abstract: Climate change will result not only in changes in the mean state of climate but also on changes in variability. However, most studies of the impact of climate change on ecosystems have focused on the effect of changes in the central tendency. The broadest objective of this thesis was to assess the effects of increased interannual precipitation variation on ecosystem functioning in grasslands. In order to address this objective, I used a combination of field experimentation and data synthesis. Precipitation manipulations on the field experiments were carried out using an automated rainfall manipulation system developed as part of this dissertation. Aboveground net primary production responses were monitored during five years. Increased precipitation coefficient of variation decreased primary production regardless of the effect of precipitation amount. Perennial-grass productivity significantly decreased while shrub productivity increased as a result of enhanced precipitation variance. Most interesting is that the effect of precipitation variability increased through time highlighting the existence of temporal lags in ecosystem response. Further, I investigated the effect of precipitation variation on functional diversity on the same experiment and found a positive response of diversity to increased interannual precipitation variance. Functional evenness showed a similar response resulting from large changes in plant-functional type relative abundance including decreased grass and increased shrub cover while functional richness showed non-significant response. Increased functional diversity ameliorated the direct negative effects of precipitation variation on ecosystem ANPP but did not control ecosystem stability where indirect effects through the dominant plant-functional type determined ecosystem stability. Analyses of 80 long-term data sets, where I aggregated annual productivity and precipitation data into five-year temporal windows, showed that precipitation variance had a significant effect on aboveground net primary production that is modulated by mean precipitation. Productivity increased with precipitation variation at sites where mean annual precipitation is less than 339 mm but decreased at sites where precipitation is higher than 339 mm. Mechanisms proposed to explain patterns include: differential ANPP response to precipitation among sites, contrasting legacy effects and soil water distribution. Finally, increased precipitation variance may impact global grasslands affecting plant-functional types in different ways that may lead to state changes, increased erosion and decreased stability that can in turn limit the services provided by these valuable ecosystems. / Dissertation/Thesis / Doctoral Dissertation Biology 2014

In addition to causing an increase in mean temperatures, climate change is also altering precipitation regimes across the globe. General circulation models project both latitude-dependent changes in precipitation mean and increases in precipitation variability. These changes in water availability will impact terrestrial primary productivity, the fixation of carbon dioxide into organic matter by plants. In my thesis, I addressed the following three questions: 1.) What will be the relative effect of changes in the mean and standard deviation of annual precipitation on mean annual primary production? 2.) Which ecosystems will be the most sensitive to changes in precipitation? 3.) Will increases in production variability be disproportionately greater than increases in precipitation variability? I gathered 58 time series of annual precipitation and aboveground net primary production (ANPP) from long-term ecological study sites across the globe. I quantified the sensitivity of ANPP at each site to changes in precipitation mean and variance. My results indicated that mean ANPP is about 40 times more sensitive to changes in precipitation mean than to changes in precipitation variance. I showed that semi-arid ecosystems such as shortgrass steppe in Colorado or typical steppe in Inner Mongolia may be the most sensitive to changes in precipitation mean. At these sites and several others, a 1% change in mean precipitation may result in a change in ANPP that is greater than 1%. To address how increases in interannual precipitation variability will impact the variability of ANPP, I perturbed the variability of observed precipitation time series and evaluated the impact of this perturbation on predicted ANPP variability. I found that different assumptions about the precipitation-ANPP relationship had different implications for how increases in precipitation variability will impact ANPP variability. Increases in ANPP variability were always directly proportional to increases in precipitation variability when ANPP was modeled as a simple linear or a lagged function of precipitation. However, when ANPP was modeled as a nonlinear, saturating function of precipitation, increases in ANPP variability were disproportionately low compared to increases in precipitation variability during wet years but disproportionately high during dry years. My thesis addresses an existing research gap regarding the long-term impact of increases in interannual precipitation variability on key ecosystem functioning. I showed that increases in precipitation variability will have negligible impacts on ANPP mean and have disproportionately large impacts on ANPP variability only when ANPP is a concave down, nonlinear function of precipitation. My work also demonstrates the importance of the precipitation-ANPP relationship in determining the magnitude of impacts to ANPP caused by changes in precipitation. Finally, my thesis highlights the potential for considerable changes in ANPP variability due to increases in precipitation variability.

In the southwest United States, the current prolonged warm drought is similar to the predicted future climate change scenarios for the region. This study aimed to determine patterns in vegetation response to the early 21st century drought across multiple biomes. We hypothesized that different biomes (forests, shrublands, and grasslands) would have different relative sensitivities to both climate drivers (precipitation and temperature) and legacy effects (previous-year's productivity). We tested this hypothesis at eight Ameriflux sites in various Southwest biomes using NASA Moderate-resolution Imaging Spectroradiometer Enhanced Vegetation Index (EVI) from 2001 to 2013. All sites experienced prolonged dry conditions during the study period. The impact of combined precipitation and temperature on Southwest ecosystems at both annual and sub-annual timescales was tested using Standardized Precipitation Evapotranspiration Index (SPEI). All biomes studied had critical sub-annual climate periods during which precipitation and temperature influenced production. In forests, annual peak greenness (EVImax) was best predicted by 9-month SPEI calculated in July (i.e., January-July). In shrublands and grasslands, EVImax was best predicted by SPEI in July through September, with little effect of the previous year's EVImax. Daily gross ecosystem production (GEP) derived from flux tower data yielded further insights into the complex interplay between precipitation and temperature. In forests, GEP was driven by cool-season precipitation and constrained by warm-season maximum temperature. GEP in both shrublands and grasslands was driven by summer precipitation and constrained by high daily summer maximum temperatures. In grasslands, there was a negative relationship between temperature and GEP in July, but no relationship in August and September. Consideration of sub-annual climate conditions and the inclusion of the effect of temperature on the water balance allowed us to generalize the functional responses of vegetation to predicted future climate conditions. We conclude that across biomes, drought conditions during critical sub-annual climate periods could have a strong negative impact on vegetation production in the southwestern United States.