Structure data
Three-dimensional mapping sensors, such as lidar, measure habitat
characteristics with high precision at fine scales (Davies and Asner,
2014). Productivity metrics derived from lidar data, such as Leaf Area
Density (LAD, Bouvier et al., 2015) and heterogeneity metrics, such as
LAD variability across a three-dimensional area, have more explanatory
power for avian species richness than two-dimensional metrics such as
canopy surface metrics (Carrasco et al., 2019). We standardized lidar
height measures from NEON’s classified discrete return point cloud
before calculating standardized metrics for each sample location using
the “lidR” package in R (Roussel et al., 2020; Roussel and Auty,
2021). Following the methods used by Carrasco et al. (2019), we selected
habitat metrics that are representative and comprehensive in describing
productivity and heterogeneity and are known to influence avian
richness: Mean LAD in both horizontal and vertical planes; the Shannon
index for LAD; the mean, maximum, and coefficient of variation of
vegetation height; the vertical distribution ratio (VDR); the mean
coefficient of variation (CV) of LAD in both horizontal and vertical
dimensions; and the deep gap fraction across each area (see Table S3 for
definitions). In addition to vertical habitat measurements, mean slope,
aspect, and elevation metrics were calculated for each survey location
from the lidar data using the same R package.
Raster products from NEON’s hyperspectral imagery include enhanced
vegetation index (EVI) which we used to determine the mean EVI for each
location using the ‘raster’ package in R (Hijmans, 2021). The mean EVI
represented a surrogate measure of vegetation productivity and health
across a two-dimensional space, with high values indicating healthy
vegetation (Huete et al., 2002).