DOI: 10.5061/dryad.zw3r228fq
title: LiDAR-derived tree architecture data of geo-located tropical trees in Luquillo experimental forest, Puerto Rico

description: Tree architecture is an important component of forest community dynamics –
 taller trees with larger crowns often outcompete their neighbors, but they
 are generally at higher risk of wind-induced damage. Yet we know little
 about wind impacts on tree architecture in natural forest settings,
 especially in complex tropical forests. Here we use airborne LiDAR from
 NASA’s Goddard’s LiDAR, Hyperspectral and Thermal  (G-LiHT)
 Airborne Image March 2017 flights over Puerto Rico and 30 years of forest
 inventory data in Puerto Rico to ask whether and how chronic winds alter
 tree architecture.  We randomly sampled 124 geo-located canopy
 individuals of four dominant tree species. For each individual, we
 measured slenderness (height/stem diameter) and crown area (m2) and
 evaluated whether exposure to chronic winds impacted architecture after
 accounting for topography (curvature, elevation, slope, soil wetness) and
 neighborhood variables (crowding and previous hurricane damage). We then
 estimated the mechanical wind-vulnerability of trees. 
description: # Wind acclimation in a subtropical forest: trees on wind-exposed slopes
 are shorter with smaller crowns --- ## Introduction This dataset was used
 for analyses and figures in Ankori-Karlinsky et al., 2025 in *New
 Phytologist*. The main question of this paper is whether exposure to
 chronic winds reduces individual trees' vulnerability of stem-break
 within species. In order to evaluate this question, we use airborne
 LiDAR-derived crown metrics from 124 geo-located trees in the 16ha
 Luquillo Forest Dynamics Plots (LFDP) in the Luquillo Experimental Forest
 (LEF) in El Yunque National Forest, Puerto Rico. The LiDAR data come from
 geo-located 35cm resolution G-LiHT
 ([https://gliht.gsfc.nasa.gov/](https://gliht.gsfc.nasa.gov/)) LiDAR
 point-clouds taken in 2017 (before Hurricane Maria) over the LEF at 12
 pulses/m2 density. We focus on 4 abundant (~40% of forest basal area)
 species that differ in life-span and wind-resistant life-history traits: *
 *Buchenavia tetraphylla* (~40-50 year lifespan, intermediate hurricane
 resistance) * *Cecropia schreberiana* (~20-40 year lifespan, low hurricane
 resistance) * *Dacryodes excelsa* (~400 year lifespan, high hurricane
 resistance) * *Manilkara bidentata* (~400 year lifespan, high hurricane
 resistance) To understand how chronic wind-induced architectural imprints
 may impact a tree’s vulnerability to storms, we calculated mechanical wind
 vulnerability for each individual tree *i* of species *s* ($rs_{is}$) at
 storm speeds (e.g., 25 m/s) based on the GALES wind vul model from
 Gardiner et al., (2000) and based on Jackson et al., (2021): $$vul_{is}
 \sim \frac{H_{is}}{dbh_{is}^{3}} ⋅\frac{A_{is}}{\rho_{s}} $$ where *H* is
 the vertical height of the tree in m, dbh is the diameter of the stem in
 cm at 1.3 m Height, *A* is the surface area of the crown in m$^{2}$, and
 $\rho$ is species-level wood weight to volume in $\frac{kg}{cm^{3}}$ from
 Swenson et al., (2012) We therefore estimate how chronic wind-exposure
 (exposure to NE-SE trade winds) may reduce wind vulnerability by leading
 to either shorter trees, wider stems, or reducing crown area. We also
 explores how a number of other covariates may influence tree architecture
 and therefore, vulnerability: * **Topography -** wetness index, elevation,
 slope, curvature within a radius of 20 m$^{2}$ * **Neighborhood Crowding**
 using the crowding index from Uriarte et al., (2004) within a radius of 20
 m$^{2}$ * **Previous Neighborhood Hurricane Damage** within a radius of 20
 m$^{2}$ ## References Gardiner, B., Peltola, H., & Kellomäki, S.
 (2000). Comparison of two models for predicting the critical wind speeds
 required to damage coniferous trees. Ecological Modelling, 129(1), 1–23.
