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1. It is challenge to scale-up from simplified proxies to ecosystem functioning since the inherent complexity of natural ecosystems hinders such an approach. One way to address this complexity is to track ecosystem processes through the lens of plant functional traits. Elevational gradients with diverse biotic and abiotic conditions offer ideal settings for inferring functional trait responses to environmental gradients globally. However, most studies have focused on differences in mean trait values among species and little is known on how intraspecific traits vary along wide elevational gradients and how this variability reflects ecosystem productivity.

2. We measured functional traits of the sub-shrub Koenigia mollis (Basionym: Polygonum molle) (a widespread species) in 11 populations along a wide elevational gradient (1515-4216 m) considering from subtropical forest to alpine biomes treeline in the central Himalayas. After measuring different traits (plant height, specific leaf area, leaf area, length of flowering branches, leaf carbon isotope – δ 13C, leaf carbon and leaf nitrogen concentrations), we investigated drivers on changes of these traits and also characterized their relationships with elevation, climate and net primary productivity (NPP).

4. Synthesis. Our study evidences how elevation-dependent climate variations affect ecosystem processes and functions. Intraspecific variability in functional traits is strongly driven by changes in water-energy dynamics, and reflects changes in community productivity over elevation. K. mollis, with one of the widest elevational ranges known to date, could be a model species to infer functional trait responses to environmental gradients globally. This study sheds new insight on how plants modify their basic ecological strategies to cope with changing environments.

3. All trait values decreased with increasing elevation, except for δ 13C that increased upwards. Likewise, most traits showed strong positive relationships with potential evapotranspiration (PET), while δ 13C exhibited a negative relationship. In this context, elevation-dependent water-energy dynamics is the primary driver of trait variations. Further, five key traits (plant height, specific leaf area, leaf carbon, leaf nitrogen and leaf δ 13C) explained 90.45% of variance in NPP.

National Science and Technology Major Project of China: Second National Science and Technology and Research Programme (STEP)（2019QZKK0000）

Peer reviewed