OOS 48-2 - Biodiversity - forest structure relationships: predicting cross-biome plant diversity with aggregated GEDI samples versus continuous airborne lidar censuses

The Global Ecosystem Dynamics Investigation (GEDI) space-borne lidar facilitates data-driven prioritization efforts for ecosystem restoration, biodiversity conservation, and carbon sequestration at unprecedented scales. For biodiversity applications, the promise of GEDI lidar lies primarily in its ability to exploit empirically-observed biodiversity-structure relationships (BSRs) to map multi-scale geographical patterns in species diversity across regions and taxonomic groups. However, GEDI is a sampling instrument, with interstitial areas between discrete footprints not sampled. In response, researchers have implemented a variety of procedures to fill these gaps and provide continuous (gridded) raster data on canopy structure.
In this study, we address several known issues with aggregating GEDI-derived structural estimates to parametrize BSRs for cross-biome biodiversity prediction. As GEDI footprints are discrete samples, often dislocated from the location of field plot derived biodiversity observations, a central goal of this study is to assess the effectiveness of aggregate GEDI footprint samples for predicting plant and tree diversity across the biomes of the United States. Owing to the trade-off between spatial proximity and sample size in GEDI footprint aggregates (and its implications for determining optimal cell size for BSR prediction using gridded products of forest structure), these analyses explicitly account for the role of the spatial scale of footprint aggregations. Specifically, we seek to answer: (1) How do discrete, dislocated GEDI sample aggregates compare to ALS co-located, spatially-continuous censuses of structure for predicting plant and tree diversity? (2) How do results vary among alpha, beta, and gamma diversity indices, among six indices of forest structure, and across ecoregions of the USA? (3) What are the effects of sensor type, sample spatial structure and sampling density of aggregated footprint based BSRs? Results fill a critical gap in our understanding of how landscape-scale characteristic forest structural attributes predict specific field-observed biodiversity patterns, especially for cryptic species like understory plants that are not directly observable from air- and space-borne sensors. Cross-biome findings inform an enhanced biogeographic perspective on BSRs, with direct application to global-scale biodiversity mapping and monitoring efforts.