Leaf Multi-dimensional Stoichiometry as a Robust Predictor of
Productivity on the Tibetan Plateau
Abstract
Accurately predicting gross primary productivity (GPP) is crucial for
understanding carbon cycling; however, many studies have predominantly
investigated GPP using environmental metrics, overlooking the pivotal
role of functional traits as intermediaries between environment and GPP
and the predictive potential of GPP. Therefore, this study proposes and
employed a three-dimensional ”engine” framework to predict GPP and
tested it by leveraging functional traits from 2040 plant communities on
the Tibetan Plateau, incorporating environmental factors and the length
of the plant growing season. Our results challenged the conventional
emphasis that the environment plays a predominant role in predicting GPP
dynamics, showing that while the environment exerts a minor direct
effect, density traits of leaf and length of plant growing season
significantly contributed to GPP predictions. With a prediction accuracy
close to 0.90, this study underscores the feasibility of the
three-dimensional engine framework in GPP prediction However,
incorporating nitrogen-to-phosphorus ratio to the framework diminished
the model’s predictive accuracy. Within the stoichiometric dimension
alone, the prediction accuracy significantly increased with the number
of input traits, indicating a substantial potential for enhancing
predictive capability. Our research facilitates the dynamic, continuous,
and relatively accurate monitoring of GPP, contributing to a better
understanding of carbon cycle dynamics and supporting informed ecosystem
planning and management.