Complete survey workflow: Example Bay

Overview

This vignette walks through a complete oystermapR workflow using a simulated survey of Example Bay — a fictional sheltered coastal inlet with realistic physical characteristics. The full pipeline is covered:

1. Loading acoustic Doppler (ADCP) current data
2. Loading bathymetric soundings
3. Loading CTD water-quality data (temperature, salinity, chlorophyll_a, pH, alkalinity)
4. Merging all sensor datasets onto a common grid
5. Running quality control
6. Predicting suitability for Ostrea edulis (European Flat Oyster)
7. Scoring wave exposure and HAB risk
8. Exporting a five-band GeoTIFF for QGIS
9. pH and aragonite saturation scoring
10. Variable impact diagnostic
11. Fine-scale habitat area analysis
12. Tolerance curve visualisation

The example data files (example_bay_adcp.csv, example_bay_soundings.xyz, and example_bay_ctd.csv) are included in the package and represent a simulated mid-summer survey.

library(oystermapR)

Survey context

Example Bay is a sheltered inlet approximately 6 km × 4 km in extent, with depths ranging from 2 m at the shoreline to around 22 m in the central channel. A six-hour ADCP transect was run on a flood tide during June. Bathymetric soundings were collected concurrently using a single-beam echosounder.

The target species is Ostrea edulis, the European Flat Oyster, whose optimal conditions include: depths of 1–20 m, current speeds of 0.05–0.4 m/s, temperatures of 5–25°C, and salinities of 25–35 PSU.

Step 1: Load ADCP data

read_nortek_adcp() reads the Nortek Signature 500 merged CSV export. It auto-detects velocity bins, spatially averages ensembles onto a grid, and derives bed shear stress from the near-bed velocity profile. The spatial_res argument controls the decimal places used for lat/lon grid binning; 2 gives approximately 1 km cells, suitable for a bay-scale survey.

adcp_file <- system.file("extdata", "example_bay_adcp.csv", package = "oystermapR")

adcp <- read_nortek_adcp(
  file        = adcp_file,
  spatial_res = 2,
  verbose     = TRUE
)

head(adcp[, c("lat", "lon", "current_velocity", "shear_stress")])

The output contains one row per grid cell. current_velocity is the mean near-bed flow magnitude; shear_stress is the estimated bed shear stress in N/m², used by predict_oyster() for suspension-feeder scoring.

Step 2: Load bathymetric soundings

read_soundings_xyz() reads a space-delimited XYZ point cloud, grids and averages the depths, and derives slope and rugosity via finite differences.

xyz_file <- system.file("extdata", "example_bay_soundings.xyz", package = "oystermapR")

bathy <- read_soundings_xyz(
  file          = xyz_file,
  spatial_res   = 2,
  min_soundings = 5,
  verbose       = TRUE
)

head(bathy[, c("lat", "lon", "depth", "slope", "roughness")])

slope is the maximum downslope gradient in degrees at each grid cell; values above ~15° typically exclude oyster settlement in the scoring model. roughness is a dimensionless rugosity index (1.0 = flat; higher = more complex substrate).

Step 3: Load CTD water-quality data

The ADCP and echosounder provide hydrodynamic and bathymetric variables, but predict_oyster() can also use water-quality data when available. The Example Bay survey included a 5 × 4 grid of CTD casts recording temperature, salinity, chlorophyll_a, pH, and total alkalinity, stored as a plain CSV.

read_generic_csv() handles any tabular sensor export with flexible column matching. Column names are recognised automatically when they follow standard conventions (lat/lon, temperature, salinity, chlorophyll_a, ph, alkalinity). For instruments that export non-standard headers, supply an explicit col_map:

# Example with non-standard headers from a YSI EXO sonde export:
ctd <- read_generic_csv(
  "ysi_export.csv",
  col_map = c(lat = "GPS_Lat", lon = "GPS_Lon",
              temperature = "Temp_C", salinity = "Sal_PSU",
              ph = "pH_total", alkalinity = "TA_umolkg")
)

