EESA’s Trevor Keenan and collaborator Ülo Niinemets of the Estonian University of Life Sciences and Estonian Academy of Sciences have just published a new study in a Letter in Nature Plants that concludes that global plant databases and models are underestimating plant growth rates and photosynthesis, plus other traits, because leaf measurements are reported as more fully exposed to sunlight than is the case. This problem is likely because it is difficult to reach the top of canopies.
“Often when researchers are in the field, it’s hard to get to leaves at the top of trees,” Trevor said. “In other cases, understory plants grow mostly in the shade, so it is impossible to sample in full sun. Traits vary quite a lot in the canopy, so if you don’t sample from the top all of your samples will be biased.”
Correcting for this underestimation of sunlight will improve climate models. For example, better accounting of light conditions for sampled leaves will improve assessment of total rate of photosynthesis, better quantify the plants’ role as a carbon sink, and evaluate plants’ adaptability to changing conditions.
“We really don’t know how plants are going to acclimate to a changing climate,” Trevor said, noting that Lab researchers are developing a theory for why plants acclimate and change their allocations of nutrients within the canopy. “We can use this to better understand why trait values vary.”
Trevor and Ülo used the worldwide CANTRIP database for canopy trait plasticity, the global Glopnet database, which comprises trait values for 1978 species, the Niinemets et al. database, which comprises trait values for 597 species, and the Poorter et al. database, containing trait values for 587 unique species. This combined “PNG” dataset comprises observations from all vegetated continents, and represents a wide range of vegetation types, from arctic tundra to tropical rainforest, including temperate and boreal forests, grasslands and deserts. They then focused on key traits: leaf mass per area (LMA), the light saturated assimilation rate, and nitrogen concentration on the basis of both mass and area. Read more…
This work was supported by Berkeley Lab’s Laboratory Directed Research and Development fund, and by DOE’s Office of Science.
In the news:
January 7, 2017: InsideHPC story looks at Trevor’s and Ülo’s work on using supercomputers to help shed light on more accurate plant models: http://insidehpc.com/2017/01/new-leaf-study-sheds-light-on-shady-past/