Plant biology

Explaining plant growth

Print edition : April 14, 2017

A cross-section of a California live oak leaf one micrometre thick. Photo: Grace John/UCLA

Several questions in plant biology are answered with just a single trait called “leaf mass per area (LMA)”, which is dry leaf mass divided by its original fresh area. LMA has been used in nearly every field of plant biology to make predictions of many processes and properties such as leaf photosynthetic rates, nitrogen content and plant environmental preferences.

However, the relationship of LMA to leaf structure —the cells and tissues that make up a leaf, and their numbers and dimensions—remained unknown.

Researchers at the University of California Los Angeles (UCLA), in association with researchers in Spain, Germany and Australia, have developed a mathematical equation for LMA that will help them determine what drives plant behaviours on the basis of their cells. The research has important implications as plants adapt to a warming environment. It was published in Ecology Letters.

“The great diversity of leaves in size, shape and colour is dazzling, and yet, it is nothing as compared to the diversity of cells and tissues inside,” said Lawren Sack, a professor of ecology and evolutionary biology and the study’s senior author. “However, we have lacked equations to relate this inner diversity to overall leaf behaviour in an exact way.”

Grace John, a UCLA doctoral student and the study’s lead author, conducted a detailed study of the anatomy of 11 species growing on the grounds of UCLA that included iconic species from many ecosystems, such as the toyon or hollywood, and a species of tea from Japan.

She measured cross sections for the sizes and numbers of cells of the different leaf tissues and stained whole leaves to measure their vein tissues. The team then developed a theoretical approach based on geometric principles to derive an equation for LMA, taking into account the dimensions and numbers of cells of each type in the leaf.

This equation was found to predict LMA of the diverse leaves with extreme precision.

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