LAUSR

Abstract Mangrove ecosystems are significant sources of greenhouse gases (GHG) that is attributed to microbial activity. However, it is still unknown how the sediment microbial populations affect GHG emissions in mangrove ecosystem. Since little is known about microbial populations of mangroves, the present study was aimed to understand the structure and function of microbial communities in the Indian part of the Sundarban mangrove ecosystem in relation to environmental variables and variation of GHG emissions during three seasons: pre-monsoon (March–June), monsoon (July–October) and post-monsoon (November–February). Seasonal variations of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) gas samples were taken from the mangrove bed. Culture methods were used to detect twelve different types of microbes such as heterotrophic (Htp), N2 fixing (Nfix), nitrifying (Ntfn), sulfur oxidizing (Soxd), Gram-negative (GMn), Gram-positive (GMp), spore forming (Sfor), denitrifying (DNtfn), anaerobic (Anrb), phosphate solubilizing (Psol), cellulose degrading (Cdeg) bacteria and actinomycetes (Actm). In the monsoon, populations of the Htp, Anrb, Psol, and Cdeg bacteria were more prevalent, whereas populations of the GMn, GMp, Ntfn, DNtfn bacteria, and Actm bacteria were more prevalent in the post-monsoon. Monsoonal CO2 and CH4 fluxes were larger than pre-monsoon and post-monsoon, resulting in increased microbial soil respiration and breakdown of soil organic carbon. Because of higher denitrification and soil temperature, N2O flux was higher in the pre-monsoon period, followed by monsoon and post-monsoon periods. A univariate statistical correlation was employed to assess the relationships between environmental variables and different microbial populations. An ANN (artificial neural network) model was proposed to evaluate the relevance of microbial population contribution to GHG emissions, and it indicated that the Htp, Anrb, and Dntfn microbial populations were most relevant for CO2, CH4, and N2O emissions. The suggested model would be used to assess the drivers behind GHG emission in the mangroves located at different parts of the world.