2009, Schaedel et al 2017) in dense tree neighborhoods. Distance dependence, in contrast, refers to the elevated risk of mortality for trees close to an affected individual, a characteristic associated with many mortality agents including pathogens, insects, and crushing (i.e., contagion; Goheen and Hansen 1993, Das et al 2008, Raffa et al 2008).…”

Estimating Individual Tree Height and Diameter at Breast Height (DBH) from Terrestrial Laser Scanning (TLS) Data at Plot Level

“…As microwaves can penetrate dense canopy to obtain information on the branches and trunks below forest canopies, synthetic aperture radar (SAR) is more advantageous than passive optical remote sensing methods to detect forest structure parameters and biomass [4]. Backscattering mechanism of SAR data [8][9][10] and interferometric synthetic aperture radar (InSAR) [11,12], polarimetric synthetic aperture radar interferometry (POLinSAR) [13,14] and polarization coherence tomography (PCT) [15,16] techniques all have obtained many research results on forest structural parameters. Meanwhile, LiDAR has been intensively applied to the study of forest structural parameters.…”

Management of Maritime Pine: Energetic Potential with Alternative Silvicultural Guidelines

“…A disadvantage that could be imputed to Scenarios 3 and 4 is the vulnerability to wind damages as it is expected that trees growing in dense stands present high values of height/diameter ratios, which usually denote lower stability to the wind effects [4]. As stated by Schaedel et al [35], the long-term effect of lower stand density is to produce trees of larger size and greater stability while not sacrificing the stand yield. In stands growing at high densities, losses can occur by windthrow.…”

“…The simulations consider stands developing in a site of high quality for the species, with a dominant height of 20 m at the reference age of 35 ModisPinaster allows simulating stand growth (in 1-year steps) and simulation of interventions. The prescription of thinning can be performed manually through the selection of the values of the number of trees to thin by diameter class or automatically using an algorithm of thinning.…”