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

Free for research, education, and non-commercial use. Commercial entities wishing to embed oystermapR in a proprietary product should contact tristantucker48@gmail.com to discuss a commercial licence.