A new study published in Plant, Cell & Environment demonstrates that leaf elementomes are closely linked to intrinsic water-use efficiency (iWUE), revealing key mechanisms underlying plant water-use strategies across diverse forest ecosystems.

Led by Prof. YAN Zhengbing from the Institute of Botany of the Chinese Academy of Sciences, the researchers systematically collected canopy leaf samples from 82 tree species spanning China's three major forest types. They quantified iWUE variability and its associations with maximum carboxylation capacity (V_c,max), stomatal conductance (g_s), leaf elementomes, and leaf reflectance spectroscopy.

The results show that variation in iWUE across forest ecosystems is primarily driven by V_c,max, g_s, leaf mass per area (LMA), and the concentrations of iron, nitrogen, sodium and manganese. Climatic factors, such as mean annual temperature, mean annual precipitation, and the climate moisture index, further shape water-use strategies via an "environment–trait–iWUE" cascade. 

Notably, climate-driven changes in leaf elementomes and LMA have a stronger direct effect on iWUE than an indirect effect through V_c,max or g_s.

The study also demonstrates that leaf reflectance spectroscopy can effectively capture variation in iWUE and its associations with functional traits. By linking iWUE with leaf elementomes and reflectance spectroscopy, this study provides novel insights into trait coordination underlying iWUE variation and constraints for improving forest carbon–water models. 

The researchers suggested that reflectance spectroscopy shows promise for large-scale monitoring of plant water-use strategies in the context of climate change.

Mechanistic links between leaf elementomes and water-use efficiency across forest ecosystems (Image by YANG Nan & YAN Zhengbing). Abbreviations: V_c,max, the maximum carboxylation rate of the enzyme RuBisCo; LMA, leaf mass per area; N, nitrogen concentration; P, phosphorus concentration; K, potassium concentration; Ca, calcium concentration; Mg, magnesium concentration; S, sulphur concentration; Fe, iron concentration; Mn, manganese concentration; Na, sodium concentration; MAT, mean annual temperature; MAP, mean annual precipitation; VPD, vapor pressure deficit; CMI, climate moisture index. The symbol "+" indicates a positive correlation between variables, while the symbol "−" indicates a negative correlation.