Predict and Map Oyster Growth Suitability from Environmental Data // Predicts spatial suitability for oyster growth from environmental survey data using Analytic Hierarchy Process (AHP) weighted scoring. Users supply sensor data from Acoustic Doppler Current Profilers (ADCP), Conductivity-Temperature- Depth (CTD) sensors, bathymetric sonar, and sidescan sonar, specify a target species, and receive per-location suitability scores, a 'GeoTIFF' heatmap for 'QGIS', contour lines, and a formatted PDF or HTML report. Supports fourteen species across global aquaculture regions, including Ostrea edulis, Magallana gigas, Crassostrea virginica, Crassostrea hongkongensis, and ten further species; see list_species(). Includes season-aware scoring, tidal height correction, Bayesian tolerance parameter updating from field observations, spatial block cross- validation (Roberts et al., 2017, doi:10.1111/ecog.02881), permutation variable importance, wave exposure and sediment stability modules, Harmful Algal Bloom (HAB) risk and anthropogenic disturbance scoring with optional live International Council for the Exploration of the Sea (ICES) data integration, hybrid larval dispersal connectivity scoring (union-find Gaussian kernel plus optional 'OpenDrift' or Finite Volume Community Ocean Model ('FVCOM') connectivity matrix), and batch multi-species comparison.