Eco-evolutionary optimization in soil organic matter models

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Abstract
Turnover of soil organic matter (SOM) by microbes is an important step in the soil carbon cycle. As microbes are living organisms that interact with their environment and one another, microbial communities are not static but can adapt to various conditions through changes in functional traits. Such adaptation of microbial functional traits can affect the fate of soil organic carbon. However, current microbial-explicit models commonly do not represent such eco-evolutionary dynamics, but treat microbes more akin to inanimate engines or chemical compartments. Eco-evolutionary optimization (EEO) approaches aim to abstract from the complexity of different ecological and evolutionary adaptation mechanisms by assuming that for given conditions, the microbial community might be dominated by those organisms with functional traits that would maximize fitness under these conditions. Different fitness proxies have been used in the literature – but a general framework for EEO approaches in SOM modeling is missing. Based on a review of previous studies, we suggest a classification of EEO approaches in SOM models based on the definition of microbial fitness and the time scale of optimization. Results from different EEO approaches differ systematically along the axes of our classification framework – however, they can also yield convergent qualitative patterns that match experimental observations. Taken together, our results show that EEO approaches have great potential for advancing SOM modeling. Yet, challenges remain – calling especially for further comparative studies and empirical validation of different approaches.