For Example Bay the column names match the oystermapR conventions directly:

ctd_file <- system.file("extdata", "example_bay_ctd.csv", package = "oystermapR")

ctd <- read_generic_csv(
  file        = ctd_file,
  spatial_res = 2,
  verbose     = TRUE
)

head(ctd[, c("lat", "lon", "temperature", "salinity", "chlorophyll_a",
             "ph", "alkalinity")])

Spatially averaging at spatial_res = 2 collapses the four replicate casts per station to a single value per ~1 km grid cell. The ph and alkalinity columns will be used in Step 9 for aragonite saturation scoring.

Step 4: Merge all sensor datasets

merge_sensor_data() performs a full outer join of any number of sensor dataframes on rounded lat/lon grid keys. Cells present in only one source are retained with NA for the missing variables, so no data is discarded. All three layers — ADCP, bathymetry, and CTD — are combined in a single call:

survey <- merge_sensor_data(adcp = adcp, bathy = bathy, ctd = ctd)

cat("Merged survey:", nrow(survey), "grid cells\n")
cat("Columns:", paste(names(survey), collapse = ", "), "\n")

A substrate_hardness column would typically come from a sidescan mosaic processed via read_sonar_tif(). Here we add a representative value for a mixed shell-gravel substrate, typical of productive inshore oyster ground.

set.seed(101)
n <- nrow(survey)
# 0 = soft mud, 1 = hard rock; 0.3--0.7 = shell/gravel mix
survey$substrate_hardness <- round(runif(n, 0.30, 0.70), 2)

Step 5: Quality control

qc_survey_data() applies three complementary checks to every numeric column:

• Range check — values outside physically plausible bounds for that variable (species-agnostic; e.g. salinity above 42 PSU) are flagged "range_fail".
• Statistical outlier — values beyond iqr_k × IQR from the median are flagged "outlier" (default k = 3, Tukey’s outer fence).
• Cross-variable sanity — physically implausible combinations (e.g. dissolved oxygen above 20 mg/L with temperature below 5°C) are flagged.

Setting apply_flags = TRUE replaces flagged values with NA so they are silently skipped downstream rather than causing erroneous scores.

survey_clean <- qc_survey_data(
  df          = survey,
  apply_flags = TRUE,
  verbose     = TRUE
)

# Count any flags raised across all columns
flag_cols <- grep("^qc_flag_", names(survey_clean), value = TRUE)
n_flagged <- sum(sapply(survey_clean[flag_cols], function(x) sum(!is.na(x) & x != "pass")))
cat("Total flagged values replaced with NA:", n_flagged, "\n")

The QC step is non-destructive by default (apply_flags = FALSE) — it adds qc_flag_<variable> columns so you can inspect which cells were problematic before deciding whether to exclude them.

Step 6: Predict suitability

predict_oyster() applies AHP-weighted scoring rules to each available variable, combines them into a suitability score in [0, 1], and classifies locations as High / Moderate / Low / Very Low / Excluded.

result <- predict_oyster(
  data    = survey_clean,
  species = "ostrea_edulis",
  verbose = TRUE
)

# Summary of suitability classes
table(result$suitability_class)

# Mean score and range
cat(sprintf(
  "Suitability: mean = %.2f, range = %.2f -- %.2f\n",
  mean(result$suitability, na.rm = TRUE),
  min(result$suitability,  na.rm = TRUE),
  max(result$suitability,  na.rm = TRUE)
))

The result dataframe retains all input columns plus suitability, suitability_class, data_completeness (fraction of variables scored), n_layers_scored (integer count of variables contributing at each point), and per-variable component scores (score_depth, score_current_velocity, score_ph, score_omega_aragonite, etc.).

# Points with fewer scored variables may have less reliable scores
summary(result$n_layers_scored)
table(result$n_layers_scored)

Step 7: Risk and disturbance scoring

oystermapR includes optional risk modules that can be appended to the result. Here we add wave exposure (derived from the current speed data and fetch geometry) and a simple HAB risk score.

