Abstract
The Mangrove ecosystem is continuously losing its dignity. A few studies have focused on understanding the changing behavior of Sundarban Mangrove Forest. However, knowledge-based database interpretation and employable pattern extraction may be an efficient approach to stand against the degrading nature of the mangrove ecosystem. Comprehending the gravity of the present scenario, the main contribution of this paper lies in the task of information retrieval by assessing the natural growth of native mangrove species of Sundarban. We have followed a methodology that makes use of association rule mining and biclustering approaches in order to come up with an off-the-shelf mechanism to analyze the data. This explores rules showing the effect of soil pH, water salinity on mangrove community structure, and on individual mangrove species and finds relation to biodiversity indices. The rules can predict probable sites for mangrove species expansion by computing the probability of introducing a new species to a particular site. Our study also generates the frequently co-occurred species lists along with the supporting sites. It could help in mangrove ecosystem restoration by identifying the most probable species that is missing from a particular site, maybe due to the gradual historical disappearance. Hence, this analytical study would enhance the possibilities of restoration of the mangrove ecosystem under survey in a systematic and empirical way.







Similar content being viewed by others
References
Adams EM. Using migration monitoring data to assess bird population status and behavior in a changing environment. Ph.D. thesis, The University of Maine. 2014.
Agrawal R, Srikant R. Fast algorithms for mining association rules. In: Proc. 20th int. conf. very large data bases, VLDB, vol. 1215. 1994. p. 487–99.
Andrienko N, Andrienko G. Exploratory analysis of spatial and temporal data: a systematic approach. Springer Science & Business Media. pp. XV-703. ISBN: 13 978-3-540-35994-7 2006.
Barik J, Mukhopadhyay A, Ghosh T, Mukhopadhyay SK, Chowdhury SM, Hazra S. Mangrove species distribution and water salinity: an indicator species approach to Sundarban. J Coast Conserv. 2018;22(2):361–8.
Bowman HHM. Ecology and physiology of the red mangrove. Proc Am Philos Soc. 1917;56(7):589–672.
Bronick CJ, Lal R. Soil structure and management: a review. Geoderma. 2005;124(1–2):3–22.
Clarke LD, Hannon NJ. The mangrove swamp and salt marsh communities of the Sydney district: III. Plant growth in relation to salinity and waterlogging. J Ecol. 1970;58(2):351–69.
Dasgupta S, Sobhan I, Wheeler D. The impact of climate change and aquatic salinization on mangrove species in the Bangladesh Sundarbans. Ambio. 2017;46(6):680–94.
Ghosh A, Schmidt S, Fickert T, Nüsser M. The Indian Sundarban mangrove forests: history, utilization, conservation strategies and local perception. Diversity. 2015;7(2):149–69.
Ghosh M, Mondal KC. Computational biodiversity. In: Proceedings of CSI annual convention. Springer; 2020. p. 1–6 (in press).
Ghosh M, Roy A, Mondal KC. FCA based constant and coherent signed bicluster identification and its application in biodiversity study. In: Proceedings of CSI annual convention. Springer; 2020. p. 1–6 (in press).
Ghosh M, Roy A, Mondal KC. Determining dark diversity of different faunal groups in Indian estuarine ecosystem: a new approach with computational biodiversity. In: Proceedings of international conference on emerging applications of information technology (EAIT-2020). Springer; 2020. pp. 1–10 (in press).
Ghosh M, Roy A, Mondal KC. Analysis of Indian estuarine data of flora & fauna. In: Proceedings of 2nd international conference on data science and applications (ICDSA 2021). Springer; 2021. pp. 1–12 (in press).
Ghosh M, Sil P, Roy A, Fajriyah R, Mondal KC. Finding prediction of interaction between SARS-CoV-2 and human protein: a data-driven approach. J Inst Eng (India) Ser B 2021;4:1–10.
Gougeon FA, et al. Automatic individual tree crown delineation using a valley-following algorithm and rule-based system. In: Proc. international forum on automated interpretation of high spatial resolution digital imagery for forestry, Victoria. Canadian Forest Service; 1998. p. 11–23.
Halder S, Samanta K, Das S, Pathak D. Monitoring the inter-decade spatial-temporal dynamics of the Sundarban mangrove forest of India from 1990 to 2019. Reg Stud Mar Sci. 2021;44:101718.
Hill MO. Diversity and evenness: a unifying notation and its consequences. Ecology. 1973;54(2):427–32.
Hossain M, Nuruddin A. Soil and mangrove: a review. J Environ Sci Technol. 2016;9(2):198.
Hussain MA, Islam A, Hasan MA, Bhaskaran B. Changes of the seasonal salinity distribution at the Sundarbans coast due to impact of climate change. In: 4th international conference on water & flood management (ICWFM). 2013. p. 637–48.
Hutchings P, Saenger P, et al. Ecology of mangroves. University of Queensland Press, 1987.
