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- Khurana, Swamini, 1989-, et al.
(author)
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Interactive effects of microbial functional diversity and carbon availability on decomposition - A theoretical exploration
- 2023
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In: Ecological Modelling. - 0304-3800 .- 1872-7026. ; 486
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Journal article (peer-reviewed)abstract
- Microbial functional diversity in litter and soil has been hypothesized to affect the rate of decomposition of organic matter and other soil ecosystem functions. However, there are no clear theoretical expectations on how these effects might change with substrate availability, heterogeneity in the substrate chemistry, and different aspects of functional diversity itself (number of microbial groups vs. distribution of functional traits). To explore how these factors shape the decomposition-diversity relation, we carry out numerical experiments using a flexible reaction network comprising microbial processes and interactions with bioavailable carbon (extracellular degradation, uptake, respiration, growth, and mortality), and ecological processes (competition among the different groups). We also considered diverse carbon substrates, in terms of varying nominal oxidation state of carbon (NOSC). The reaction network was used to test the effects of (i) number of microbial groups, (ii) number of carbon pools, (iii) microbial functional diversity, and (iv) amount of bioavailable carbon. We found that the decomposition rate constant increases with increasing substrate concentration and heterogeneity, as well as with increasing microbial functional diversity or variance of microbial traits, albeit these biological factors are less important. The multivariate dependence of the decomposition rate constant (and other decomposition and mi-crobial growth metrics) on substrate and microbial factors can be described using power laws with exponents lower than one, indicating that diversity effects on decomposition and microbial growth are reduced at high substrate concentration and heterogeneity, or at high microbial diversity.
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2. |
- Le Noë, Julia, et al.
(author)
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Soil organic carbon models need independent time-series validation for reliable prediction
- 2023
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In: Communications Earth & Environment. - 2662-4435. ; 4:1
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Journal article (peer-reviewed)abstract
- Numerical models are crucial to understand and/or predict past and future soil organic carbon dynamics. For those models aiming at prediction, validation is a critical step to gain confidence in projections. With a comprehensive review of ~250 models, we assess how models are validated depending on their objectives and features, discuss how validation of predictive models can be improved. We find a critical lack of independent validation using observed time series. Conducting such validations should be a priority to improve the model reliability. Approximately 60% of the models we analysed are not designed for predictions, but rather for conceptual understanding of soil processes. These models provide important insights by identifying key processes and alternative formalisms that can be relevant for predictive models. We argue that combining independent validation based on observed time series and improved information flow between predictive and conceptual models will increase reliability in predictions.
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