Effects of elevated atmospheric carbon dioxide on biomass and carbon accumulation in a model regenerating longleaf pine community
Plant species vary in response to atmospheric CO2 concentration due to differences in physiology, morphology, phenology, and symbiotic relationships. These differences make it very difficult to predict how plant communities will respond to elevated CO2. Such information is critical to furthering our understanding of community and ecosystem responses to global climate change. To determine how a simple plant community might respond to elevated CO2, a model regenerating longleaf pine community composed of five species was exposed to two CO2 regimes (ambient, 365 mu mol mol(-1) and elevated, 720 mu mol mol(-1)) for 3 yr. Total above- and belowground biomass was 70 and 49% greater, respectively, in CO2-enriched plots. Carbon (C) content followed a response pattern similar to biomass, resulting in a significant increase of 13.8 Mg C ha(-1) under elevated CO2. Responses of individual species, however, varied. Longleaf pine (Pinus palustris Mill.) was primarily responsible for the positive response to CO2 enrichment. Wiregrass (Aristida stricta Michx.), rattlebox (Crotalaria rotundifolia Walt. Ex Gruel.), and butterfly weed (Asclepias tuberosa L.) exhibited negative above- and belowground biomass responses to elevated CO2, while sand post oak (Quercus margaretta Ashe) did not differ significantly between CO2 treatments. As with pine, C content followed patterns similar to biomass. Elevated CO2 resulted in alterations in community structure. Longleaf pine comprised 88% of total biomass in CO2-enriched plots, but only 76% in ambient plots. In contrast, wire-grass, rattlebox, and butterfly weed comprised 19% in ambient CO2 plots, but only 8% under high CO2. Therefore, while longleaf pine may perform well in a high CO2 world, other members of this community may not compete as well, which could alter community function. Effects of elevated CO2 on plant communities are complex, dynamic, and difficult to predict, clearly demonstrating the need for more research in this important area of global change science.