oystermapR - 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.
