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ABSTRACT:

Seasonal differences in leaf-level physiology give lianas a competitive advantage over trees in a tropical seasonal forest

Journal Article

Cai Z-Q; Schnitzer S; Bongers F

2009

Oecologia

161

25-33

Lianas are an important component of most tropical forests where they vary in abundance from high in seasonal forests to low in aseasonal forests. We tested the hypothesis that the physiological ability of lianas to fix carbon (and thus grow) during seasonal drought may confer a distinct advantage in seasonal tropical forests which may explain pan-tropical liana distributions. We compared a range of leaf-level physiological attributes of 18 co-occurring liana and 16 tree species during the wet and dry seasons in a tropical seasonal forest in Xishuangbanna China. We found that during the wet season lianas had significantly higher CO2 assimilation per unit mass (A mass) nitrogen concentration (N mass) and δ13C values and lower leaf mass per unit area (LMA) than trees indicating that lianas have higher assimilation rates per unit leaf mass and higher integrated water-use efficiency (WUE) but lower leaf structural investments. Seasonal variation in CO2 assimilation per unit area (A area) phosphorus concentration per unit mass (P mass) and photosynthetic N-use efficiency (PNUE) however was significantly lower in lianas than in trees. For instance mean tree A area decreased by 30.1% from wet to dry season compared with only 12.8% for lianas. In contrast from the wet to dry season mean liana δ13C increased four times more than tree δ13C with no reduction in PNUE whereas trees had a significant reduction in PNUE. Lianas had higher A mass than trees throughout the year regardless of season. Collectively our findings indicate that lianas fix more carbon and use water and nitrogen more efficiently than trees particularly during seasonal drought which may confer a competitive advantage to lianas during the dry season and thus may explain their high relative abundance in seasonal tropical forests.

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The Liana Ecology Project is supported by Marquette University and funded in part by the National Science Foundation.

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