DISSERTATION RESEARCH: Mechanisms of asymmetrical ecosystem responses to precipitation extremes in xeric vs. mesic grasslands

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Native grasslands once covering millions of acres across the US have been reduced to a tiny fraction of their former distribution. These remaining grasslands play important roles in the cycling of carbon and nutrients, biodiversity, and as sources for restoration. The health and seasonal dynamics of the remaining native grasslands in the central US are controlled by the amount and timing of rainfall. Across the central US rainfall variability is predicted to increase, with greater frequencies of extreme wet and dry years expected. Grasslands are likely to be particularly sensitive to such changes, with evidence that these systems may be more sensitive to wet versus dry years. However, studies rarely include assessment of extreme years. This Doctoral Dissertation Improvement Grant (DDIG) award, will experimentally assess the sensitivity of the grassland composition, carbon cycle, and nutrient cycling to rainfall extremes within an intact semi-arid grassland system. The research results will be valuable to natural resource and land managers in managing or restoring native grasslands. Graduate and undergraduate students will be trained and participate in the research. This research project aims to advance a fundamental understanding of how ecosystems will respond to predicted increases in environmental variability and extremity. The grassland ecosystem's response to rainfall extremes is hypothesized to be non-linear and bi-modal. A novel experimental treatment is proposed to expose semi-arid grassland to a large gradient of growing season precipitation that will be able to detect nonlinearities in response variables that are otherwise not detectable in few-factor experimental design. The research will allow assessment of the comparative sensitivities of key carbon cycle components to dry versus wet years, and how this sensitivity may change when precipitation transitions from moderate to extreme deviations from mean levels.