HR: 1340h
AN: B23C-1496    [Abstracts]
TI: The Use of Bayesian Modeling to Assess the Impact of Altered Precipitation on Leaf-level Carbon Exchange in Four Desert Savanna Ecosystems
AU: * Patrick, L
EM: lisa.patrick@ttu.edu
AF: Texas Tech University, Department of Biological Sciences, MS43131, Lubbock, TX 79409, United States
AU: Ogle, K
EM: kogle@uwyo.edu
AF: University of Wyoming, Department of Botany, Box 3165, Laramie, WY 82071, United States
AU: Tissue, D
EM: david.tissue@ttu.edu
AF: Texas Tech University, Department of Biological Sciences, MS43131, Lubbock, TX 79409, United States
AU: Tissue, D
EM: david.tissue@ttu.edu
AF: University of Western Sydney, Locked Bag 1797, Penrith South DC, NSW 1797, Australia
AU: Cable, J
EM: jcable1@uwyo.edu
AF: University of Wyoming, Department of Botany, Box 3165, Laramie, WY 82071, United States
AB: Savannas are complex ecosystems with diverse plant communities and spatially variable nutrient and carbon dynamics. In semi-arid regions, savannas are rapidly changing as a result of climate change and/or land-use, both of which have the potential to alter carbon cycling processes. To determine the potential impacts of climate change on savanna systems, it is critical to understand the processes governing vegetation dynamics across the diverse range of savanna ecosystem types. Because water is the primary driver of biological activity in these ecosystems, changes in precipitation frequency and magnitude may significantly affect plant community composition and ecosystem carbon cycling through effects on leaf-level carbon dynamics. Here, we utilized photosynthesis data and models to explore the underlying mechanisms responsible for changes in leaf-level carbon exchange under altered precipitation. Our objective was to determine whether dominant plants in four North American deserts exhibited a common photosynthetic response to precipitation manipulations. In the summer of 2005 and 2006, photosynthetic CO₂- and light-response curves were measured on the dominant plant functional groups (grasses and shrubs) in the Great Basin, Mojave, Sonoran, and Chihuahuan deserts. We used a hierarchical Bayesian modeling framework to integrate the extensive field data with a biochemical-based photosynthesis model, yielding estimates of photosynthetic parameters (e.g. rate of daytime respiration, maximum rate of carboxylation, and maximum rate of electron transport). The modeling results indicated that, generally, plant photosynthesis parameters were conserved across all desert sites and plant species. There is, however, evidence that supplemental precipitation affected photosynthetic responses as some species differed in key biochemical parameters under this treatment. This result suggests that in these ecosystems changes in precipitation associated with climate change have the potential to alter species composition through plant controls on ecosystem carbon cycling. The hierarchical Bayesian approach that we employed facilitated the estimation of key leaf-level carbon exchange parameters for native desert plants from geographically distinct regions. Such integration of data and models is expected to improve estimates of leaf to ecosystem carbon flux processes that are critical to understanding the impacts of climate change on complex systems such as desert savannas.
DE: 0428 Carbon cycling (4806)
DE: 0466 Modeling
DE: 0476 Plant ecology (1851)
DE: 1637 Regional climate change
DE: 1854 Precipitation (3354)
SC: Biogeosciences [B]
MN: 2007 Fall Meeting