Local tree-tree interactions have been identified as the key mechanism underlying biodiversity-ecosystem functioning relationships at larger spatial scales. Complementarity of crown structures has been described to enhance canopy packing and thereby increase tree productivity. Several studies have used the theoretical volumetric complementarity in the context of the crown complementarity index (CCI). While the index is very useful to quantify vertical stratification, it lacks integration of distance, and the volume of interacting crown elements. To address these limits, we developed the crown overlap index (COI). It quantifies the volume of crown interactions within a given distance threshold, from 3D laser scanning point cloud data. We applied CCI and COI on point clouds of 115 tree pairs on the BEF-China experiment. We could show an inverse correlation between CCI and COI. At high CCI values, COI was consistently low. At lower complementarities, the crown overlap varied widely, hereby extending the dimensionality of tree interactions. Additionally, the COI allows to quantify areas of increased biotic interactions. We applied both indices to understand how crowns interact based on leaf trait and structural complexity dissimilarity. Both enhanced CCI, but decreased COI, indicating higher complementarity but lower overlap. We link this to crown shyness, a phenomenon describing the tendency of trees to keep their crowns separated. Leaf trait dissimilarity likely mirrors crown plasticity, where flexible deciduous species can “grow around” their neighbours, while rigid evergreen trees grow into one-another. Independent of leaf habit, structural complexity dissimilarity enhanced CCI and reduced COI through differences in crown organisation.

The structural data was collected using a “FARO Focus 3D X130” terrestrial laser scanner (TLS). These devices use a technology called light detection and ranging (LiDAR) to create a high-resolution 3D model of their surroundings, in the form of a point cloud (Figure 1D). Scanning was conducted between August and September 2023. A multi-scan approach with seven scans per TSP was utilised. The positioning of these was not according to a rigid design but adapted to the local terrain and vegetation density to reduce occlusion. Physical registration aids (spheres, targets) were used. The single scans were registered automatically and manually by one operator using the “FARO SCENE” software. The two TSP trees were then manually segmented from the point cloud. This was done primarily by one operator and reviewed by two others using the software “RIEGL RiSCAN PRO”. All structural parameters were extracted only from these two single tree point clouds per TSP. The structural parameter extraction and data processing was then performed in R v4.5.2 (R Core Team, 2025). For this a newly developed R-library “coi” was used that is available at: https://github.com/ataeub/coi. For the trait data please refer to: https://data.botanik.uni-halle.de/bef-china/datasets/648

BEF-China main experiment: Site A Xingangshan, Jiangxi Province, Dexing County, China 29°080 29°110N, 117° 900 117°930E (geoname ID 7651294)

Tree individuals on Site A of the BEF China Main experiment

Refer to: https://doi.org/10.5281/zenodo.18443104