Aboveground-belowground stoichiometric characteristics and allometric relationships across different vegetation types...

We investigated the differences in ecological stoichiometric characteristics between aboveground and belowground components across five typical vegetation types in the Luo Mountain, Ningxia, including desert steppe, shallow mountain shrubland, broadleaf forest, mixed coniferous and broad-leaved forest, and coniferous forest. We examined the allometric growth relationships and homeostasis features of aboveground and belowground stoichiome-try, and analyze the main litter and soil nutrients influencing ecological stoichiometry. Results showed that leaf carbon (C), nitrogen (N), and phosphorus (P) contents were significantly higher than roots across all vegetation types. Desert steppe exhibited the highest leaf C content (464.45 g·kg^-1), being significantly higher than the three forest types, while its root C content was the lowest (235.44 g·kg^-1). Shallow mountain shrubland had the lowest leaf N and P contents (18.43 and 0.58 g·kg^-1, respectively), while its leaf C/N, C/P, and N/P were the highest (24.9, 795.5, and 31.9, respectively). The five vegetation types exhibited significant negative allometric relationship between leaf and root C contents. There was significant positive allometric relationship for leaf and root P contents. Leaf C-P exhibited negative allometry, whereas leaf N-P and root C-N showed isometric growth. Leaf and root N contents, along with C/N, C/P, and N/P, remained absolutely stable, while P content was consistently sensitive. Litter N/P had the greatest impact on leaf stoichiometry, while litter C/P most influenced root stoichio-metry. Aboveground nutrient allocation was higher than belowground part in all vegetation types of Luo Mountain, with shallow mountain shrubland adopting a more conservative resource utilization strategy, while the other vegetation types tended to be resource acquisition. There was a trade-off for C allocation in Luo Mountain plants, but a synergistic strategy for P allocation.