Sensitive Indicators of Zonal Stipa Species to Changing Temperature and Precipitation in Inner Mongolia Grassland, China.

Climate change often induces shifts in plant functional traits. However, knowledge related to sensitivity of different functional traits and sensitive indicator representing plant growth under hydrothermal change remains unclear. Inner Mongolia grassland is predicted to be one of the terrestrial ecosystems which are most vulnerable to climate change. In this study, we analyzed the response of four zonal Stipa species (S. baicalensis, S. grandis, S. breviflora, and S. bungeana) from Inner Mongolia grassland to changing temperature (control, increased 1.5, 2, 4, and 6°C), precipitation (decreased 30 and 15%, control, increased 15 and 30%) and their combined effects via climate control chambers. The relative change of functional traits in the unit of temperature and precipitation change was regarded as sensitivity coefficient and sensitive indicators were examined by pathway analysis. We found that sensitivity of the four Stipa species to changing temperature and precipitation could be ranked as follows: S. bungeana > S. grandis > S. breviflora > S. baicalensis. In particular, changes in leaf area, specific leaf area and root/shoot ratio could account for 86% of the changes in plant biomass in the four Stipa species. Also these three measurements were more sensitive to hydrothermal changes than the other functional traits. These three functional indicators reflected the combination of plant production capacity (leaf area), adaptive strategy (root/shoot ratio), instantaneous environmental effects (specific leaf area), and cumulative environmental effects (leaf area and root/shoot ratio). Thus, leaf area, specific leaf area and root/shoot ratio were chosen as sensitive indicators in response to changing temperature and precipitation for Stipa species. These results could provide the basis for predicting the influence of climate change on Inner Mongolia grassland based on the magnitude of changes in sensitive indicators.