Assessing GEDI data fusions to map woodpecker distributions and biodiversity hotspots

Abstract

Abstract In forested systems, woodpecker species richness has been linked with songbird diversity, and identifying woodpecker biodiversity hotspots may contribute important information for conservation planning. The availability of global forest structure data via the Global Ecosystem Dynamics Investigation (GEDI) instrument provides a new tool for examining broad extent relationships amongst environmental variables, forest structure, and woodpecker diversity hotspots. Within the Marine West Coast Forest ecoregion, USA, we used eBird data for 7 woodpecker species to model encounter rates based on bioclimatic variables, process data (e.g. duration and timing of survey), MODIS forest land cover data, and GEDI-fusion metrics. The GEDI-fusion metrics included foliage height diversity (fhd), rh98 (a representation of canopy height), and canopy cover, which were created by combining GEDI data with Landsat, Sentinel-1, topographic, and climatic information within a random forest modeling framework. AUCs for the species-specific models ranged from 0.77–0.98, where bioclimatic and process predictors were amongst the most important variables for all species. GEDI-fusion forest structure metrics were highly ranked for all species, with fhd included as a highly ranked predictor for all species. The structural metrics included as top predictors for each species were reflective of known species-specific habitat associations. Hotspots in this ecoregion tended to be inland and occurred most often on privately-owned lands. Identification of hotspots is the first step towards management plans focused on biodiversity, and understanding ownership patterns is important for future conservation efforts. The near-global extent of GEDI data, along with recent studies that recommend woodpeckers as indicators of biodiversity across multiple forest types at local and global scales, suggest that synthesis of GEDI-derived data applied to woodpecker detection information might be a powerful approach to identifying biodiversity hotspots.