Crown traits of coniferous trees and their relation to shade tolerance can differ with leaf type: a biophysical demonstration using computed tomography scanning data.

Plant light interception and shade tolerance are intrinsically related in that they involve structural, morphological and physiological adaptations to manage light capture for photosynthetic utilization, in order to sustain survival, development and reproduction. At the scale of small-size trees, crown traits related to structural geometry of branching pattern and space occupancy through phyllotaxis can be accurately evaluated in 3D, using computed tomography (CT) scanning data. We demonstrate this by scrutinizing the crowns of 15 potted miniature conifers of different species or varieties, classified in two groups based on leaf type (10 needlelike, 5 scalelike); we also test whether mean values of crown traits measured from CT scanning data and correlations with a shade tolerance index (STI) differ between groups. Seven crown traits, including fractal dimensions (FD1: smaller scales, FD2: larger scales) and leaf areas, were evaluated for all 15 miniature conifers; an average silhouette-to-total-area ratio was also calculated for each of the 10 needlelike-leaf conifers. Between-group differences in mean values are significant (P < 0.05) for STI, FD1, FD2, and the average leaf area displayed (ĀD). Between-group differences in sign and strength of correlations are observed. For example, the correlation between STI and FD1 is negative and significant (P < 0.10) for the needlelike-leaf group, but is positive and significant (P < 0.05) for the miniature conifers with scalelike leaves, which had lower STI and higher FD1 on average in our study; the positive correlation between STI and ĀD is significant (P < 0.05) for the scalelike-leaf group, and very moderate for the needlelike-leaf one. A contrasting physical attachment of the leaves to branches may explain part of the between-group differences. Our findings open new avenues for the understanding of fundamental plant growth processes; the information gained could be included in a multi-scale approach to tree crown modeling.