Restoration of resilient plant communities in response to environmental degradation is a critical task, and a changing climate necessitates the introduction of plant communities adapted to anticipated future conditions. Ecotypes of dominant species can affect associated organisms as well as ecosystem function. The extent of ecotypic variation in dominant tallgrass prairie species and the consequences of this variation for ecosystem functioning were studied by manipulating two potential drivers of plant community dynamics: climate and the soil microbial community. Climate was manipulated indirectly through the use of reciprocal restorations across a rainfall gradient where regional sources of dominant grasses Andropogon gerardii and Sorghastrum nutans were seeded with 8 other native species that occur in tallgrass prairie. Four dominant grass sources (originating from central Kansas [CKS], eastern Kansas [EKS], southern Illinois [SIL], or a mixture of these) were reciprocally planted within four sites that occurred across a precipitation gradient in western KS (Colby, KS), CKS (Hays, KS), EKS (Manhattan, KS) and SIL (Carbondale, IL). The three grass sources and mixture of sources were sown into plots according to a randomized complete block design at each sites (n=16, 4 plots / block at each site). Aboveground net primary productivity (ANPP) was measured at the end of the 2010 and 2011 growing season at each site. In 2010, total ANPP declined from western to eastern Kansas, but increased across the geographic gradient in 2011. The dominant grasses did not comprise the majority of community ANPP in WKS, CKS or SIL in either year but did contribute most to total ANPP at the EKS site in 2011. In 2010, volunteer forbs comprised the largest proportion of ANPP in WKS, whereas and in both years planted forbs comprised the largest proportion of ANPP in SIL. Ecotypic variation in ANPP of A. gerardii was not evident, but Sorghastrum nutans ANPP exhibited a site by source effect in 2010 that did not suggest a home site advantage. Variation in the competitive environment at each site may have masked ecotypic variation during community assembly. Further, ANPP responses suggest that grasslands in early stages of establishment may respond more stochastically to climatic variation than established grasslands. Longer term studies will clarify whether ecotypes of dominant prairie grasses affect ecosystem function or community trajectories differently during restoration. Ecotypes of dominant species may support different soil microflora, potentially resulting in plant-soil feedback. A second experiment tested for local adaptation of prairie plant assemblages to their soil microbial community. Native plant assemblages from Kansas and Illinois were tested for local adaptation to their `home' soil by reciprocally crossing soil and plant source in a greenhouse experiment. Seeds and soil were obtained from two remnant prairies, one in eastern Kansas and one in central Illinois, with similar species composition but differing climate. Seeds of four species (Andropogon gerardii, Elymus canadensis, Lespedeza capitata, Oligoneuron rigidum) common to both locations were collected, germinated, and transferred to pots to create 4-species assemblages from each region. Non-prairie (NP) soil from the edge of an Illinois agricultural field was also included as an inoculum treatment to increase relevance to restoration. Kansas and Illinois plant assemblages were subjected to a fully factorial combination of soil inocula [with associated microbial communities] (3 sources: KS, IL, NP) and soil sterilization treatment (sterilized or live). Plants were harvested after 20 weeks and soil was analyzed for microbial composition using phospholipid fatty acid (PLFA) markers. Soil sources had different nutrient concentrations and sterilization resulted in a flush of NH4+, which complicated detection of soil microbial effects. However, plant sources did exhibit variation in productivity responses to soil sources, with Kansas plants more responsive to live soil sources than Illinois plants. Despite confounding variation in soil fertility, soil inoculation was successful at manipulating soil microbial communities, and plant sources responded differently to soil sources. Consideration of feedback between soil and plants may be a missing link in steering restoration trajectories.

Scientists worldwide are increasingly faced with the need to assess geologic hazards for very infrequent events that have high consequence, for instance, in siting nuclear facilities for volcanic hazards. One of the methods currently being developed for such assessments is the Bayesian method. This paper outlines the Bayesian technique by focusing on the volcanic hazard assessment for the Armenia Nuclear Power Plant, (ANPP), which is located in a Quaternary volcanic field. The Bayesian method presented in this paper relies on the development of a probabilistic model based on the spatial distribution of past volcanic events and a geologic process model. To develop the probabilistic model a bivariate Gaussian kernel function is used to forecast probabilities based on estimates of λt, temporal recurrence rate and λs, spatial density. Shortcomings often cited in such purely probabilistic assessments are that it takes into account only known features and the event, new volcano formation, is rare and there is no opportunity for repeated experiments or uniform observations, the hallmarks of classical probability. One approach to improving such probabilistic models is to incorporate related geological data that reflect controls on vent distribution and would improve the accuracy of subsequent models. Geophysical data indicate that volcanism in Armenia is closely linked to crustal movement along major right lateral strike-slip fault systems that generates transtension across region. The surface expression of this transtension is pull-apart basins, filled with thick deposits of sediment, and antithetic normal faults. Volcanism in Armenia is concentrated in these deep sedimentary basins as is reflected in regional gravity data surveys. This means that low gravity anomalies are likely good indicators of future volcanic activity and therefore would improve probabilistic hazard models. Therefore, gravity data are transformed into a likelihood function and combined with the original probability model in quantitative fashion using Bayesian statistics. The result is a model that is based on the distribution of past events but modified to include pertinent geologic information. Using the Bayesian approach in this example increases the uncertainty, or range in probability, which reflects how well we actually know our probability estimate. Therefore, we feel it is appropriate to consider a range in probabilities for volcanic disruption of the ANPP, 1-4 x 10-6 per year (t=1 yr). We note that these values exceed the current International Atomic Energy Agency standard, 1 x 10-7 per year by at least one order of magnitude.

