Disentangling the relationship between environmental drivers and productivity in subalpine wet grasslands.

INTRODUCTION

Grasslands exhibit significant variability in productivity across fine spatial scales, which is crucial for understanding terrestrial carbon cycling, particularly under global climate change. While alpine grasslands have been extensively studied, subalpine wet grasslands (2000-4000 m) remain underexplored. Investigating their productivity and responses to environmental factors is essential for a comprehensive understanding of ecosystem dynamics in these regions.

METHOD

We applied destructive sampling techniques, optimized grassland investigation, and employed multivariate modeling to examine how different environmental variables influence grassland biomass. An 80-plot field-based dataset was established in a subalpine wet grassland.

RESULTS

Our findings reveal that plant biomass peaked at elevations between 3400 and 3500 m. Belowground biomass accounted for 85% of total productivity, with the majority contributed by dominant species. Vegetation-related variables, such as coverage and root/shoot ratio, were the primary determinants of aboveground biomass, whereas soil properties were key regulators of belowground biomass. Although direct and indirect effects of landform and climatic factors influenced total biomass, the patterns of total and belowground biomass were consistent. The results underscore the significant positive impact of vegetation cover, root-to-shoot ratio, and soil conditions on grassland productivity. Notably, soil organic carbon, water content, and the nitrogen-to-phosphorus ratio affected belowground biomass.

DISCUSSION

These insights enhance our understanding of the intricate interactions between climate, soil, landform, and plant communities in influencing grassland biomass and highlight the importance of preserving plant diversity and maintaining optimal soil conditions in subalpine wet grasslands. One grassland does not fit all; fine-scale classification is essential to capture the variability in productivity across different grassland types.