Tree‐mycorrhizal associations detected remotely from canopy spectral properties. (20th April 2016)

• Record Type: Journal Article
• Authors: Fisher, Joshua B.; Sweeney, Sean; Brzostek, Edward R.; Evans, Tom P.; Johnson, Daniel J.; Myers, Jonathan A.; Bourg, Norman A.; Wolf, Amy T.; Howe, Robert W.; Phillips, Richard P.
• Is Part Of: Global change biology. Volume 22:Number 7(2016:Jul.)
• Page Start: 2596
• Page End: 2607
• Publication Date: 2016-04-20
• Subjects: canopy -- landscape -- mycorrhizae -- nutrients -- remote sensing -- species -- spectral; Climatic changes -- Environmental aspects -- Periodicals; Troposphere -- Environmental aspects -- Periodicals; Biodiversity conservation -- Periodicals; Eutrophication -- Periodicals; 551.5
• DOI: 10.1111/gcb.13264
• Languages: English
• ISSNs: 1354-1013

Abstract

Abstract: A central challenge in global ecology is the identification of key functional processes in ecosystems that scale, but do not 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 ~130 000 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.