## 4 Discussion and Conclusions  
The bacterial community in the selected regions of the Bangladesh region of the Bay of Bengal experienced eutrophication and stressors from the biological and chemical pollutants due to anthropogenic pressure. On the other hand, the physical singularity of the water cycle in this region had several benefits [2]. High abundances of Acidobacteria, Bacteroidetes, Actinobacteria, Cyanobacteria, Chlorobi, Firmicutes, Nitrospirae, Chloroflexi, Proteobacteria, Planctomycetes and Verrucomicrobia were found in the water samples in the study area. These bacteria play an important role in maintaining the region; for example, Actinobacteria maintains the quality of the water by moldering organic carbon and remediating pollutants, while Cyanobacteria keeps the water environment safe [5].  
The diversity of the bacterial community in the Bay of Bengal, Bangladesh, was quantified in this study according to bacterial concentration, qPCR and NGS. After extraction, the raw sequences were checked for initial quality and reads in fast QC pipelines. For the quality trimming of reads, Sickle software was used. Assembling of 16S rRNA sequences into operational taxonomic units (OTUs) and removal of singleton OTUs were performed using MICCA OTU [37]. We used a spatial model through a geographically weighted regression approach to fit the bacterial diversity into hexagons. The distribution of bacterial diversity and water temperature was approximated using hexagon tessellation. The relationship between bacterial diversity and water temperature was determined through GWR. Furthermore, GWR was used to predict the diversity of the study area. Calculation of residuals was applied to assess the model's fitness.  
We allocated a number of 13 hexagons over the study area to determine the relationship between water temperature and bacterial diversity. We found that the highest 31% of hexagons consisted of a high water temperature and high diversity. On the other hand, low water temperature and low intensity were seen in 23% of hexagons. The model fitness was assessed by calculating residuals.  
There is currently no spatial model work available utilizing hexagons to determine diversity in the Bay of Bengal, Bangladesh, so a statistical spatial forecast model using a geographical weighted regression approach was applied here and found that this model has potential in forecasting bacterial diversity.  
Conceptualization, N.A. and M.W.; Methodology, N.A., M.W., A.Y., K.S.I. and J.-J.L.; Software, N.A., A.Y and M.W.; Validation, K.S.I. and M.W.; Formal Analysis, N.A. and M.W.; Writing--Original Draft Preparation, N.A. and M.W.; Writing--Review & Editing, N.A., M.W., A.Y., K.S.I. and J.-J.L.; Visualization, N.A., A.Y. and M.W.; Supervision, M.W., K.S.I. and J.-J.L.; Funding Acquisition, M.W. and J.-J.L. All authors have read and agreed to the published version of the manuscript.  
The research received no external funding. [PERSON] has been supported by a Postdoctoral Fellowship from Nanjing University of Information Science and Technology (NUST), China. A Jahangirnagar University scholarship supported [PERSON] and data are available from her upon request. [PERSON] was supported by \"The Startup Foundation for Introducing Talent of NUIST\".  
Authors are grateful to two anonymous reviewers for their constructive review that significantly improved the manuscript.  
The authors declare no conflict of interest.