Kamruzzaman M, Ahmed S, Osawa A. Biomass and net primary productivity of mangrove communities along the Oligohaline zone of Sundarbans, Bangladesh. For Ecosyst. 2017;4(1):16.
Madeira SC, Oliveira AL. Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Trans Comput Biol Bioinform (TCBB). 2004;1(1):24–45.
Mondal B, Saha AK, Roy A. Mapping mangroves using LISS-IV and Hyperion data in part of the Indian Sundarban. Int J Remote Sens. 2019;40(24):9380–400.
Mondal I, Thakur S, Ghosh P, De TK. Assessing the impacts of global sea level rise (SLR) on the mangrove forests of Indian Sundarbans using geospatial technology. In: Geographic information science for land resource management. 2021. p. 209–27.
Mondal KC. Algorithms for data mining and bio-informatics. Ph.D. thesis, University of Nice Sophia Antipolis. 2013.
Mondal KC, Pasquier N, Mukhopadhyay A, Maulik U, Bandhopadyay S. A new approach for association rule mining and bi-clustering using formal concept analysis. In: International workshop on machine learning and data mining in pattern recognition. Springer; 2012. p. 86–101.
Mondal KC, Pasquier N. Galois closure based association rule mining from biological data. In: Biological knowledge discovery handbook. 2013. p. 761–802.
Naidu MT, Kumar OA. Tree diversity, stand structure, and community composition of tropical forests in Eastern Ghats of Andhra Pradesh, India. J Asia Pac Biodivers. 2016;9(3):328–34.
Payo A, Mukhopadhyay A, Hazra S, Ghosh T, Ghosh S, Brown S, Nicholls RJ, Bricheno L, Wolf J, Kay S, et al. Projected changes in area of the Sundarban mangrove forest in Bangladesh due to SLR by 2100. Clim Change. 2016;139(2):279–91.
Rashid SH, Böcker R, Hossain A, Khan SA. Undergrowth species diversity of Sundarban mangrove forest Bangladesh in relation to salinity, vol. 17. Hohenheim: Ber. Inst. Landschafts-Pflanzenökologie Univ.; 2008. p. 41–56.
Samanta K, Hazra S. Mangrove forest cover changes in Indian Sundarban (1986–2012) using remote sensing and GIS. In: Environment and earth observation. Springer; 2017. p. 97–108.
Sievers M, Chowdhury MR, Adame MF, Bhadury P, Bhargava R, Buelow C, Friess DA, Ghosh A, Hayes MA, McClure EC, et al. Indian Sundarbans mangrove forest considered endangered under Red List of Ecosystems, but there is cause for optimism. Biol Cons. 2020;251:108751.
Silva LAE, Siqueira MF, dos Santos Pinto F, Barros FSM, Zimbrão G, Souza JM. Applying data mining techniques for spatial distribution analysis of plant species co-occurrences. Expert Syst Appl. 2016;43:250–260.
Silva MA, Trevisan DQ, Prata DN, Marques EE, Lisboa M, Prata M. Exploring an ichthyoplankton database from a freshwater reservoir in legal amazon. In: International conference on advanced data mining and applications. Springer; 2013. p. 384–95.
Simpson EH. Measurement of diversity. Nature. 1949;163(4148):688.
Singh S, Malik ZA, Sharma CM. Tree species richness, diversity, and regeneration status in different oak (Quercus spp.) dominated forests of Garhwal Himalaya, India. J Asia Pac Biodivers. 2016;9(3):293–300.
Sreelekshmi S, Nandan SB, Kaimal SV, Radhakrishnan C, Suresh V. Mangrove species diversity, stand structure and zonation pattern in relation to environmental factors—a case study at Sundarban delta, east coast of India. Reg Stud Mar Sci. 2020;35:101111.
Wakushima S, Kuraishi S, Sakurai N, Supappibul K, Siripatanadllok S. Stable soil pH of Thai mangroves in dry and rainy seasons and its relation to zonal distribution of mangroves. J Plant Res. 1994;107(1):47–52.
Zhang C, Zhang S. Association rule mining: models and algorithms. Berlin: Springer; 2002.
Acknowledgements
The authors are grateful to the Department of Science & Technology, Government of India, New Delhi, for financial assistance under the scheme of WOS-A (Women Scientist Scheme A) to carry out this Ph.D. research project.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the topical collection “Next-Generation Digital Transformation through Intelligent Computing” guest edited by PN Suganthan, Paramartha Dutta, Jyotsna Kumar Mandal and Somnath Mukhopadhyay”.
Rights and permissions
About this article
Cite this article
Ghosh, M., Roy, A. & Mondal, K.C. Knowledge Discovery of Sundarban Mangrove Species: A Way Forward for Managing Species Biodiversity. SN COMPUT. SCI. 3, 18 (2022). https://doi.org/10.1007/s42979-021-00869-1
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s42979-021-00869-1