oystermapR: Predict and Map Oyster Growth Suitability from Environmental
Data
Predicts spatial suitability for oyster growth from environmental
survey data using rank-reciprocal 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 five-band 'GeoTIFF' heatmap for 'QGIS', contour
lines, and a formatted PDF or HTML report. Supports seventeen species across
global aquaculture regions, including Ostrea edulis, Magallana gigas,
Crassostrea virginica, Crassostrea hongkongensis, and thirteen further
species; see list_species(). Includes ocean acidification scoring via
in-house aragonite saturation state (Omega_arag) calculation using
Lueker et al. (2000) <doi:10.1016/S0304-4203(00)00022-0> and Mucci (1983)
<doi:10.1357/002224083788520153> equilibrium constants (no external
dependencies), variable impact diagnostics (variable_impact()), fine-scale
habitat area analysis for restoration reporting in m2 with contiguous patch
identification (area_summary()), tolerance curve visualisation
(plot_tolerance()), 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), batch
multi-species comparison, and evidence-weighted scoring in which each
variable's weight is its published rank multiplied by the measured
reliability and realised spread of that layer, with the fraction of the
species' evidence base the survey observed reported alongside the score
rather than normalised away, and lineage-specific tolerance profiles for
related populations, in which thinly sampled lineages borrow strength from
relatives across a genetic distance matrix only where tolerance is shown to
track relatedness, with inverse mapping of where each lineage is more likely
to occur and which source population suits a site.
| Version: |
1.6.0 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
dplyr (≥ 1.0.0), terra (≥ 1.7.18), sf (≥ 1.0.0), rlang (≥
1.0.0), cli (≥ 3.0.0), stats, utils |
| Suggests: |
ggplot2 (≥ 3.4.0), testthat (≥ 3.0.0), knitr, rmarkdown (≥
2.20), tinytex, httr (≥ 1.4.0), jsonlite (≥ 1.8.0), ncdf4 (≥
1.19), gstat (≥ 2.1.0), sp (≥ 1.6.0), lubridate (≥ 1.9.0), suncalc (≥ 0.5.0), oce (≥ 1.7.0) |
| Published: |
2026-10-13 |
| DOI: |
10.32614/CRAN.package.oystermapR |
| Author: |
T Tucker  [aut,
cre] |
| Maintainer: |
T Tucker <tristantucker48 at gmail.com> |
| BugReports: |
https://github.com/trissyboats/oystermapR/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://github.com/trissyboats/oystermapR |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Materials: |
README, NEWS |
| CRAN checks: |
oystermapR results |
Documentation:
Downloads:
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