Fig 1.
Study areas are marked with a red dashed polygon which extends two nautical miles from the shoreline of Pulau Redang and other small islands.
Fig 2.
Bathymetry map of Redang archipelago.
Bathymetry map of the study area. Red and blue dots represent the presence and absence of seagrass occurrence, respectively.
Fig 3.
Backscatter mosaic of Redang archipelago.
Backscatter mosaic of the study areas. Red and blue dots represent the presence and absence of seagrass occurrence, respectively.
Table 1.
Multiple analysis window sizes of bathymetric and backscatter predictors for seafloor morphology and sediment properties used in this study.
Table 2.
Low correlation between predictors for each model, Model1_3, Model1_9, Model1_21, Model50_3, Model50_9, and Model50_21extracted using Pearson Product-moment correlation coefficient.
Table 3.
Training and test AUC values (mean) for both models and measured model performance for the high-resolution (1 m) and low-resolution model (50 m).
Fig 4.
Relative contributions of the bathymetric and backscatter predictors by MaxEnt models.
These predictors were used to build the seagrass habitat suitability models.
Fig 5.
Model predictions on the suitability of the seagrass habitat across the MBES surveyed area.
These models were produced using predictors derived from three analysis window sizes (n = 3, 9, and 21) and gridded at 1 m and 50 m; (A) 1 m (n = 3), (B) 1 m (n = 9), (C) 1 m (n = 21), (D) 50 m (n = 3), (E) 50 m (n = 9), and (F) 50 m (n = 21). The suitability index ranges from 1 (high suitability) to 0 (low suitability).
Fig 6.
Model predictions of the suitability of seagrass habitat zoomed into three sites within the east-south area.
Site A–northern area of Pulau Pinang; Site B–Cina Terjun cape; Site C–Pulau Ekor Tebu. The suitability index ranges from 1 (high suitability) to 0 (low suitability), while for spatial resolution and scale, (A) 1 m (n = 3), (B) 1 m (n = 9), (C) 1 m (n = 21), (D) 50 m (n = 3), (E) 50 m (n = 9), (F) 50 m (n = 21), respectively.