NANO Alumni Subrata Sarker and colleagues published the following article in the Journal of Algal Research
Spatial prediction of seaweed habitat for mariculture in the coastal area of Bangladesh using a Generalized Additive Model
Sarker, S. et al. (2021) Journal of Algal Research. DOI 10.1016/j.algal.2021.102490
Seaweeds are marine autotrophic macroalgae found in the coastal waters, and have both ecological and economic values. Present study aimed to understand the drivers of commercially important seaweed species distribution and predict their suitable habitat for cultivation in the coastal area of Bangladesh. We used Generalized Additive Model to predict the potential habitats of seaweeds utilizing data on seaweed occurrence (presence and absence data), environmental conditions and bathymetry. Required data were compiled from in-situ measurements, satellite observations and model simulations. Our model explained 78% variability in seaweed habitat distribution. We found that total suspended matter was the main predictor of seaweed habitat distribution followed by salinity, nitrate concentrations, depth, zonal and meridional components of current and sea surface temperature. Our model predicted that about 11,200 km2 areas had 20–100% occurrence probability for seaweed. We also found that about 4100km2 area had higher probability of seaweed occurrence (50–100%). Our model predictions had a good agreement with in-situ observations (Area under curve = 0.83, R2 = 0.81). Since Bangladesh is focusing on the expansion of ocean based economic activities, our study will serve as a tool to start commercial mariculture of seaweed by providing information on site suitability for farm establishment.
- Northern Bay of Bengal
Link for the publication here