Metabolic growth mechanisms and theoretical growth potential of global woody plant communities

Abstract

Predicting the growth and maximum biomass (M_max) of woody plant communities (WPCs) remains a central challenge in terrestrial ecology due to the complex and heterogeneous nature of tree growth. While metabolic scaling theory (MST) provides a valuable conceptual framework, it remains limited in its ability to fully explain community-level growth or carbon dynamics. To address this limitation, we developed an iterative growth model for forests (IGMF), built upon an iterative growth framework grounded in MST's core principles and further incorporating the self-thinning effect. The IGMF and its extensions suggest that community-level growth, net primary productivity (NPP), and other key components of the carbon budget—including gross primary productivity, autotrophic respiration, organ turnover, and non-structural carbohydrate storage—may be approximated as functions of current biomass, biomass-specific maintenance respiration, stand age, or M_max. These relationships provide a basis for estimating the global M_max of WPCs during 2018–2020 at approximately 1440 ± 26 Pg (1 Pg = 1 × 10¹⁵ g), with an additional biomass potential of about 510 Pg under current conditions. However, machine learning projections suggest that this potential may decline by up to 246 Pg by 2100, primarily within evergreen broadleaf forests. Our analyses also indicate that species richness, by promoting functional convergence, can amplify the negative effects of temperature and precipitation seasonality on M_max. In contrast, warming in the Northern Hemisphere may favor M_max accumulation in open shrublands. Together, these results help to clarify the growth dynamics of WPCs and suggest a possible shift in the major contributors to terrestrial carbon sequestration—from forests to shrublands—under future climate scenarios.