PS 47-31
Linkages between leaf litter traits, leaf decomposition, and soil methanogenesis in a forested wetland soil

Thursday, August 14, 2014
Exhibit Hall, Sacramento Convention Center
Joseph B. Yavitt, Natural Resources, Cornell University, Ithaca, NY
Alexis K. Heinz, Natural Resources, Cornell University, Ithaca, NY
Elizabeth M. Corteselli, Natural Resources, Cornell University, Ithaca, NY
Background/Question/Methods

Our aim was to examine linkages between leaf litter and microbial methane production in soil from a forested wetland. If plant species have unique influence on ecosystem function, then using traits of leaf litter aboveground has tremendous potential to predict rates of microbial activity belowground. Our basic hypothesis was that slowly decomposing leaves would fuel long-lasting rates of methane production. We used leaf litter from 15 tree species (angiosperm and gymnosperm, deciduous and evergreen) and soil from a forested wetland in New York State. We followed the rate of mass loss in the field for five years and correlated variation in rates with leaf litter traits, including specific leaf area and biochemical composition of plant cells and cell walls. We quantified microbial activity and methane production in soil amended with litter of increasing state of decay.

Results/Conclusions

The mass loss rate was (fastest to slowest): deciduous angiosperm, evergreen gymnosperm, and deciduous gymnosperm. We found positive relationships between rates of mass loss and hemicellulose content in the early stage versus lignin content in the later stage. Rates of methane production showed a positive relationship with pectin content in the early stage and with lignin in the later stage. Overall, soil plus litter exhibited methanogenic conditions, i.e., equal amounts of methane production and carbon dioxide production. Our interpretation is that fermentation of pectin and lignin produces methanol, which is a methanogenic substrate. Leaf traits and, in particular, leaf carbon fractions show promise as a means to understand ecosystem function and predict rates of soil microbial activity.