Modeling loblolly pine canopy dynamics for a light capture model

• Published in: Forest ecology and management 2003-02, Vol.173 (1), p.145-168
• Main Authors: MacFarlane, David W, Green, Edwin J, Brunner, Andreas, Amateis, Ralph L
• Format: Article
• Language: English
• Subjects: Animal and plant ecology; Animal, plant and microbial ecology; Autoecology; Biological and medical sciences; canopy; Canopy dynamics; equations; Forestry; forests; Fundamental and applied biological sciences. Psychology; General forest ecology; Generalities. Production, biomass. Quality of wood and forest products. General forest ecology; leaf area; Light-competition; Loblolly pine; Model parameters; Pinus taeda; planting; Plants and fungi; prediction; regression analysis; site index; stand density; trees
• ISSN: 0378-1127; 1872-7042
• DOI: 10.1016/S0378-1127(02)00011-7

Advances in forest modeling make it possible to estimate light capture for every tree in a stand, and may allow for improvements in modeling stand dynamics. A major difficulty in using such models is that they rely heavily on parameterization of crown characteristics, which presumably differ from stand to stand. We reformulated crown parameters of the tRAYci light capture model for describing crown shape, relative foliar shell thickness and leaf area density (LAD) into generalized equations, which can be used to describe canopy dynamics in even-aged loblolly pine ( P. taeda L .) stands. We used parameter equations to model 8 years of change in the canopy of 36, 17-year-old experimental loblolly pine stands, planted under a variety conditions, and estimated annual light capture for every tree over the study period. The results of our analysis suggest that differences in LAD between stands were effectively captured by our parameter estimation methods, but model predictions remained sensitive to parameters describing crown shape and foliar shell thickness. Our results suggest that estimated light capture from tRAYci is somewhat robust to different parameter settings because light capture estimation is strongly influenced by individual tree dimensions, and our methods enhanced this quality. General regression equations were developed for predicting crown characterization parameters from site index, stand age and stand density, but these equations did not fully capture differences in parameter values predicted from stand measurement data. Regression analysis and C p analysis suggest that planting density was a superior predictor variable for characterizing canopy dynamics when compared to current density. Also discussed in this manuscript are general patterns in canopy dynamics with special references to tRAYci model structure and behavior.