Predict and Map Oyster Growth Suitability from Environmental Data.
oystermapR 
Predict and map oyster growth suitability from environmental survey data
oystermapR takes tabular sensor data — from ADCPs, CTDs, bathymetric sonar, or standard CSV files — applies species-specific AHP-weighted scoring rules, and returns per-location suitability scores alongside a GeoTIFF heatmap ready to load in QGIS.
Supported species
| Key | Species | Common name |
|---|---|---|
ostrea_edulis | Ostrea edulis | European Flat Oyster |
magallana_gigas | Magallana gigas | Pacific Oyster |
crassostrea_angulata | Crassostrea angulata | Portuguese Oyster |
ostrea_stentina | Ostrea stentina | Denticulate Flat Oyster |
ostrea_lurida | Ostrea lurida | Olympia Oyster |
Installation
# From CRAN (once accepted)
install.packages("oystermapR")
# Development version from GitHub
devtools::install_github("trissyboats/oystermapR")
Requires R >= 4.1.0. Core dependencies: dplyr, terra, sf, cli, rlang.
Quick start
library(oystermapR)
# Load your survey data
df <- read.csv("my_survey.csv")
# Run the prediction
result <- predict_oyster(
data = df,
species = "ostrea_edulis",
output_geotiff = "oyster_suitability.tif",
verbose = TRUE
)
# Inspect high-suitability sites
subset(result, suitability_class == "High")
# Export matching QGIS colour ramp style
export_qml_style("oyster_suitability.tif")
The output dataframe contains suitability (0–1), suitability_class (High / Moderate / Low / Very Low / Excluded), per-variable component scores, and the original columns.
Input data
Your CSV needs at minimum lat, lon, and date. Any environmental columns present are scored automatically — missing variables are skipped and their weights redistributed.
| Variable | Recognised column names |
|---|---|
| Temperature (°C) | temperature, temp, temp_c |
| Salinity (PSU) | salinity, sal, salinity_psu |
| Dissolved oxygen (mg/L) | dissolved_oxygen, do, oxygen |
| Depth (m) | depth, depth_m |
| Current velocity (m/s) | current_velocity, velocity, current |
| Shear stress (N/m²) | shear_stress, tau, bed_shear |
| Chlorophyll-a (µg/L) | chlorophyll_a, chla, chlorophyll |
| Turbidity (NTU) | turbidity, ntu, turb |
| Slope (degrees) | slope, slope_deg |
| Substrate hardness | substrate_hardness, hardness |
A sample dataset is included at inst/extdata/sample_survey.csv.
Sensor readers
# Nortek Signature 500 ADCP
adcp <- read_nortek_adcp("adcp_export.csv")
# Ping 3DSS / BioBase bathymetric raster
bathy <- read_sonar_tif("bathymetry.tif")
# Merge multiple sensor sources onto a common spatial grid
survey <- merge_sensor_data(adcp = adcp, bathy = bathy)
Validation and diagnostics
# Validate against known presence/absence records
val <- validate_against_records(result, records)
val$auc # ROC-AUC
val$tss # True Skill Statistic
# Spatial block cross-validation (avoids inflated AUC from spatial autocorrelation)
cv <- spatial_block_cv(result, records, n_blocks = 5)
cv$mean_auc
# Variable importance
imp <- permutation_importance(result, records)
# Partial dependence curve for a single variable
sensitivity_analysis(result, records, variable = "temperature")
Bayesian tolerance updating
Update species tolerance parameters from your own field observations:
fit <- update_species_tolerances(
records = field_data,
species = "ostrea_edulis",
update_vars = c("temperature", "salinity", "depth")
)
# Subsequent predict_oyster() calls use the updated parameters automatically
result2 <- predict_oyster(df, "ostrea_edulis")
# Persist across sessions
save_tolerance_update("ostrea_edulis")
Additional modules
| Function | Purpose |
|---|---|
score_wave_exposure() | JONSWAP wave height from fetch and wind speed |
score_sediment_stability() | Shields parameter mobility analysis |
score_larval_connectivity() | Hybrid Gaussian kernel + OpenDrift connectivity matrix |
score_predation_risk() | Starfish, crab, and snail predation pressure |
score_hab_risk() | Harmful algal bloom risk (PSP/ASP/DSP/AZP) |
score_anthropogenic_disturbance() | Bottom trawling, anchor damage, dredging |
add_shellfish_classification() | UK/EU harvesting area classification (A/B/C) |
compare_species() | Side-by-side suitability across multiple species |
composite_seasonal() | Merge summer/winter surveys into a composite score |
generate_report() | Export a formatted PDF or HTML report |
QGIS workflow
# Export GeoTIFF + contour lines
predict_oyster(df, "ostrea_edulis", output_geotiff = "suitability.tif")
# Export matching colour ramp style file
export_qml_style("suitability.tif")
Load suitability.tif in QGIS, then drag the .qml file onto the layer to apply the yellow → orange → red suitability colour ramp instantly.
Citation
If you use oystermapR in published work, please cite:
Tucker T. (2026). oystermapR: Predict and Map Oyster Growth Suitability from Environmental Data. R package version 1.0.0. https://github.com/trissyboats/oystermapR
License
GPL-3 © T Tucker
Free for research, education, and non-commercial use. Commercial entities wishing to embed oystermapR in a proprietary product should contact [email protected] to discuss a commercial licence.