Skip to main content
Dryad

Low-severity winds reduce tropical forest structural complexity regardless of climate, topography or forest age

Cite this dataset

Ankori-Karlinsky, Roi et al. (2024). Low-severity winds reduce tropical forest structural complexity regardless of climate, topography or forest age [Dataset]. Dryad. https://doi.org/10.5061/dryad.fbg79cnzk

Abstract

Forests are often exposed to regular, non-severe winds (chronic wind exposure), yet the effect of such winds on canopy structure in tropical forests remains understudied. The height and structural complexity of a forest canopy are strongly and positively correlated with biodiversity and carbon accumulation. Understanding the drivers of canopy structural complexity across broad environmental gradients can therefore improve the mapping and modeling of diversity and carbon dynamics. Here we predict the height and structural complexity of forests in the heterogeneous island of Puerto Rico, with a particular focus on the impacts of chronic wind exposure. To do so, we used remote sensing to randomly sample ~20,000, 0.28 ha forested sites stratified by forest age, and used airborne LiDAR data from 2016 to quantify canopy height and a key metric of structural complexity, rugosity – the standard deviation in canopy height. We then ran random forest models to predict canopy height and rugosity based on chronic wind exposure, forest age, mean annual precipitation, elevation, slope, soil type, soil available water storage, and exposure to two previous hurricanes (in 1989 and 1998). Canopy height was 4 m taller on average (41%) between forests aged 17-25 years and old-growth forests and by 4 m on average (41%) between 1,000 and 2,000 mm-yr precipitation, leveling off at 2,000 mm-yr. Height was 2.12 m (16%) shorter on average between sites exposed to chronic winds and protected sites after accounting for all other factors. Rugosity was 1 m (32%) greater between the tallest and shortest forests, by 0.5 m (15%) between 1,000 and 2,000 mm-yr precipitation, and smaller by 0.5 m (15%) between forests above and below 1,000 m elevation. Rugosity was highest in forests of intermediate age (25-40 years), and lowest in old-growth forests, possibly because of higher elevation and chronic wind exposure in old-growth forests. We found no effect of slope, soil characteristics or previous hurricane exposure on either height or rugosity. Our results suggest that alongside forest age and climate context, chronic wind exposure plays an integral role in shaping the structure and carbon cycle of tropical forests.

README: Drivers of tropical forest height and canopy structural complexity across heterogeneous landscapes:


This dataset was used for analyses and figures in Ankori-Karlinsky et al., 2023 in Ecosystems.

The dataset contains 20,660 30 m-radius forested sites in Puerto Rico stratified by forest age class (see Martinuzzi et al., 2020 for details).

For each site, we quantified canopy height and rugosity using 2016 airborne USGS LiDAR data.

The dataset was used to predict both metrics based on forest age, exposure to chronic winds, precipitation, topography, soil properties, and exposure to previous hurricanes.

Metadata

Each row is a 30 m-radius site.
Below is information on each column:
x - longitude in NAD83 (EPSG 4269)
y - latitude in NAD83
Forest_Age - Forest age class (1-5):
* 5-16 years
* 17-25 years
* 26-39 years
* 40-65 years
* 66+ years
CHM - Mean height (m) from canopy height model
Max_Height - Maximum height (m)
Rugosity - Stdev in height from 15m moving windows
MAP - Mean annual precipitation (mm/year) from PRISM
Wind_Binary - Protected (0) vs. exposed (1)
Elev_Mean - Mean elevation (m) from USGS DEM
Slope - Mean slope (in degrees)
Soil - Soil type (e.g. volcanic vs. limestone) from USGS
Hugo/Georges exposure - Mean exposure from 0-1 to hurricanes Hugo (1989) and Georges (1998) from Boose et al., (2004)
AWS_mean - Available water storage in soils from gSSURGO

Sharing/access Information

Please cite Ankori-Karlinsky et al., 2023 Ecosystems if you use these data.

Usage notes

We measured canopy height and rugosity for ~20,000 30 m-radius forested sites in Puerto Rico using airborne LiDAR data from a 2016 flight collected by the USGS (Carswell Jr. 2016) with aggregate nominal pulse spacing of ≤0.35 m and pulse density of ≥8.0 pulses/m2. We then used 16 years of wind direction and speed data from 9 weather stations to calculate a map of chronic wind exposure using a 30 m DEM from the USGS. We then ran random forest models to predict drivers of canopy height and rugosity based on chronic wind exposure, mean annual precipitation, forest age class, elevation, slope, soil type, soil available water storage, and exposure to two previous hurricanes (Hugo in 1989 and Georges in 1998).

Funding

National Science Foundation, Award: DEB-1546686

National Science Foundation, Award: DEB-1801315

Swedish Research Council, Award: 2019-03758