Score where marine species can establish across any survey area, then add the overlays and analyses that turn the score into a permit, a lease application, or an investment case.
Native and invasive. Tropical and polar. Conservation-priority and commercially harvested. Every species ships with documented tolerance envelopes drawn from peer-reviewed literature.
Habitat suitability tells you where the species could live. These packs add what regulators, lessors, insurers, and funders need to see alongside it.
Marine protected areas, navigation channels, military closures, cable corridors, designated shellfish waters, statutory shipwreck protection zones.
NOAA AOA boundaries, ESA-listed species critical habitat, EFH consultation zones, designated shellfish growing waters, regional best-practice exclusion zones.
CMIP6 ensemble sea-surface temperature, ocean pH, aragonite saturation, dissolved oxygen, marine-heatwave frequency, NOAA SLR scenarios. Four pathways × four horizons (2030 / 2050 / 2080 / 2100).
NOAA NDBC buoys, satellite chlorophyll-a, OFS tide / current forecasts, OISST anomalies, Coral Reef Watch DHW, operational HAB alerts, WAVEWATCH III wave forecasts.
ESVD benefit-transfer coefficients, US BEA Marine Economy figures, FAO commodity prices, Verra VCS blue-carbon methodology, FEMA NFIP claim records, NOAA commercial-fishery landings.
Every scoring run produces the same set of deliverables. Designed for the people who'll read them - regulators, funders, leaseholders, restoration committees.
In-browser viridis heatmap with points, density, four basemaps (light / dark / OSM / satellite), and scale bar. Hover any cell for its score and coordinates.
GeoTIFF raster, .qlr layer-definition, .qgs project file. Pre-styled with the same colour ramp so your QGIS view matches the in-app heatmap exactly.
Multi-page PDF: project summary, scoring distribution, regulatory conflict tagging, climate sensitivity, economic figures, mitigation recommendations, full citation block.
Every input row + per-variable scores + final suitability + exclusion reasons + limiting factors. Bring it into Excel, R, Python, or your stats package of choice.
Hash-chained append-only JSONL log of every run, overlay loaded, parameter changed, and report exported. Designed for regulator submissions and FOIA-ready archival.
REST/JSON endpoints for upload, run submission, status polling, and result download. Wire scoring into your own QGIS plugin, R script, or Python pipeline.
Published tolerance envelopes are the starting point — not the answer. Upload your own presence / absence records and the Bayesian posterior refits to reflect what your species actually does in your water. Optional bootstrap power analysis tells you how confident the fit is and how much more data would sharpen it.
Drop a CSV of lat / lon / presence records — environment variables optional. Missing env vars are back-filled from CMEMS at the survey’s bounding box. Records are stored per organisation and are user-deletable at any time.
MAP + Laplace approximation (fast, default) or full MCMC (small-N). The species’s tolerance envelopes are refit on your records; the peer-reviewed prior stays in the mix, so a handful of local records shifts the posterior without erasing the literature.
200 resample-and-refit passes over your records. Reports 95% confidence intervals on every fitted parameter and a sample-size recommender: “you have 22 records; 88 would halve the CI, 352 would quarter it.” Add-on to any envelope fit.
Marine survey data comes in every possible naming convention and every possible unit. Rather than making you rename columns and convert units before you upload, we detect what you meant, tell you what we detected, and let you correct anything that’s wrong before the run kicks off.
A rules engine covers the ~50 most common naming patterns per variable
(lat / latitude / y / Y_WGS84;
depth / depth_m / D.fath / Water_depth /
z). For anything weirder we fall back to an LLM classification with your
confirmation. Once you confirm a mapping, it’s saved per organisation — next
upload from your team just works.
Prof_T and your
modeller uses temperature_c. Both get recognised as temperature in °C.Depth in fathoms → metres. Temperature in Fahrenheit → Celsius. Salinity in PPT → PSU. Dissolved oxygen in mL/L or µmol/kg → mg/L. Coordinates in DMS, DDM, UTM zones, British National Grid, or WGS84 spherical Mercator → decimal degrees. Every conversion is shown to you with the source unit and the target unit before the run submits.
After parsing, you see a preview table: raw column name, detected variable, detected unit, converted values (first few rows), and a status pill (green = confident, amber = please confirm, red = we could’t parse). Change any mapping with one click. Nothing runs until you say go.
Standard CSV is supported on every tier. Column names + units are auto-detected. Raw sonar formats decode natively via the sonar pipeline.
Upload the CSV you already have. We recognise every common naming and unit convention marine data comes in, show you what we detected, and let you confirm or override before the run kicks off. No pre-formatting, no manual conversion.
Column-name examples we handle: D.fath, depth_ft,
z, Water_depth_m, Prof_T, T_C,
Sal_psu, DO_mgL, and thousands more. Your mapping is
saved per organisation so subsequent uploads just work.
Clean-room readers are pure R, ship on all tiers, and have no Python or GPL dependencies. Substrate classification (mud / sand / gravel / cobble / bedrock, Folk-5 aligned) available from any clean-room input.
Free tier covers your first survey end-to-end. No credit card. Five minutes from signing up to first heatmap.
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