# Wave exposure: uses current_velocity and depth as proxies for fetch exposure
result <- score_wave_exposure(result, verbose = FALSE)

# HAB risk: without live ICES data, scores from chlorophyll_a alone
result <- score_hab_risk(result, verbose = FALSE)

cat("Wave exposure range:", round(range(result$wave_exposure, na.rm=TRUE), 3), "\n")
cat("HAB risk range:     ", round(range(result$hab_risk,      na.rm=TRUE), 3), "\n")

Step 8: Export GeoTIFF for QGIS

export_geotiff() interpolates the suitability scores onto a regular raster and writes a five-band GeoTIFF. export_qml_style() writes a matching QGIS colour-ramp style file (.qml) so the layer renders immediately with the standard oystermapR colour scheme (red = excluded, green = high suitability).

# Write five-band GeoTIFF and companion QGIS style file
export_geotiff(
  df         = result,
  path       = "example_bay_suitability.tif",
  resolution = 0.001,
  contours   = TRUE
)
export_qml_style("example_bay_suitability.tif")

Load example_bay_suitability.tif into QGIS via Layer → Add Layer → Add Raster Layer, then right-click the layer and choose Load Layer Style to apply the .qml file.

The GeoTIFF contains five bands:

Band 5 is particularly useful as a data-coverage overlay: cells where only 2–3 variables were scored are visually distinguishable from cells with full data.

Inspecting component scores

The result dataframe includes a score_<variable> column for every variable that was scored. Comparing these helps diagnose which environmental factor is the main limiting constraint at a site.

score_cols <- grep("^score_", names(result), value = TRUE)
# Mean component score per variable (higher = more suitable)
col_means <- sort(colMeans(result[score_cols], na.rm = TRUE))
print(round(col_means, 3))

Variables scoring consistently below 0.5 are the main limiting factors for Ostrea edulis at this site. Scores near 1.0 indicate that variable is not constraining growth.

Step 9: pH and aragonite saturation scoring

pH and aragonite saturation state (Ω_arag) are scored for all 17 species. If ph and alkalinity are present in the merged survey data, predict_oyster() computes omega_aragonite automatically via calculate_aragonite() before the scoring step — no manual pre-processing is needed.

# Verify aragonite was auto-calculated and scored
"omega_aragonite"  %in% names(result)      # column present
"score_ph"         %in% names(result)      # pH scored
"score_omega_aragonite" %in% names(result) # aragonite scored

# Distribution of omega_aragonite across Example Bay
summary(result$omega_aragonite)

If your sensor does not record alkalinity, you can estimate Ω_arag from typical open-ocean values for your region and supply it as a pre-computed column, or omit it — the scoring model will redistribute its weight across remaining variables.

# Manual calculation: sensors gave pH only, alkalinity approximated
df$alkalinity <- 2300   # µmol/kg — representative NE Atlantic value
df$omega_aragonite <- calculate_aragonite(
  pH          = df$ph,
  alkalinity  = df$alkalinity,
  temperature = df$temperature,
  salinity    = df$salinity
)

Step 10: Variable impact diagnostic

variable_impact() summarises the contribution of each environmental variable to the suitability score across the dataset. It is the primary QA tool for understanding model behaviour and planning future surveys.

impact <- variable_impact(result, "ostrea_edulis")
print(impact)

The output columns are:

• norm_weight_pct — the variable’s share of the total AHP weight at a typical fully-observed point.
• mean_score — average per-variable score across non-excluded points. Values below ~0.5 identify environmental bottlenecks at this site.
• mean_contribution — norm_weight_pct × mean_score / 100. The net weighted contribution to the suitability score. Highest-impact variables have both high weight and high score.
• pct_coverage — percentage of non-excluded points where this variable was observed (non-NA). Low values indicate data sparsity.

