Incorporating canopy structure from simulated GEDI lidar into bird species distribution models

The Global Ecosystem Dynamics Investigation (GEDI) lidar began data acquisition from the International Space Station in March 2019 and is expected to make over 10 billion measurements of canopy structure and topography over two years. Previously, airborne lidar data with limited spatial coverage have been used to examine the relationship between forest canopy structure and faunal diversity, most commonly bird species. GEDI's latitudinal coverage will permit these types of analyses at larger spatial extents, over the majority of the Earth's forests, and most importantly in areas where canopy structure is complex or poorly understood. In this regional study, we examine the impact that GEDI-derived canopy structure metrics have on the performance of bird species distribution models (SDMs) in Sonoma County, California. We simulate GEDI waveforms for a two-year period and then interpolate derived Canopy Structure metrics to three grid sizes of analysis. In addition to these variables, we also include Phenological, Climate and other Auxiliary variables to predict the probability of occurrence of 25 common bird species. We use a weighted average ensemble of seven individual machine learning models to make predictions for each species and calculate variable importance. We found that canopy structure variables were, on average at 250m resolution, the second most important group (32.7%) of predictor variables after climate variables (36.8%). Canopy structure variables were most important for predicting probability of occurrence of conifer forest specialists. Regarding spatial analysis scale, we found that finer-scale models more frequently performed better than coarser-scale models, and the importance of canopy structure variables was greater at finer spatial resolutions. Canopy structure metrics derived from GEDI show promise for improving SDM, but these findings should be corroborated for additional bird species, as well as other taxa and ecosystems within the observation domain of actual GEDI data.