Climate and human driven disturbances continuously shift the functional diversity of ecosystems over time and hence, ecosystem processes and services. Semi-arid ecosystems cover large portions of the global terrestrial landscape and significantly influence global carbon dynamics and provide critical habitat quality and other ecosystem services. Measuring functional diversity across space and time is crucial to capture not only the current state but also the resilience and recovery of ecosystem processes that shape productivity and habitat. In this study, we calculate functional diversity within Reynolds Creek Experimental Watershed (RCEW), a semi-arid ecosystem in SW, Idaho, as functional richness, divergence and evenness using three morphological traits. These three traits include canopy height, foliage height diversity, and plant area index. Full-waveform lidar provides accurate estimates of these morphological traits. The traits were derived from NASA’s Airborne Snow Observatory (ASO) small footprint lidar and simulated at a coarser scale with NASA’s Global Ecosystem Dynamics Investigation (GEDI) large footprint full-waveform lidar signals. GEDI aims to collect vegetation structure globally and thus, this study further evaluates GEDI’s capability to capture functional composition shifts including in the most challenging semi-arid ecosystems.
Results/Conclusions
We found a high correlation (R2 > 60%) between field observed and lidar derived morphological traits, supporting the use of spatially continuous lidar data to calculate functional diversity. Diversity indices calculated at 30 m spatial resolution from ASO data failed to show relationships with either environmental gradients or disturbance. At 250 m spatial resolution, both evenness and divergence correlated with slope and aspect. Functional richness calculated at 500 m spatial resolution from both ASO and GEDI clearly delineate tree-shrub ecotones as well as areas of diverse richness within the tree / shrub dominant regions. These results indicate that there is scale dependency of diversity measures. Functional richness and functional evenness are inversely correlated (R2 = -75%) revealing high functional heterogeneity of the ecosystem. Functional divergence from both ASO and GEDI data captures burned (disturbed) areas, however, GEDI based divergence values are greater than those of ASO. Our results indicate that, GEDI can be used not only to calculate vegetation structure, but also functional diversity. We suggest that these diversity – ecosystem process relationships in semi-arid ecosystems may be used to explain habitat quality at the local scale, as well as inform global vegetation dynamics models.