RESPONSES OF COMMUNITY STRUCTURE AND ECOSYSTEM FUNCTIONING TO CLIMATE CHANGE – META-ANALYSIS, MODELING, EXPERIMENTAL STUDY AND DATA-MODEL FUSION

Observations, experimental studies and modeling endeavors all show that global climate change, mainly increased surface air temperature and associated change in precipitation regime, has caused impacts on plant community structure and terrestrial ecosystem functioning. The direction, rate and magnitude of ecosystem responses to climate change vary across time and space. Mechanisms and feedbacks responsible for the ecosystem responses are complex, from physiological and phonological to community-shift driven. Therefore, to advance our understanding, it is of great importance to recognize general patterns in the ecosystem responses and identify probably underlying mechanisms. In this dissertation, I attempted to generalize central patterns of effects of warming and altered precipitation on plant community and ecosystem carbon (C) dynamics and identify mechanisms using multiple approaches including meta-analysis, manipulative experiment, ecosystem C modeling and model-data fusion. In the first study, I conducted a modeling analysis of the effects of extreme drought on two key ecosystem processes, production and respiration, and to provide broader context I complemented this with a synthesis of published results across multiple ecosystems. The synthesis indicated that across a broad range of biomes gross primary production (GPP) generally was more sensitive to extreme drought than was ecosystem respiration (ER). Furthermore, this differential sensitivity between production and respiration increased as drought severity increased and occurred only in grassland ecosystems but not in evergreen needle-leaf and broad-leaf forests or woody savannahs. The modeling analysis was designed to better understand the mechanisms underlying this pattern and focused on four grassland sites arrayed across the Great Plains, USA. Model results consistently showed that net primary productivity (NPP) was reduced more than heterotrophic respiration (Rh) by extreme drought (i.e., 67% reduction in annual ambient rainfall) at all four study sites. The sensitivity of NPP to drought was directly attributable to rainfall amount, whereas sensitivity of Rh to drought was driven by soil drying, reduced carbon (C) input and a drought-induced reduction in soil C content, a much slower process. However, differences in reductions in NPP and Rh diminished as extreme drought continued due to a gradual decline in the soil C pool leading to further reductions in Rh. The findings suggest that responses of production and respiration differ in magnitude, occur on different timescales and are affected by different mechanisms under extreme, prolonged drought. In the second study, I used a meta-analysis approach to quantify the responses of community productivity and structure to both increased and decreased precipitation by synthesizing 44 experimental studies in grassland ecosystem. The results showed that decreased precipitation suppress aboveground net primary productivity (ANPP) by 16.7% and belowground net primary productivity (BNPP) by 5.4%; increased precipitation enhanced ANPP by 25.7% but had no impact on BNPP; community structure showed little responses to precipitation change, except species richness responding negatively to decreased precipitation by 8%. Response of ANPP to altered precipitation was significantly greater than that of BNPP and response of ANPP to increased precipitation was stronger than that to decreased precipitation. In general, ANPP of different PFTs, except C4 showed positive and negative responses to decreased and increased precipitation, respectively, but we did not detect any difference in responses among the PFTs. The response ratios of dominant PFTs to altered precipitation positively correlated with that of the whole plant community, with the slope less than 1. Productivity sensitivity to both precipitation change declined exponentially with mean annual precipitation. Our analyses provide a complementary perspective to long-term observational productivity-precipitation relationship, suggest that changes in ecosystem functioning driven by community shift under precipitation change was uncommon and indicate that future greater precipitation variability could overall favor plant growth. Our findings have implications for both modeling community and experimental studies. In the third study, I explored the long-term responses of a prairie plant community to 14-year (2000-2013) manipulations of climate warming and clipping in Oklahoma, USA. Community composition was resistant to experimental warming in the first seven years, but started to show responses since the eighth year; clipping consistently affected community composition over the years. Compositional change under long-term warming was mainly contributed by one invasive species and three dominant species. The negative correlations in relative abundance between the invasive species and the dominant species suggest inter-specific competition. Community structure (i.e., richness, evenness and diversity) had no overall response to experimental warming. However, in 2007, the extreme wet year, warming reduced species richness by 30%. Clipping promoted species richness by 10% on average over the 14 years but decreased community evenness. Warming did not interact with clipping in influencing the plant community variables. Our study provides experimental evidence for long-term shifts in plant community composition due to climate warming and revealed novel mechanisms (i.e., species invasion and associated biotic interactions) underlying the long-term shift. The results also suggest that climate extremes may elicit or advance community responses to climate warming. The findings have implications for terrestrial carbon modeling with dynamic global vegetation. In the fourth study, measurements from a nine-year warming experimental site in a tallgrass prairie were assimilated into a terrestrial ecosystem C cycle model to assess warming effect on key model parameters and to quantify uncertainties of long-term C projection. Warming decreased allocation of gross primary production (GPP) to shoot, and turnover rate of the live C pools (i.e., shoot and root C), but increased the turnover rates of litter and fast soil C pools. Consequently, warming increased live C pools, but decreased litter and soil C pools, and overall decreased total ecosystem C in a 90-year model projection. Information content gained from assimilated datasets was much greater for plant, litter and fast soil C pools than for slow and passive soil C pools. Sensitivity analysis revealed that fast turnover C pools were most sensitive to their turnover rates and modest to C-input related parameters on both short-term and long-term time scales. However, slow turnover C pools were sensitive to turnover rate and C input in long-term prediction, not in short-term prediction. As a result , total soil and ecosystem C pools were generally insensitive to any parameter in short term, but determined by turnover rates of the fast, slow and passive soil C and transfer coefficients from upstream C to slow and passive C pools. Our findings suggest that data assimilation is an effective tool to explore the effect of warming on C dynamics; the nine-year field data contribute more information for the fast C processes than for the slow C processes ; and C cycle model parameters change with warming, and models need to account for that phenomenon not to produce bias in C projections. However, warming-induced changes in parameter values also suggest that some important ecosystem processes may be missing or not adequately represented in the ecosystem C models. These studies demonstrated that the patterns in responses of community structure and ecosystem functioning to climate change could be generalized and showed the complexity of potential mechanisms and feedbacks underlying the ecological responses. Future research is still needed in synthesizing existing observations and experiments, unifying them through statistical and process-based modeling and data assimilation and developing theories in this research area