Description
Correlating plant litter decay rates with initial tissue traits (e.g. C, N contents) is
common practice, but in woody litter, predictive relationships are often weak. Variability
in predicting wood decomposition is partially due to territorial competition among fungal
decomposers that, in turn, have a range of nutritional strategies (rot types) and
consequences on residues. Given this biotic influence, researchers are increasingly
using culture-independent tools in an attempt to link variability more directly to
decomposer groups. Our goal was to complement these tools by using certain wood
modifications as 'signatures' that provide more functional information about
decomposer dominance than density loss. Specifically, we used dilute alkali solubility
(DAS; higher for brown rot) and lignin:density loss (L:D; higher for white rot) to infer rot
type (binary) and fungal nutritional mode (gradient), respectively. We first determined
strength of pattern among 29 fungi of known rot type by correlating DAS and L:D with
mass loss in birch and pine. Having shown robust relationships for both techniques
above a density loss threshold, we then demonstrated and resolved two issues
relevant to species consortia and field trials, 1) spatial patchiness creating gravimetric
bias (density bias), and 2) brown rot imprints prior or subsequent to white rot
replacement (legacy effects). Finally, we field-tested our methods in a New Zealand
Pinus radiata plantation in a paired-plot comparison. Overall, results validate these lowcost
techniques that measure the collective histories of decomposer dominance in
wood. The L:D measure also showed clear potential in classifying 'rot type' along a
spectrum rather than as a traditional binary type (brown versus white rot), as it places
the nutritional strategies of wood-degrading fungi on a scale (L:D=0-5, in this case).
These information-rich measures of consequence can provide insight into their
biological causes, strengthening the links between traits, structure, and function during
wood decomposition.
common practice, but in woody litter, predictive relationships are often weak. Variability
in predicting wood decomposition is partially due to territorial competition among fungal
decomposers that, in turn, have a range of nutritional strategies (rot types) and
consequences on residues. Given this biotic influence, researchers are increasingly
using culture-independent tools in an attempt to link variability more directly to
decomposer groups. Our goal was to complement these tools by using certain wood
modifications as 'signatures' that provide more functional information about
decomposer dominance than density loss. Specifically, we used dilute alkali solubility
(DAS; higher for brown rot) and lignin:density loss (L:D; higher for white rot) to infer rot
type (binary) and fungal nutritional mode (gradient), respectively. We first determined
strength of pattern among 29 fungi of known rot type by correlating DAS and L:D with
mass loss in birch and pine. Having shown robust relationships for both techniques
above a density loss threshold, we then demonstrated and resolved two issues
relevant to species consortia and field trials, 1) spatial patchiness creating gravimetric
bias (density bias), and 2) brown rot imprints prior or subsequent to white rot
replacement (legacy effects). Finally, we field-tested our methods in a New Zealand
Pinus radiata plantation in a paired-plot comparison. Overall, results validate these lowcost
techniques that measure the collective histories of decomposer dominance in
wood. The L:D measure also showed clear potential in classifying 'rot type' along a
spectrum rather than as a traditional binary type (brown versus white rot), as it places
the nutritional strategies of wood-degrading fungi on a scale (L:D=0-5, in this case).
These information-rich measures of consequence can provide insight into their
biological causes, strengthening the links between traits, structure, and function during
wood decomposition.
| Date made available | 2015 |
|---|---|
| Publisher | Data Repository for the University of Minnesota |