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).