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Dryad

Data from: Leaf trait variations associated with habitat affinity of tropical karst tree species

Cite this dataset

Geekiyanage, Nalaka et al. (2018). Data from: Leaf trait variations associated with habitat affinity of tropical karst tree species [Dataset]. Dryad. https://doi.org/10.5061/dryad.ft55t

Abstract

1. Karst hills, i.e., jagged topography created by dissolution of limestone and other soluble rocks, are distributed extensively in tropical forest regions, including southern parts of China. They are characterized by a sharp mosaic of water and nutrient availability, from exposed hilltops with poor soil development to valleys with occasional flooding, to which trees show species specific distributions. Here we report the relationship of leaf functional traits to habitat preference of tropical karst trees. 2. We described leaf traits of 19 tropical tree species in a seasonal karst rainforest in Guangxi, China, 12 species in-situ and 13 ex-situ in a non-karst arboretum, which served as a common garden, with six species sampled in both. We examined how the measured leaf traits differed in relation to species’ habitat affinity and evaluated trait consistency between natural habitats vs. the arboretum. 3. Leaf mass per area (LMA) and optical traits (light absorption and reflectance characteristics between 400 and 1050 nm) showed significant associations with each other and habitats, with hilltop species showing high values of LMA and low values of photochemical reflectance index (PRI). 4. For the six species sampled in both the karst forest and the arboretum, LMA, leaf dry matter content, stomatal density and vein length per area showed inconsistent within-species variations, whereas some traits (stomatal pore index and lamina thickness) were similar between the two sites. 5. In conclusion, trees specialized in exposed karst hilltops with little soils are characterized by thick leaves with high tissue density indicative of conservative resources use, and this trait syndrome could potentially be sensed remotely with PRI.

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Location

South China