# Variables scoring below 0.5 are potential site limiters
impact[impact$mean_score < 0.5, c("variable", "norm_weight_pct", "mean_score")]

# Variables with sparse data coverage
impact[impact$pct_coverage < 80, c("variable", "pct_coverage")]

Use sort_by = "pct_coverage" to prioritise sensor deployment for the next survey, or sort_by = "mean_score" to focus on the worst-performing variables.

Step 11: Fine-scale habitat area analysis

area_summary() converts the point-based result into habitat area estimates at sub-hectare resolution. This is especially useful for restoration planning, where the difference between 200 m² and 800 m² of suitable habitat is operationally significant.

# Auto-estimate cell size from median nearest-neighbour spacing
s <- area_summary(result, verbose = TRUE)

The function prints a concise summary to the console. The returned list contains three elements:

# Per-class breakdown
s$class_summary[, c("class", "area_m2", "area_ha", "pct_total_area",
                    "mean_suitability")]

# Totals
s$total[c("surveyed_area_m2", "suitable_area_m2", "pct_suitable", "cell_size_m")]

# Contiguous patches of High + Moderate suitability
head(s$patches)

# Patches meeting the OSPAR 100 m² viable area threshold
viable <- s$patches[s$patches$viable, ]
cat(nrow(viable), "viable patches; largest:", round(max(viable$area_m2)), "m²\n")

For ROV or AUV surveys with a known fixed grid resolution, supply it explicitly to avoid auto-estimation error:

# 5 m ROV grid survey
s5m <- area_summary(result, cell_size_m = 5, viable_area_m2 = 100)

# 25 m ADCP trackline survey, larger minimum viable unit for production scale
s25m <- area_summary(result, cell_size_m = 25, viable_area_m2 = 500)

Step 12: Tolerance curve visualisation

plot_tolerance() draws the mathematical scoring function for any scored variable directly from the species tolerance parameters — no dataset required. This is useful for QA (confirming threshold values match published literature), stakeholder reporting (“here is exactly what the model rewards”), and creating publication-quality figures.

# Temperature scoring curve with zone shading
plot_tolerance("ostrea_edulis", "temperature")

# All four seasons overlaid on a single plot
plot_tolerance("ostrea_edulis", "temperature", season = "all")

# Ocean acidification variables
plot_tolerance("ostrea_edulis", "ph")
plot_tolerance("ostrea_edulis", "omega_aragonite")

# Dissolved oxygen: compare tolerance between species
plot_tolerance("ostrea_edulis",        "dissolved_oxygen")
plot_tolerance("crassostrea_iredalei", "dissolved_oxygen")

Curves are colour-coded: green background = optimal zone, orange = acceptable/poor, red = hard-excluded. Dashed red vertical lines show the exclusion thresholds. The function returns a ggplot2 object invisibly, so it can be saved or modified:

p <- plot_tolerance("ostrea_edulis", "salinity")
ggplot2::ggsave("salinity_tolerance_O_edulis.png", p, width = 8, height = 5)

For data-driven response curves showing how the model actually responded to observed covariate values in this survey, use sensitivity_analysis() instead.

Next steps

• Validation — if you have historical presence/absence records for the site, pass them to validate_against_records() to calculate AUC, TSS, F1, and Brier score against model predictions.
• Bayesian tolerance updating — update_species_tolerances() can refine the O. edulis scoring parameters from field observations at this site.
• Multi-species comparison — run the same workflow for "magallana_gigas" and call compare_species() to see which species is better suited to each grid cell. Useful for restoration target selection.
• Sensitivity analysis — sensitivity_analysis() produces partial dependence curves showing how suitability varies with each variable at the observed covariate values in this survey (data-driven, contrast to plot_tolerance() which is parameter-driven).
• Live data — fetch_live_environmental_data() can replace the simulated temperature and salinity with real-time CMEMS model output or ICES observational data; see ?oystermapR_live_config for credential setup.

Session info

sessionInfo()