Predicting forest canopy and species displacement
Out of an effort to account for what seemed in airborne images to be unusually large tree growth in a Hawaiian forest, scientists at Brown University and the Carnegie Institution for Science have developed a new mathematical model that predicts how trees compete for space in the canopy.
What their model revealed for this particular forest of hardy native Metrosideros polymorpha trees on the windward slope of Manua Kea, is that an incumbent tree limb greening up a given square meter would still dominate its position two years later a forbidding 97.9 percent of the time. The model described online in the journal Ecology Letters could help generate similar predictions for other forests, too.
Why track forest growth using remote sensing, pixel by pixel? Some ecologists could use that information to learn how much one species is displacing another over a wide area or how quickly gaps in the canopy are filled in. Others could see how well a forest is growing overall. Tracking the height of a forest's canopy reveals how tall the trees are and therefore how much carbon they are keeping out of the atmosphere — that is, as long as scientists know how to interpret the measurements of forest growth.
James Kellner, assistant professor of ecology and evolutionary biology at Brown University, the paper's lead and corresponding author, noticed what seemed like implausibly large canopy growth in LIDAR images collected by the Carnegie Airborne Observatory over 43 hectares on the windward flank of Manua Kea. In the vast majority of pixels (each representing about a square meter) the forest growth looked normal, but in some places the height change between 2007 and 2009 seemed impossible: sometimes 10 or 15 meters.
The data were correct, he soon confirmed, but the jumps in height signaled something other than vertical growth. They signaled places where one tree had managed to overtop another or where the canopy was filling in a bare spot. The forest wasn't storing that much more carbon; taller trees were growing a few meters to the side and creating exaggerated appearances of vertical growth in the overhead images.
Turning that realization into a predictive mathematical model is not a simple matter. Working with co-author Gregory P. Asner at the Carnegie Institution for Science in Stanford, Calif., Kellner created the model, which provides a probabilistic accounting of whether the height change in a pixel is likely to be the normal growth of the incumbent tree, a takeover by a neighboring tree, or another branch of the incumbent tree.
Read more at Brown University.
Tree canopy image via Shutterstock.