Organ-specific non-structural carbohydrate allocation mediates growth-storage trade-offs and drought resilience in a semiarid forest

Background Non-structural carbohydrates (NSC) are central to plant carbon metabolism, acting as critical buffers that regulate tree growth and survival under environmental stress. In semiarid regions, increasing hydroclimatic variability poses significant challenges to forest stability. However, the mechanistic regulation of how organ-specific NSC allocation mediates metabolic trade-offs between structural growth and carbon storage, and how these partitioning strategies influence drought resilience, remains poorly understood. This study aims to unravel the functional significance of organ-specific carbon partitioning in a natural mixed forest in Northern China. Results Our findings reveal that radial growth and drought responses are governed by organ-specific allocation logic rather than total NSC pools. Radial growth was positively associated with starch concentrations in leaves and fine roots and with soluble sugar concentrations in branches ( P  < 0.05). Conversely, a significant negative correlation was found between radial growth and starch concentrations in branches ( P  < 0.05), supporting a clear growth–storage trade-off mediated by carbon sequestration in perennial tissues. Furthermore, higher leaf soluble sugar concentrations were the primary physiological determinant of enhanced post-drought growth recovery ( P  < 0.05), indicating that the mobilization of short-term, readily available carbon pools is essential for restoring physiological function following extreme water deficit. Conclusions This study demonstrates that drought resilience in semiarid forests depends less on absolute NSC concentrations than on the strategic partitioning of carbon among functional organs and chemical forms. These results provide mechanistic insights into the regulation of plant carbon metabolism and its role in underpinning forest stability. Our work offers a framework for refining vegetation models by integrating organ-specific carbon allocation logic to better predict forest responses to global climate change.