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The article discusses the importance of time in accurately defining and quantifying carbon sequestration in soils for climate change mitigation. The authors argue that the definition proposed by Don et al. is incomplete because it does not explicitly consider the time that carbon remains stored in an ecosystem. They emphasize that a comprehensive definition should include both the amount of carbon sequestered and the duration of its storage. The authors suggest that considering time in the definition of carbon sequestration is crucial for effective climate change actions. [Extracted from the article]

Plant litter decomposition stands at the intersection between carbon (C) loss and sequestration in terrestrial ecosystems. During this process organic matter experiences chemical and physical transformations that affect decomposition rates of distinct components with different transformation fates. However, most decomposition studies only fit one-pool models that consider organic matter in litter as a single homogenous pool and do not incorporate the dynamics of litter transformations and transfers into their framework. As an alternative, compartmental dynamical systems are sets of differential equations that serve to represent both the heterogeneity in decomposition rates of organic matter and the transformations it can undergo. This is achieved by including parameters for the initial proportion of mass in each compartment, their respective decomposition rates, and mass transfer coefficients between compartments. The number of compartments as well as their interactions, in turn, determine the model structure. For instance, a one-pool model can be considered a compartmental model with only one compartment. Models with two or more parameters, in turn, can have different structures, such as a parallel one if each compartment decomposes independently or in a series if there is mass transfer from one compartment to another. However because of these differences in model parameters, comparisons in model performance can be complicated. In this context we introduce the concept of transit time, a random variable defined as the age distribution of particles when they are released from a system, which can be used to compare models with different structures. In this study, we first asked what model structures are more appropriate to represent decomposition from a publicly available database of decomposition studies in arid lands: aridec. For this purpose, we fit one- and two-pool decomposition models with parallel and series structures, compared their performance using the bias-corrected Akaike information criterion (AICc) and used model averaging as a multi-model inference approach. We then asked what the potential ranges of the median transit times of litter mass in arid lands are and what their relationships with environmental variables are. Hence, we calculated a median transit time for those models and explored patterns in the data with respect to mean annual temperature and precipitation, solar radiation, and the global aridity index. The median transit time was 1.9 years for the one- and two-pool models with a parallel structure and 5 years for the two-pool series model. The information in our datasets supported all three models in a relatively similar way and thus our decision to use a multi-model inference approach. After model averaging, the median transit time had values of around 3 years for all datasets. Exploring patterns of transit time in relation to environmental variables yielded weak correlation coefficients, except for mean annual temperature, which was moderate and negative. Overall, our analysis suggests that current and historical litter decomposition studies often do not contain information on how litter quality changes over time or do not last long enough for litter to entirely decompose. This makes fitting accurate mechanistic models very difficult. Nevertheless, the multi-model inference framework proposed here can help to reconcile theoretical expectations with the information content from field studies and can further help to design field experiments that better represent the complexity of the litter decomposition process. [ABSTRACT FROM AUTHOR]

Plant litter decomposition stands at the intersection between carbon (C) loss and sequestration in terrestrial ecosystems. Organic matter during this process experiences chemical and physical transformations that affect decomposition rates of distinct components with different transformation fates. However, most decomposition studies only fit one-pool models that consider organic matter in litter as a single homogenous pool and do not incorporate the dynamics of litter transformations and transfers in their framework. To this extent, compartmental dynamical systems are sets of differential equations that can be used to represent both the heterogeneity in decomposition rates of organic matter, and the transformations it can undergo. Further, a metric that can be used to compare models with different structures is the transit time, that is, the mean age of particles when they are released from a compartmental system. In this study, we first asked what model structures are more appropriate to represent decomposition from a publicly available database of decomposition studies in aridlands: aridec. For this purpose, we fit one- and two-pool decomposition models with parallel and series structures and used model averaging as a multi-model inference approach. We then asked what the potential ranges of the median transit times of C in aridlands are and what are their relationships with environmental and chemical variables. Hence, we calculated transit time for those models and explored patterns in the data with respect to mean annual temperature and precipitation, solar radiation, the Global Aridity Index, and one litter chemistry trait, the initial lignin content. Median transit time was 1.9 years for the one- and two-pool model with parallel structure, and five years for the two-pool series model. The information in our datasets supported all three models in a relatively similar way, thus our decision to use a multi-model inference approach. After model-averaging, median transit time had values of around three years for all datasets. Exploring patterns of transit time in relation to environmental variables yielded weak correlation coefficients, except for mean annual temperature, which was moderate and negative. Overall, our analysis suggests that the information content in litter decomposition studies often holds little information on the heterogeneity of litter and its transformation rate. Nevertheless, the multi-model inference framework proposed here can help to reconcile theoretical expectations with the information content from field studies and can further help to design field experiments that better represent the complexity of the litter decomposition process. [ABSTRACT FROM AUTHOR]