DOI: 10.5061/dryad.2fqz612n6
title: New insights into tree architecture from mobile laser scanning and geometry analysis

description: The structure and dynamics of a forest are defined by the architecture and
 growth patterns of its individual trees. In turn, tree architecture and
 growth result from the interplay between the genetic building plans and
 environmental factors. We set out to investigate whether (i) latitudinal
 adaptations of the crown shape occur due to characteristic solar elevation
 angles at a species’ origin, (ii) architectural differences in trees are
 related to seed dispersal strategies, and (iii) tree architecture relates
 to tree growth performance. We used Mobile Laser Scanning (MLS) to scan
 473 trees and generated three-dimensional data of each tree. Tree
 architectural complexity was then characterized by fractal analysis using
 the box-dimension approach along with a topological measure of the
 top-heaviness of a tree. The tree species studied originated from various
 latitudinal ranges but were grown in the same environmental settings in
 the arboretum. We found that trees originating from higher latitudes had
 significantly less top-heavy geometries than those from lower latitudes.
 Therefore, to a certain degree, the crown shape of tree species seems to
 be determined by their original habitat. We also found that tree species
 with wind-dispersed seeds had a higher structural complexity than those
 with animal-dispersed seeds (p &lt; 0.001). Furthermore, tree
 architectural complexity was positively related to the growth performance
 of the trees (p &lt; 0.001). We conclude that the use of 3D data from
 MLS in combination with geometrical analysis, including fractal analysis,
 is a promising tool to investigate tree architecture.
description: A ground-based mobile laser
 scanning (MLS) system was used to obtain 3D point cloud data for accurate
 measurement and mapping of the environment. The scanning was carried out
 in February 2020 when the trees were leafless, to ensure free sight on the
 tree crowns. All 473 trees were scanned carrying the scanner in the hand
 at around breast height with the arm outstretched while moving at a slow
 walking pace. We walked in a zig-zag route around the trees and covered
 two planting rows at a time in each scan by following the direction of the
 row and finally ending at the exact point where the scan started (up and
 down the row). We made sure to close the loop every time. By zig-zagging
 every other tree on the way back, we covered all trees from both sites.
 All MLS data was processed using GeoSLAM Hub 6 software and subsampled
 (0.01 m) in Cloud Compare. We obtained records of the
 periodical circumference measurements for 391 of the individual trees
 since the time of plantation from the Bavarian State Institute for
 Viticulture and Horticulture (LWG). Tree circumference was measured using
 calipers. We calculated the difference between the initial plantation
 radial measurement and the present radius of the tree individuals as a
 measure of tree growth and expressed it as annual radial
 increment. We
 used an algorithm written in Mathematica (Wolfram Research, Champaign,
 USA) to determine the structural complexity of each tree individual as
 shown in Seidel (2018). We used the relative height of
 maximum horizontal crown area (Rel.Hmaxarea) to describe the top-heaviness
 of a tree’s geometry. It was calculated based on the height of the maximum
 horizontal crown area in relation to the total tree height. Therefore, it
 is a relative measure corrected for tree height and given in percent. The
 underlying parameter “height of maximum crown area” was calculated as
 described in Seidel et al. (2011). In short, the tree point clouds were
 split into horizontal layers of 10 cm in thickness and the area of the
 convex-hull polygon enclosing all points in each horizontal layer was
 calculated. The height of the layer with the largest area is considered
 Hmaxarea (or ‘HCPA’ (height of maximum crown projection area) in earlier
 studies). The relative Hmaxarea was then given in percent of the total
 tree height. Tree height was derived from the point cloud as the
 difference between highest point and lowest point in the point cloud of a
 tree (zmax – zmin). The secondary data for the places
 of species origin and latitudinal range were obtained from the database of
 the European Forest Genetic Resources Program (EUFORGEN 1994) and Van Den
 Berk Nurseries (Vdberk 2020). Information regarding seed
 dispersal strategy was obtained from the Royal Botanic Gardens Kew Seed
 Information Database (SID 2020) as well as from additional literature
 (Howe and Smallwood 1982; Clark et al. 1999; Loewer 2005; Oyama et al.
 2018).
description: The mid-point of the maximum and
 minimum latitudinal distribution of each species was only estimated. We
 are aware that this mid-point latitude is of limited accuracy since highly
 detailed geographical information on every species’ natural distribution
 would be needed for an exact mid-point determination. This is, however,
 unavailable for many species. We used the absolute values of the latitudes
 in order to analyze both hemispheres together since we do not assume an
 effect on tree architecture based on the hemisphere (average solar
 elevation angles are the same). This analysis was performed for 431 trees
 from 83 species since we could not find exact origins for some of the
 cultivars.