02215nas a2200241   4500008004100000022001400041245012300055210006900178300001400247490000800261520148100269653001801750653000901768653001401777653001201791653001401803100001601817700002301833700001801856700001501874700001801889856006601907       2016                        eng d  a0029-854900aTight coupling of leaf area index to canopy nitrogen and phosphorus across heterogeneous tallgrass prairie communities0 aTight coupling of leaf area index to canopy nitrogen and phospho  a889 - 8980 v1823 a<p>Nitrogen (N) and phosphorus (P) are limiting nutrients for many plant communities worldwide. Foliar N and P along with leaf area are among the most important controls on photosynthesis and hence productivity. However, foliar N and P are typically assessed as species level traits, whereas productivity is often measured at the community scale. Here, we compared the community-level traits of leaf area index (LAI) to total foliar nitrogen (TFN) and total foliar phosphorus (TFP) across nearly three orders of magnitude LAI in grazed and ungrazed tallgrass prairie in north-eastern Kansas, USA. LAI was strongly correlated with both TFN and TFP across communities, and also within plant functional types (grass, forb, woody, and sedge) and grazing treatments (bison or cattle, and ungrazed). Across almost the entire range of LAI values and contrasting communities, TFN:TFP ratios indicated co-limitation by N and P in almost all communities; this may further indicate a community scale trend of an optimal N and P allocation per unit leaf area for growth. Previously, results from the arctic showed similar tight relationships between LAI:TFN, suggesting N is supplied to canopies to maximize photosynthesis per unit leaf area. This tight coupling between LAI, N, and P in tallgrass prairie suggests a process of optimal allocation of N and P, wherein LAI remains similarly constrained by N and P despite differences in species composition, grazing, and canopy density.</p>10aCo-limitation10afire10agrassland10agrazers10anutrients1 aKlodd, A.E.1 aNippert, Jesse, B.1 aRatajczak, Z.1 aWaring, H.1 aPhoenix, G.K.  uhttps://link.springer.com/article/10.1007%2Fs00442-016-3713-303241nas a2200193   4500008004100000245009100041210006900132260004300201490001400244520262400258653002402882653001202906653001902918653001802937653001402955100001802969700002302987856003703010       2011                        eng d00aResponses of switchgrass (Panicum virgatum L.) to precipitation amount and temperature0 aResponses of switchgrass Panicum virgatum L to precipitation amo  aManhattan, KSbKansas State University0 vMS Thesis3 a<p>Anthropogenic climate change is likely to alter the function and composition of ecosystems worldwide through increased precipitation variability and temperatures. To predict ecosystem responses, a greater understanding of the physiological and growth responses of plants is required. Dominant species drive ecosystem responses, and it is essential to understand how they respond to understand potential ecosystem changes. Dominant species, such as switchgrass (Panicum virgatum L.), posses large genotypic and phenotypic variability, which will impact the degree of responses to projected climate changes. I studied the physiological and growth responses of switchgrass, a common perennial warm-season C4 grass that is native to the tallgrass prairie, to alterations in precipitation amount and temperature. The first experiment I conducted focused on the responses of three ecotypes of P. virgatum to three precipitation regimes (average, 25% below, 25% above). I concluded that the physiological responses of photosynthesis, stomatal conductance, transpiration, dark-adapted fluorescence, and mid-day water potential in P. virgatum were explained by ecotypic differences. Robust responses to altered precipitation were seen in the water use efficiency, mid-day water potential, and aboveground biomass. Ecotypic differences were also seen in several aboveground biomass variables, and most strikingly in flowering times and rates. There were few interactions between ecotype and precipitation, suggesting precipitation is a strong driver of biomass production, whereas adaption of ecotypes to their local environment affects physiological processes. A second experiment studied the response of local populations of P. virgatum to nocturnal warming. Results showed significant differences in daytime E, daytime gs, and flowering phenology between treatments. Differences in aboveground biomass were between topographic positions. I concluded that water availability, based on topographic position, is a strong driver of P. virgatum aboveground biomass production, but nocturnal warming has the potential to impact flowering phenology, physiological responses, and exacerbate plant water stress. I also reviewed the literature on the ecological effects of implementing switchgrass cultivation for biofuel. From the literature review, I concluded that large-scale switchgrass cultivation will have widespread ecological impacts. If landscape heterogeneity is maintained through harvest rotations, no till farming, and mixed species composition, ecosystem services can be maintained while providing economic value.</p>10aAboveground biomass10aBiofuel10aClimate change10aEcophysiology10agrassland1 aHartman, J.C.1 aNippert, Jesse, B.  uhttp://hdl.handle.net/2097/1072002991nas a2200217   4500008004100000245009600041210006900137300001100206490000800217520225200225653000902477653001902486653001402505653003002519653001802549653002202567100002302589700002002612700001902632856012202651       2006                        eng d00aIntra-annual rainfallvariability and grassland productivity: can the past predictthe future0 aIntraannual rainfallvariability and grassland productivity can t  a65 -740 v1843 aPrecipitation quantity has been shown to influence grassland aboveground net primary productivity (ANPP) positively whereas experimental increases in of temporal variability in water availability commonly exhibit a negative relationship with ANPP. We evaluated long term ANPP datasets from the Konza Prairie Long Term Ecological Research (LTER) program (1984–1999) to determine if similar relationships could be identified based on patterns of natural variability (magnitude and timing) in precipitation. ANPP data were analyzed from annually burned sites in native mesic grassland and productivity was partitioned into graminoid (principally C4 grasses) and forb (C3 herbaceous) components. Although growing season precipitation amount was the best single predictor of total and grass ANPP (r 2=0.62), several measures of precipitation variability were also significantly and positively correlated with productivity, independent of precipitation amount. These included soil moisture variability, expressed as CV, for June (r 2=0.45) and the mean change in soil moisture between weekly sampling periods in June and August (%wv) (r 2=0.27 and 0.32). In contrast, no significant relationships were found between forb productivity and any of the precipitation variables (p>0.05). A multiple regression model combining precipitation amount and both measures of soil moisture variability substantially increased the fit with productivity (r 2=0.82). These results were not entirely consistent with those of short-term manipulative experiments in the same grassland, however, because soil moisture variability was often positively, not negatively related to ANPP. Differences in results between long and short term experiments may be due to low variability in the historic precipitation record compared to that imposed experimentally as experimental levels of variability exceeded the natural variability of this dataset by a factor of two. Thus, forecasts of ecosystem responses to climate change (i.e. increased climatic variability), based on data constrained by natural and recent historical rainfall patterns may be inadequate for assessing climate change scenarios if precipitation variability in the future is expected to exceed current levels.10aANPP10aClimate change10agrassland10aPrecipitation variability10asoil moisture10atallgrass prairie1 aNippert, Jesse, B.1 aKnapp, Alan, K.1 aBriggs, J., M.  uhttp://knz.lternet.edu/content/intra-annual-rainfallvariability-and-grassland-productivity-can-past-predictthe-future