B060-0005
Drivers of Ecosystem Functional Diversity in the Circumpolar Arctic Tundra Using Seasonal Dynamics of MODIS NDVI

Friday, 11 December 2020
Poster
Domingo Alcaraz-Segura, University of Granada, Botany, Granada, Spain, Amanda Hildt Armstrong, Universities Space Research Association Columbia, Columbia, MD, United States, Howard E Epstein, University of Virginia, Charlottesville, VA, United States, Morgan Tassone, University of Virginia, Environmental Sciences, Charlottesville, VA, United States, Elisa Montefiori, Universidad de Granada, Granada, Spain and Martha K Raynolds, University of Alaska Fairbanks, Institute of Arctic Biology, Fairbanks, AK, United States
Abstract:
The Arctic is a region with a high degree of spatial variability in ecosystem functioning, and one that is changing dramatically over time due to dynamics in climate and land use. To assess the spatial and temporal heterogeneity of ecosystem functioning, we identified Ecosystem Functional Types (EFTs), patches of the land surface that process energy and matter in similar ways and potentially show coordinated responses to environmental factors. We classified EFTs for the circumpolar Arctic tundra using three key functional attributes, derived from the seasonal dynamics of the MODIS Normalized Difference Vegetation Index (NDVI) from 2001- 2018: mean growing season NDVI, date of maximum green-up, date of maximum senescence. Using the new raster version of the Circumpolar Arctic Vegetation Map (CAVM), we assessed the correspondence between vegetation structure and ecosystem functioning for each of the 5 tundra bioclimatic subzones and the 15 defined physiognomic units. Finally, we determined ecosystem functional diversity as EFT richness within a 7x7 pixel moving window, and evaluated the environmental controls (climatic, geological, anthropogenic) on the spatial patterns of EFTs and EFT richness. Climatic drivers considered were Summer Warmth Index, mean growing season precipitation, and snow-free period onset date, yet we expected EFT distribution and abundance to also be driven by geologic (elevation, Topographic Wetness Index, landscape age, substrate pH, soil texture), biological (physiognomic vegetation type, herbivory), and anthropogenic (nighttime lights as indicators of communities and energy extraction facilities) drivers. This functionally based framework can assess landscape heterogeneity that is not solely determined by biodiversity composition and structure (e.g. vegetation composition), and aids in the identification of “functional hotspots,” as possible targets for conservation priorities.
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