Leaf multi-dimensional stoichiometry as a robust predictor of productivity on the Tibetan Plateau.

IF 9.3 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Xin Li, Jiahui Zhang, Kathrin Rousk, Yinghua Zhang, Yi Jiao, Pu Yan, Nianpeng He
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引用次数: 0

Abstract

Accurately predicting gross primary productivity (GPP) is crucial for understanding carbon cycling; however, most studies have predominantly investigated GPP using only environmental metrics, overlooking the pivotal role of functional traits as intermediaries between the environment and GPP and the predictive potential of GPP. Therefore, this study developed a three-dimensional "engine" framework to predict GPP and tested it by leveraging functional traits from 2,040 plant communities on the Tibetan Plateau, incorporating environmental factors and the length of the plant-growing season. Our results highlight that while the environment exerts a dominant direct influence on GPP dynamics, the contribution of leaf density traits to GPP prediction should not be overlooked. The proposed framework achieved a prediction accuracy close to 0.92, underscoring its feasibility in GPP prediction. However, incorporating the nitrogen-to-phosphorus ratio into 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. In the dimension of environmental factors, incorporating more environmental factors does not significantly enhance the model's predictive ability. 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.

叶片多维化学计量作为青藏高原生产力的可靠预测因子。
准确预测总初级生产力(GPP)对理解碳循环至关重要;然而,大多数研究主要只使用环境指标来研究GPP,忽视了功能性状作为环境和GPP之间中介的关键作用以及GPP的预测潜力。因此,本研究利用青藏高原2040个植物群落的功能性状,结合环境因素和植物生长季节长度,构建了预测GPP的三维“引擎”框架,并对其进行了验证。我们的研究结果强调,虽然环境对GPP动态具有主要的直接影响,但叶密度性状对GPP预测的贡献不容忽视。该框架的预测精度接近0.92,表明了该框架在GPP预测中的可行性。然而,将氮磷比纳入框架会降低模型的预测准确性。仅在化学计量维度内,随着输入性状数量的增加,预测精度显著提高,表明预测能力有很大的提升潜力。在环境因子维度上,加入更多的环境因子并不会显著提高模型的预测能力。我们的研究促进了对GPP的动态、连续和相对准确的监测,有助于更好地了解碳循环动态,并为明智的生态系统规划和管理提供支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Integrative Plant Biology
Journal of Integrative Plant Biology 生物-生化与分子生物学
CiteScore
18.00
自引率
5.30%
发文量
220
审稿时长
3 months
期刊介绍: Journal of Integrative Plant Biology is a leading academic journal reporting on the latest discoveries in plant biology.Enjoy the latest news and developments in the field, understand new and improved methods and research tools, and explore basic biological questions through reproducible experimental design, using genetic, biochemical, cell and molecular biological methods, and statistical analyses.
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