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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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.