Journal
GLOBAL CHANGE BIOLOGY
Volume 22, Issue 7, Pages 2596-2607Publisher
WILEY
DOI: 10.1111/gcb.13264
Keywords
canopy; landscape; mycorrhizae; nutrients; remote sensing; species; spectral
Funding
- Indiana Academy of Sciences
- CTFS-ForestGEO
- 1923 Fund
- Smithsonian Institution
- Washington University in St. Louis' Tyson Research Center
- International Center for Advanced Renewable Energy and Sustainability (I-CARES)
- US Department of Energy Office of Biological and Environmental Research Terrestrial Ecosystem Science Program
- US National Science Foundation Ecosystem Science Program
- Indiana University's Research and Teaching Preserve
- Indiana University's Office of the Vice Provost for Research
- National Aeronautics and Space Administration
- Direct For Social, Behav & Economic Scie [1437591] Funding Source: National Science Foundation
- Division Of Behavioral and Cognitive Sci [1437591] Funding Source: National Science Foundation
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A central challenge in global ecology is the identification of key functional processes in ecosystems that scale, but donot require, data for individual species across landscapes. Given that nearly all tree species form symbiotic relationships with one of two types of mycorrhizal fungi - arbuscular mycorrhizal (AM) and ectomycorrhizal (ECM) fungi - and that AM- and ECM-dominated forests often have distinct nutrient economies, the detection and mapping of mycorrhizae over large areas could provide valuable insights about fundamental ecosystem processes such as nutrient cycling, species interactions, and overall forest productivity. We explored remotely sensed tree canopy spectral properties to detect underlying mycorrhizal association across a gradient of AM- and ECM-dominated forest plots. Statistical mining of reflectance and reflectance derivatives across moderate/high-resolution Landsat data revealed distinctly unique phenological signals that differentiated AM and ECM associations. This approach was trained and validated against measurements of tree species and mycorrhizal association across similar to 130000 trees throughout the temperate United States. We were able to predict 77% of the variation in mycorrhizal association distribution within the forest plots (P<0.001). The implications for this work move us toward mapping mycorrhizal association globally and advancing our understanding of biogeochemical cycling and other ecosystem processes.
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