Decomposition-rate estimation of leaf litter in karst forests in China based on a mathematical model

Aims

The objectives of the study were to analyze the relationship between decomposition rates and initial chemistry of leaf litter and to establish an optimal model to predict the decomposition rates of a large number of plant species in karst forests of China.

Methods

We determined the decomposition rate of leaf litter from 21 representative species in karst forests through a litterbag experiment. Using Akaike information criteria, we selected an optimal model among 925 regression models of decomposition rate based on initial chemistry indexes to estimate annual leaf-litter-decomposition rate for an additional 96 important species.

Results

Of the 21 representative species, Elaeocarpus decipiens and Phoebe sheareri exhibited the highest (62.85 %) and lowest (23.50 %) annual decomposition rates, respectively. In the first and second quarters, climatic conditions were not advantageous to decomposition, but 20 species reached their highest decomposition rate. Most of 117 tested species accumulated fewer nutrients and more non-easily-decomposed materials in their leaf litter than plant species in non-karst forests. The selected optimal model was: $ mathrm{annual} mathrm{decomposition} mathrm{rate}=111.838-0.114;left( {mathrm{total} mathrm{carbon}} right)+0.021;left( {mathrm{total} mathrm{nitrogen}} right)+0.068;left( {mathrm{total} mathrm{potassium}} right)-0.027;left( {mathrm{lignin}} right)-0.398;left( {mathrm{tannin}} right)-0.015;left( {mathrm{starch}} right) $ . Predicted annual leaf-litter-decomposition rates of the additional 96 tree species were 20–80 %.

Conclusions

This study enhances our understanding of leaf-litter decomposition for plant species in karst forests and provides a method for estimating annual leaf-litter-decomposition rates.