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Am J Bot. 2001 Sep;88(9):1568-76.

Functional significance of variation in bryophyte canopy structure.

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  • 1Department of Biological Sciences, Union College, Schenectady, New York 12308 USA.


In most bryophytes, the thickness of boundary layers (i.e., unstirred layers) that surrounds plant surfaces governs rates of water loss. Architectural features of canopies that influence boundary layer thickness affect the water balance of bryophytes. Using field samples (9.3 cm diameter cushions) from 12 populations (11 species) of mosses and liverworts, we evaluated the relationship between canopy structure and boundary layer properties. Canopy structure was characterized using a contact surface probe to measure canopy depth along perpendicular transects at spatial scales ranging from 0.8 to 30 mm on 186 points per sample. Semivariance in depth measurements at different spatial scales was used to estimate three architectural properties: surface roughness (L(r)), the scale of roughness elements (S(r)), and fine-scale surface texture, the latter characterized by the fractal dimension (D) of the canopy profile. Boundary layer properties were assessed by evaporation of ethanol from samples in a wind-tunnel at wind speeds from 0.6 to 4.2 m/s and applied to characterize mass transfer using principles of dynamic similarity (i.e., using dimensionless representations of conductance and flow). In addition, particle image velocimetry (PIV) was used to visualize and quantify flow over two species. All cushions exhibited the characteristics of turbulent as opposed to laminar boundary layers, and conductance increased with surface roughness. Bryophyte canopies with higher L(r) had greater conductances at all wind speeds. Particle image velocimetry analysis verified that roughness elements interacted with flow and caused turbulent eddies to enter canopies, enhancing evaporation. All three morphological features were significantly associated with evaporation. When L(r), S(r), and D were incorporated with a flow parameter into a conductance model using multiple linear regression, the model accounted for 91% of the variation in mass transfer.

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