Predicting the limits to tree height using statistical regressions of leaf traits

Area of Science:

• Plant physiology
• Forest ecology
• Biophysics

Background:

• Tree crowns exhibit significant plasticity in leaf morphology and physiology.
• Vertical gradients in leaf traits are pronounced in tall trees.
• These gradients have been proposed as indicators for estimating maximum tree height.

Purpose of the Study:

• To evaluate the proposed method of using leaf trait gradients and biophysical limits to predict maximum tree height.
• To assess the suitability and theoretical validity of proposed biophysical endpoints.
• To investigate the influence of environmental factors on height predictions.

Main Methods:

• Analysis of published and new experimental data from tall conifer and angiosperm species.
• Application of regression methods to analyze leaf trait gradients.
• Examination of theoretical biophysical limits and endpoints.

Main Results:

• Height predictions were highly sensitive to variations in the shape of leaf trait regressions between individual trees.
• The selection of specific biophysical endpoints significantly impacted prediction accuracy.
• Environmental factors, including site conditions, considerably influenced the height prediction outcomes.
• Leaf mass per area and leaf water potential coupled with twig cavitation vulnerability presented significant challenges.
• Photosynthetic rate and carbon isotope discrimination showed potential but require further characterization of complex relationships.

Conclusions:

• The proposed method for predicting maximum tree height using leaf trait gradients is sensitive to biological and environmental variability.
• Leaf mass per area, leaf water potential, and twig cavitation vulnerability are not ideal predictors.
• Photosynthetic rate and carbon isotope discrimination show promise but need further research to account for complex environmental interactions.