 [https://doi.org/10.1016/S0304-3800(00)00220-9](https://doi.org/10.1016/S0304-3800\(00\)00220-9) Jackson, T., Shenkin, A. F., Majalap, N., Jami, J. B., Sailim, A. B., Reynolds, G., Coomes, D. A., Chandler, C. J., Boyd, D. S., Burt, A., Wilkes, P., Disney, M., & Malhi, Y. (2021). The mechanical stability of the world’s tallest broadleaf trees. Biotropica, 53(1), 110–120. [https://doi.org/10.1111/btp.12850](https://doi.org/10.1111/btp.12850) Swenson, N. G., Stegen, J. C., Davies, S. J., Erickson, D. L., Forero-Montaña, J., Hurlbert, A. H., Kress, W. J., Thompson, J., Uriarte, M., Wright, S. J., & Zimmerman, J. K. (2012). Temporal turnover in the composition of tropical tree communities: Functional determinism and phylogenetic stochasticity. Ecology, 93(3), 490–499. [https://doi.org/10.1890/11-1180.1](https://doi.org/10.1890/11-1180.1) Uriarte, M., Canham, C. D., Thompson, J., & Zimmerman, J. K. (2004). A Neighborhood Analysis of Tree Growth and Survival in a Hurricane-Driven Tropical Forest. Ecological Monographs, 74(4), 591–614. [https://doi.org/10.1890/03-4031](https://doi.org/10.1890/03-4031) ## Metadata Each row is one individual tree stem. Below is information on each column:\ **UniqueID**: Tag number of tree in the Luquillo Forest Dynamics Plot (LFDP)\ **Species**: Species 6-letter abbreviation\ **Quadrat**: 20m² quadrat number\ **Q5**: 5m² quadrat number\ **X**: Relative x coordinate of tree stem\ **Y**: Relative y coordinate of tree stem\ **DBH**: Diameter at 1.3m height in cm\ **Elev_20m**: Average elevation in m in 20m radius around tree stem\ **Slope_20m**: Average topographic slope in degrees in 20m radius around tree stem\ **Curve_20m**: Average topographic curvature in 20m radius around tree stem\ **TWI_20m**: Average topographic wetness index in 20m radius around tree stem\ **NCI_2000_20m**: Average crowding index in 20m radius around tree stem from 2000 census\ **Wind_Binary**: Chronic wind exposure (protected or exposed)\ **Wood_Density**: Mean species-level wood specific gravity according to Swenson et al. 2012 (kg/m³)\ **SLA**: Mean species-level specific leaf area according to Swenson et al. 2012\ **Hugo_damage**: Proportion severely damaged trees from Hurricane Hugo in 1989 in 20m radius around tree stem\ **Height**: Maximum height of tree crown from ALS data in m\ **Crown_Volume**: Volume of tree crown from ALS data in m³\ **Crown_area**: Area of tree crown from ALS data in m²\ **Mean_aspect**: Average direction of tree crown from ALS data in degrees\ **AGR_all**: Absolute growth rate of tree from 2016 census to earliest census observed\ **Slenderness**: Height/DBH\ **DBH3**: DBH to the third power to model cantilever beam\ **Wind_Vul**: Mechanical vulnerability of wind-induced stem-break calculated according to Gardiner et al. 2000 GALES model\ **Comment**: Comment in Geo7x handheld Trimble device indicating direction of measurement relative to the location of the tree stem\ **Corr_Type**: Correction type (if any) used by Pathfinder software from Trimble\ **Rcvr_Type**: Recovery type of information\ **GPS_Date**: Date measurement was taken\ **GPS_Time**: Time of day measurement was taken\ **Unfilt_Pos**: Number of measurements (1 per second) taken before processing\ **Filt_Pos**: Number of measurements (1 per second) taken after processing\ **GNSS_Heigh**: Elevation above sea level measured in m\ **Vert_Prec**: Vertical precision accuracy in m\ **Horz_Prec**: Horizontal precision accuracy in m\ **Std_Dev**: Standard deviation of precision accuracy\ **Northing**: Northing coordinate (in UTM -20N Puerto Rico projection)\ **Easting**: Easting coordinate (in UTM -20N Puerto Rico projection) ## Sharing/access Information Please cite Ankori-Karlinsky et al., 2025 in *New Phytologist* if you use these data.