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Response of an obligate CAM plant to competition and increased watering intervals
Jingjing Fan1, Zhengyu Wang1, Chengyi Tu2
1College of Geography and Environment, Shandong Normal University, Jinan, China.
Abstract:
Climate change has exacerbated precipitation variability, profoundly impacting vegetation dynamics and community structures in arid ecosystems. There remains a notable knowledge gap regarding the ecological effects of altered precipitation on crassulacean acid metabolism (CAM) plants and their interactions with other photosynthetic types. This study investigated the response of the typical obligate CAM plant Orostachys fimbriata to extended watering intervals (WI4-WI8) and various competitive patterns (M1-M4) with the C3 grass Melilotus officinalis and the C4 grass Setaria viridis through greenhouse experiments. The results showed that: (1) In species mixtures, CAM plants had slightly reduced the total biomass (TB) compared to monocultures, yet maintained competitiveness by increasing the root-to-shoot biomass (R:S) ratio, stabilizing plant height, and sustaining their photosynthetic rates. (2) As watering intervals increased, CAM plants adapted by further elevating the R:S ratio, reducing height, and decreasing aboveground biomass. However, their height, CO2 assimilation rate, and above- and below-ground biomass were significantly suppressed, particularly when coexisting with C4 plants. More extreme watering regime caused a 47.6% decrease in TB of CAM plants in M4, while C3 and C4 grasses declined by 53.2% and 37.8%, respectively. (3) Given the predicted extension of drought intervals and the intensification of individual rainfall events under future climate conditions, the competitive pressure from C4 plants with high drought tolerance and resource acquisition advantages may limit the expansion potential of CAM plants in drylands. This study enhances the understanding of adaptive mechanisms of CAM plants competing and coexisting with grasses under variable environments, providing scientific bases for predicting arid ecosystem dynamics.
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