Title of article :
Assessing biodegradation benefits from dispersal networks
Banitz، نويسنده , , Thomas and Fetzer، نويسنده , , Ingo and Johst، نويسنده , , Karin and Wick، نويسنده , , Lukas Y. and Harms، نويسنده , , Hauke and Frank، نويسنده , , Karin، نويسنده ,
The performance of biodegradation of organic pollutants in soil often depends on abiotic conditions and the bioavailability of these pollutants to degrading bacteria. In this context, bacterial dispersal is an essential aspect. Recent studies on the potential promotion of bacterial dispersal by fungal hyphae raised the idea of specifically applying fungal networks to accelerate bacterial degradation processes in situ. Our objective is to investigate these processes and their performance via simulation modelling and address the following questions: (1) Under what abiotic conditions can dispersal networks significantly improve bacterial degradation? and (2) To what extent does the spatial configuration of the networks influence the degradation performance? To answer these questions, we developed a spatially explicit bacterial colony model, which is applied to controlled laboratory experiments with Pseudomonas putida G7 organisms as a case study. Using this model, we analyzed degradation performance in response to different environmental scenarios and showed that conditions of limited bacterial dispersal also limit degradation performance. Under such conditions, dispersal networks have the highest potential for improving the bioavailability of pollutants to bacteria. We also found that degradation performance significantly varies with the spatial configuration of the dispersal networks applied and the time horizon over which performance is assessed. Regarding future practical applications, our results suggest that (1) fungal networks may dramatically improve initially adverse conditions for biodegradation of pollutants in soil, and (2) the networkʹs spatial structure and accessibility are decisive for the success of such tasks.
Bioavailability , Organic pollutants , Bioremediation , Bacterial model , Fungal networks
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