Inverse method for estimating respiration rates from decay time series

[摘要] Long-term organic matter decomposition experiments typically measure the mass lost from decaying organic matter as a functionof time.These experiments can provide information about the dynamics of carbon dioxide input to the atmosphere and controlson natural respiration processes. Decay slows down with time, suggesting that organic matter is composed of components (pools)with varied lability.Yet it is unclear how the appropriate rates, sizes, and number of pools vary with organic matter type,climate, and ecosystem.To better understand these relations, it is necessary to properly extract the decay rates fromdecomposition data.Here we present a regularized inverse method to identify an optimally-fitting distribution of decay ratesassociated with a decay time series.We motivate our study by first evaluating a standard, direct inversion of the data.Thedirect inversion identifies a discrete distribution of decay rates, where mass is concentrated in just a small number ofdiscrete pools.It is consistent with identifying the best fitting "multi-pool" model, without prior assumption of thenumber of pools.However we find these multi-pool solutions are not robust to noise and are over-parametrized.We thereforeintroduce a method of regularized inversion, which identifies the solution which best fits the data but not the noise.Thismethod shows that the data are described by a continuous distribution of rates, which we find is well approximated bya lognormal distribution, and consistent with the idea that decomposition results from a continuum of processes at differentrates.The ubiquity of the lognormal distribution suggest that decay may be simply described by just two parameters: a meanand a variance of log rates.We conclude by describing a procedure that estimates these two lognormal parameters from decaydata.Matlab codes for all numerical methods and procedures are provided.

[学科分类] 地球化学与岩石