For the first time ever, a team of scientists led by NASA has created a method for detecting underground forest fungi from space. The new data might help scientists in their quest to understand and predict how climate change affects forest habitats.
Every forest possesses a network of fungi living beneath them. This network of myocorrhizal fungi engages in a mutually beneficial relationship with the trees and spreads out for miles, searching for nutrients that they then trade for sugar with the trees.
"Nearly all tree species associate with only one of two types of mycorrhizal fungi," said Richard Phillips, co-author of the study.
These two types of fungi respond differently to climate change, meaning it is beneficial for scientists to know where each type exists in their highest numbers in order to help them predict where forests will thrive and where they will struggle to survive.
Although typically maps of forest-fungi networks rely on counting individual tree species, the new method used satellite imagery to detect these hidden networks, allowing scientists to create larger-scale maps.
The team used satellite images of forest canopes to determine the patterns in spectral signatures - the unique amount of light absorption and reflection in trees that creates a specific light wavelength pattern - of tree species that associated with a type of fungus that does not appear in tree species associated with the other type.
"Individual tree species have unique spectral fingerprints, but we thought the underlying fungi could be controlling them as groups," said Joshua Fisher, who led the study.
The team examined images of four U.S. forest research regions that include approximately 130,000 trees across 77 species, all of which had already been mapped in terms of their fungus relationships from the ground.
Using images of the forest canopies taken by the NASA/U.S. Geological Survey Landsat-5 satellite from 2008 to 2011, the team searched for similarities that matched with areas of fungus dominance, revealing various milestones throughout the growing season and the relationship between the timing of these milestones and the unique regions.
After determining the timing of the milestones, the team was able to use a statistical model to predict the areas of fungus domination in any image by analyzing canopy changes, with a correct fungus association found in 77 percent of the images. Using this technique, they created numerous landscape-wide maps of the forest-fungi associations in each region.
"That these below-ground agents manifest themselves in changes in the forest canopies is significant," Fisher said." This allows, for the first time, some light to be shed on their hidden processes."
The findings were published in the March 1 issue of Global Change Biology.