We used long-term observations of grassland aboveground net plant production (ANPP, 19392016), growing seasonal advanced very-high-resolution radiometer remote sensing normalized difference vegetation index (NDVI) data (1982-2016), and simulations of actual evapotranspiration (1912-2016) to evaluate the impact of Pacific Decadal Oscillation (PDO) and El Nino-Southern Oscillation (ENSO) sea surface temperature (SST) anomalies on a semiarid grassland in northeastern Colorado. Because ANPP was well correlated (R-2 = 0.58) to cumulative April to July actual evapotranspiration (iAET) and cumulative growing season NDVI (iNDVI) was well correlated to iAET and ANPP (R-2 = 0.62 [quadratic model] and 0.59, respectively), we were able to quantify interactions between the long-duration (15-30 yr) PDO temperature cycles and annual-duration ENSO SST phases on ANPP. We found that during cold-phase PDOs, mean ANPP and iNDVI were lower, and the frequency of low ANPP years (drought years) was much higher, compared to warm-phase PDO years. In addition, ANPP, iNDVI, and iAET were highly variable during the cold-phase PDOs. When NINO-3 (ENSO index) values were negative, there was a higher frequency of droughts and lower frequency of wet years regardless of the PDO phase. PDO and NINO-3 anomalies reinforced each other resulting in a high frequency of above-normal iAET (52%) and low frequency of drought (20%) when both PDO and NINO-3 values were positive and the opposite pattern when both PDO and NINO-3 values were negative (24% frequency of above normal and 48% frequency of drought). Precipitation variability and subsequent ANPP dynamics in this grassland were dampened when PDO and NINO-3 SSTs had opposing signs. Thus, primary signatures of these SSTs in this semiarid grassland are (1) increased interannual variability in ANPP during cold-phase PDOs, (2) drought with low ANPP occurring in almost half of those years with negative values of PDO and NINO-3, and (3) high precipitation and ANPP common in years with positive PDO and NINO-3 values.

The link between aboveground net primary productivity (ANPP) and resource gradients generated by complex terrain (solar radiation, nutrients, and moisture) has been established in the literature. Belowground ecosystem stocks and functions, such as soil organic carbon (SOC), dissolved organic carbon (DOC), and belowground productivity have also been related to the same topography and resource distributions, and therefore it is expected that they share spatial and temporal patterns with ANPP. However, stand structure on complex terrain is a function of multiple trajectories of forest development that interact with existing resource gradients, creating feedbacks that complicate the relationships between resource availability and ANPP. On a 96 ha forested watershed in the H.J. Andrews Experimental Forest in the Western Cascades range of Oregon, spatiotemporal heterogeneity in the secondary succession of a replanted Pseudotsuga menziesii stand following harvest results from the interaction of stand composition and abiotic drivers and may create unique "hot spots" and "hot moments" that complicate gradient relationships. In this dissertation, I tested the hypotheses that (chapter 3) multiple successional trajectories exist and can be predicted from a general linear model using specific topographic, historical, and biological parameters and that an estimated "maximum ANPP" may better represent stand characteristics than ANPP measured at a particular moment in time. I also test that (chapter 4) the distribution of light fraction carbon (LFC; C with a density of less than 1.85 g/cm��) is spatially variable, elevated on hardwood-initiated sites (hardwood biomass > 50% of biomass), and positively correlated with litter fall and ANPP. Chapter 4 also tests that heavy fraction carbon (HFC; C with a density of greater than 1.85 g/cm��) is a function of both soil mineralogy, stand composition, and ANPP, such that edges observed spatially in site mineralogy (changes in soil type) are reflected in sharp changes in the composition of the forest community and the magnitude of HFC stores. Finally, I hypothesized (chapter 5) that in complex terrain, dissolved organic carbon (DOC) export can be predicted from landform characteristics, relates to ANPP, and may be measured by several methods which are well-correlated with one another. In chapter 6, I discuss how litter fall measurements can be extrapolated to a watershed extent, and use litter fall as an example of the error that can occur in scaling up measurements taken at a small scale, within a heterogeneous stand on complex terrain, to a landscape scale extent. / Graduation date: 2013