| The purpose of this study is to examine how changes in annual precipitation, its seasonal distribution, and its intra- and interannual variability might alter the structure and function of arid and semiarid plant communities. This general issue is addressed in an integrative way, using creosotebush (Larrea tridentata) as a model system and by coupling field studies and quantitative models. There are a paucity of field studies on this ecologically important species, yet there are numerous dogma regarding its ability to cope with its harsh, xeric environment. This motivated the work here, which reevaluates some key tenets associated with the physiology and growth of Larrea. To do this, a field study was conducted in New Mexico at the Jornada Long-Term Ecological Research (LTER) site in the northern Chihuahuan Desert. The field experiment encompassed a diversity of abiotic conditions and measured leaf-level gas exchange and stomatal behavior and evaluated root area and water uptake dynamics. Based on these data (and data from the literature), mathematical and statistical models of the aboveground (stomatal behavior, photosynthesis), belowground (root water uptake, root area dynamics), and whole-plant (growth, allocation, carbon and water balance) processes were developed to render a more integrated understanding of Larrea's primary production. The study uses the results for Larrea and synthesizes information from the literature to develop a conceptual model of how different desert plant functional types respond to precipitation variability. The results indicate that the timing, frequency, and magnitude of rainfall at the annual and especially seasonal and individual storm scales are critical to the short- and long-term physiological and growth dynamics of Larrea and other desert plant functional types. This study provides a comprehensive, mechanistic framework for inferring how arid and semiarid plants and plant communities will potentially respond to climate change, especially with regard to shifting precipitation patterns. |
