Accounting for photodegradation dramatically improves prediction of carbon losses in dryland systems

Traditional models of decomposition fail to capture litter mass loss patterns in dryland systems. This shortcoming has stimulated research into alternative drivers of decomposition, including photodegradation. Here, we use aboveground litter decomposition data for dryland (arid) sites from the Long-term Intersite Decomposition Experiment Team data set to test hypotheses (models) about the mechanisms and impacts of photodegradation. Incorporating photodegradation into a traditional biotic decomposition model substantially improved model predictions for mass loss at these dryland sites, especially after four years. The best model accounted for the effects of solar radiation via photodegradation loss from the intermediate cellulosic and lignin pools and direct inhibition of microbial decomposition. Despite the concurrent impacts of photodegradation and inhibition on mass loss, the best photodegradation model increased mass loss by an average of 12% per year compared to the biotic-only decomposition model. The best model also allowed soil infiltration into litterbags to reduce photodegradation and inhibition of microbial decomposition by shading litter from solar radiation. Our modeling results did not entirely support the popular hypothesis that initial lignin content increases the effects of photodegradation on litter mass loss; surprisingly, higher initial lignin content decreased the rate of cellulosic photodegradation. Importantly, our results suggest that mass loss rates due to photodegradation may be comparable to biotic decomposition rates: Mass loss due to photodegradation alone resulted in litter mass losses of 6–15% per year, while mass loss due to biotic decomposition ranged from 20% per year during early-stage decomposition to 3% per year during late-stage decomposition. Overall, failing to account for the impacts of solar radiation on litter mass loss under-predicted long-term litter mass loss by approximately 26%. Thus, not including photodegradation in dryland decomposition models likely results in large underestimations of carbon loss from dryland systems

Clustering Variable Length Sequences by

We present a novel clustering method using HMM parameter space and eigenvector decomposition. Unlike the existing methods, our algorithm can cluster both constant and variable length sequences without requiring normalization of data. We show that the number of clusters governs the number of eigenvectors used to span the feature similarity space. We are thus able to automatically compute the optimal number of clusters. We successfully show that the proposed method accurately clusters variable length sequences for various scenarios