Potential productivity and change for tobosagrass in the Great Plains Landscape Conservation Cooperative area

This data set includes the relative production scenarios for tobosagrass [0.08(Temp) - 0.58]; this is the model from Epstein, et al. (1998). Soil texture (percent by weight) came from the Earth Systems Science Center (2008) which provided processed soils data from NRCS (gSSURGO), mean annual temperature (Celsius) and/or mean annual precipitation (millimeters) came from contemporary (1981 - 2010) estimates (Maurer et al. 2002) or a GCM. Global Climate Models (GCM) providing scenarios included: warmer-wetter scenario (CESM1-BGC, RCP4.5, Neale et al., 2010), warmer drier scenario (GISS-E2-R, RCP4.5, Schmidt, 2014), hotter-wetter scenario (Miroc-ESM, RCP8.5, Watanabe et al., 2011), and hotter-drier scenario (ACCESS 1-0, RCP8.5, Collier and Uhe, 2012). The results were binned into 7 classes based on breaks in the data and comparison with field observations.Climate change has been identified as a high-priority threat to grasslands by the Great Plains Landscape Conservation Cooperative (GPLCC) and as a priority change agent for grasslands in the Southern Great Plains Rapid Ecoregional Assessment by the Bureau of Land Management. The area of interest includes four level III ecoregions: the High Plains, Central Great Plains, Southwestern Tablelands, and the Nebraska Sand Hills. To address this priority information need for multiple stakeholders, we evaluated the potential vulnerability of four grassland communities (shortgrass, mixed-grass, and tallgrass prairies, and semiarid grasslands) using four climate change scenarios (representing hotter-drier, hotter-wetter, warmer-drier, and warmer-wetter conditions, relative to contemporary conditions). We used relative above-ground productivity models (Epstein et al., 1998) to evaluate the potential for change in productivity for each grassland community using mean annual precipitation and temperature for the contemporary climate (1981-2010) and the four climate scenarios (2016-2045), and the percent of sand, silt, and clay from the dominant soils component from the Natural Resource Conservation Service (Earth System Science Center, 2008). We selected two indicator species for each community: shortgrass prairie: blue grama (Bouteloua gracilis) and buffalo grass (Bouteloua dactyloides); mixedgrass prairie: sideoats grama (Bouteloua curtipendula) and little bluestem (Schizachyrium scoparium); tallgrass prairie: big bluestem (Andropogon gerardii) and Indiangrass (Sorghastrum nutans); and semiarid grasslands: black grama (Bouteloua eriopoda) and tobosagrass (Pleuraphis mutica). For each indicator species, we evaluated the potential change in relative productivity for each climate scenario compared to the contemporary climate. We used standard deviations to classify the differences between predicted productivity relative to the contemporary predicted productivity to evaluate whether the distributions of the indicator species were expected to remain stable, decrease, or expand for each scenario.Spatial data representing the estimated relative productivity of grassland species in the Southern Great Plains are provided as a 30 x 30-meter gridded surface (raster dataset). This information will help to address priority management questions for grassland conservation in the GPLCC and Southern Great Plains regions and can be used to inform other regional-level land management decisions.Collier, Mark, and Uhe, Peter, 2012, CMIP5 datasets from the ACCESS1.0 and ACCESS1.3 coupled climate models: Centre for Australian Weather and Climate Research Technical Report No. 059, 25 p.Earth System Science Center, 2008, Soil fraction data: College of Earth and Mineral Sciences at The Pennsylvania State University, accessed January 7, 2016, at http://www.soilinfo.psu.edu/index.cgi?soil_data&amp;conus&amp;data_cov&amp;fract&amp;datasets&amp;alb.Epstein, H.E., Lauenroth, W.K., Burke, I.C., and Coffin, D.P., 1998, Regional productivities of plant species in the Great Plains of the United States: Plant Ecology, v. 134, p. 173-195.Maurer, E.P., Wood, A.W., Adam, J.C., Lettenmaier, D.P., and Nijssen, B., 2002, A long-term hydrologically-based data set of land surface fluxes and states for the conterminous United States: Journal of Climate, v. 15, no. 22, p. 3237-3251.Natural Resources Conservation Service [NRCS], Surface Soils Geographic Database [gSSURGO], United States Department of Agriculture Natural Resources Conservation Service, at https://catalog.data.gov/dataset/gridded-soil-survey-geographic-gssurgo-10-database-for-the-conterminous-united-states-10-m.Neale, R.B.; Chen, Chih-Chieh; Gettelman, Andrew; Lauritzen, P.H.; Park, Sungsu; Williamson, D.L.; Conley, A.J.; Garcia, Rolando; Kinnison, Doug; Lamarque, Jean-Francois; Marsh, Dan; Mills, Mike; Smith, A.K.; Tilmes, Simone; Vitt, Francis; Morrison, Hugh; Cameron-Smith, Philip; Collins, W.D.; Iacono, M.J.; Easter, R.C.; Ghan, S.J.; Liu, Xiaohong; Rasch, P.J.; Taylor, M.A., 2010, Description of the NCAR Community Atmosphere Model (CAM 5.0): National Center for Atmospheric Research Technical Note NCAR/TN-486+STR, 274 p.Schmidt, G.A., M. Kelley, L. Nazarenko, R. Ruedy, G.L. Russell, I. Aleinov, M. Bauer, S.E. Bauer, M.K. Bhat, R. Bleck, V. Canuto, Y.-H. Chen, Y. Cheng, T.L. Clune, A. Del Genio, R. de Fainchtein, G. Faluvegi, J.E. Hansen, R.J. Healy, N.Y. Kiang, D. Koch, A.A. Lacis, A.N. LeGrande, J. Lerner, K.K. Lo, E.E. Matthews, S. Menon, R.L. Miller, V. Oinas, A.O. Oloso, J.P. Perlwitz, M.J. Puma, W.M. Putman, D. Rind, A. Romanou, M. Sato, D.T. Shindell, S. Sun, R.A. Syed, N. Tausnev, K. Tsigaridis, N. Unger, A. Voulgarakis, M.-S. Yao, and J. Zhang, 2014: Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive. J. Adv. Model. Earth Syst., 6, no. 1, 141-184, doi:10.1002/2013MS000265.Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., Nozawa, T., Kawase, H., Abe, M., Yokohata, T., Ise, T., Sato, H., Kato, E., Takata, K., Emori, S., and Kawamiya, M., 2011, MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments, Geosci. Model Dev., 4, 845-872, https://doi.org/10.5194/gmd-4-845-2011.

Tags: biota, colorado, disturbance, energy, environment, great-plains-landscape-conservation-cooperative, kansas, management, nebraska, new-mexico, oklahoma, rapid-ecological-assessment, south-dakota, southern-great-plains, texas, usgs-5cae314ee4b0c3b00654cee9, vegetation, wilderness, wildlife, wyoming
Organization: Department of the Interior