Species-specific physiological parameters regulate adaptive resource allocation and allometric growth patterns in C3 ( Leymus chinensis ) and C4 ( Hemarthria altissima ) grasses under changing precipitation regimes

BACKGROUND AND AIMS: Plant biomass allocation serves as a core adaptive strategy for buffering against environmental fluctuations. Optimal partitioning theory (OPT) and allometric partitioning theory (APT) provide distinct theoretical frameworks to explain the underlying resource allocation dynamics. Yet, how functional traits govern biomass allocation patterns and interspecific differences in C3 and C4 grasses under altered precipitation regimes resulting from variations in precipitation amount and frequency remains poorly understood. METHODS: A factorial pot experiment was conducted using a randomized complete design with three precipitation amounts (175, 350, 525 mm) and three precipitation frequencies (10, 20, 30 events) to investigate physiological traits and biomass allocation strategies of a C3 grass (Leymus chinensis) and a C4 grass (Hemarthria altissima). KEY RESULTS: Our results revealed: (1) H. altissima (C4) exhibited lower physiological sensitivity to precipitation variability compared to L. chinensis (C3); (2) both the studied C3 and C4 grasses showed significant allometric relationships between above- (AGB) and below-ground (BGB) biomass (P < 0.001), yet the root-to-shoot ratio (R/S) remained responsive to precipitation treatments even after accounting for plant size effects; and (3) a principal component analysis (PCA) identified two orthogonal axes of physiological variation: the first principal component (PC1, photosynthetic growth dimension) was positively correlated with total biomass, while water use efficiency (WUE, analysed as an independent water balance dimension) was associated with greater BGB, lower AGB and higher R/S. CONCLUSIONS: These findings demonstrated that biomass allocation in these grasses is co-regulated by allometric constraints and environmental plasticity, aligning with the complementary predictions of APT and OPT, and mediated by distinct physiological dimensions (i.e. PC1 driving growth and WUE governing allocation). While the C3 and C4 grasses share these regulatory mechanisms, they exhibit divergent physiological sensitivities to precipitation variability, highlighting the necessity of integrating functional trait dynamics when modelling plant responses to changing precipitation regimes.