@@ -8,7 +8,7 @@ Datasets we currently lack are tracked in [boettiger-lab/data-workflows#161](htt
 
 | Paper artefact | What it shows | Reproducible? | Blocker |
 |---|---|:---:|---|
-| **Headline: 396 M in existing PAs, 1.15 B within 10 km** | Resident + buffer population for current PA/OECM network | 🟡 Partial | Have WDPA + WD-OECM + GHS-POP. Numbers will differ by year (we have GHS-POP 2020, not 2023). |
+| **Headline: 396 M in existing PAs, 1.15 B within 10 km** | Resident + buffer population for current PA/OECM network | 🟢 Resident reproduced | **Reproduced 385.5 M in PA+OECM (4.9% of world pop)** via GHS-POP 2020 × (WDPA ∪ WD-OECM) hex at h8 — within ~3% of the paper's 396 M (snapshots are June 2026 / GHS-POP 2020). The 10 km buffer figure still needs a spatial buffer step. |
 | **Fig 1 — scenario maps** | Three Target-3 scenarios overlaid with current PAs | ❌ | Scenario rasters not hosted; the `prioritizr` runs that produced them require species AOH, KBA, NCP, HMI we don't have |
 | **Fig 2a — resident + neighbouring population by scenario** | Population in new PAs vs buffer | ❌ | Same scenario rasters |
 | **Fig 2b — HDI breakdown by scenario** | % of scenario population in Low / Medium / High / Very High HDI | ❌ | MOSAIKS gridded HDI not in catalog |
@@ -26,7 +26,7 @@ Datasets we currently lack are tracked in [boettiger-lab/data-workflows#161](htt
 
 These analyses are doable with the layers currently configured in `layers-input.json`:
 
-- **Global conserved-area coverage**: `% of land area inside a designated PA or OECM` — union of `wdpa` and `wdoecm-may-2026` hex, deduplicated on `h8`. This is the paper's PA+OECM basis; WDPA alone lands short of the 17.2% figure.
+- **Global conserved-area coverage**: `% of land area inside a designated PA or OECM` — union of `wdpa` and `wdoecm-may-2026` hex, deduplicated on `h8`, with `cgls-lc100-2019` as the land mask (exclude `lc_class` 0/80/200 = no-data/water/sea so ocean PAs don't inflate the denominator). **Reproduced 17.54%** — essentially matches the paper's 17.2% PA+OECM figure. WDPA alone lands lower.
 - **Population inside / near conserved areas**: `ghs-pop-2020` × (`wdpa` ∪ `wdoecm-may-2026`). Comparable to the paper's first row.
 - **Country-level coverage**: `ISO3` on WDPA/OECM × Overture countries.
 - **Ecoregion representation**: `wwf-ecoregions-2017` × (`wdpa` ∪ `wdoecm-may-2026`). % of each ecoregion inside the conserved-area network — speaks to the biodiversity-representation argument of the paper without reproducing the `prioritizr` optimization.
 
@@ -292,6 +292,75 @@
                 }
             ]
         },
+        {
+            "collection_id": "cgls-lc100-2019",
+            "collection_url": "https://s3-west.nrp-nautilus.io/public-land-cover/cgls-lc100-2019/stac-collection.json",
+            "assets": [
+                {
+                    "id": "cgls-lc100-2019-cog",
+                    "display_name": "Land Cover (Copernicus 100m, 2019)",
+                    "group": "Ecosystems & Freshwater",
+                    "visible": false,
+                    "legend_type": "categorical",
+                    "legend_label": "FAO LCCS class"
+                }
+            ]
+        },
+        {
+            "collection_id": "wetlands-ramsar",
+            "collection_url": "https://s3-west.nrp-nautilus.io/public-wetlands/ramsar/stac-collection.json",
+            "assets": [
+                {
+                    "id": "ramsar-pmtiles",
+                    "display_name": "Ramsar Wetlands (Intl. Importance)",
+                    "group": "Ecosystems & Freshwater",
+                    "visible": false,
+                    "default_style": {
+                        "fill-color": "#0277bd",
+                        "fill-opacity": 0.5
+                    },
+                    "outline_style": {
+                        "line-color": "#01466b",
+                        "line-width": 0.5,
+                        "line-opacity": 0.8
+                    },
+                    "tooltip_fields": [
+                        "Site name",
+                        "Country",
+                        "Wetland Type",
+                        "Area (ha)",
+                        "Designation date"
+                    ]
+                }
+            ]
+        },
+        {
+            "collection_id": "hydrobasins-v1c",
+            "collection_url": "https://s3-west.nrp-nautilus.io/public-hydrobasins/stac-collection.json",
+            "assets": [
+                {
+                    "id": "level-03-pmtiles",
+                    "display_name": "Watersheds (HydroBASINS L3)",
+                    "group": "Ecosystems & Freshwater",
+                    "visible": false,
+                    "default_style": {
+                        "fill-color": "#0288d1",
+                        "fill-opacity": 0
+                    },
+                    "outline_style": {
+                        "line-color": "#0288d1",
+                        "line-width": 0.6,
+                        "line-opacity": 0.6
+                    },
+                    "tooltip_fields": [
+                        "HYBAS_ID",
+                        "SUB_AREA",
+                        "UP_AREA",
+                        "ORDER"
+                    ]
+                }
+            ]
+        },
         {
             "collection_id": "overture-divisions-countries-2026-02-18",
             "collection_url": "https://s3-west.nrp-nautilus.io/public-overturemaps/2026-02-18.0/countries/stac-collection.json",
 
@@ -40,6 +40,9 @@ Use `list_datasets` / `get_dataset` (or `browse_stac_catalog` / `get_stac_detail
   - **iNaturalist ranges** (`inaturalist-ranges`) — modeled species ranges (H3 res 4 + polygons); community-science complement to IUCN.
 - **NCP — Biodiversity & Natural Habitat** (Chaplin-Kramer 2019 composite, 300 m) and **Irrecoverable / Vulnerable Carbon** (Noon 2022, 300 m).
 - **GHS-POP 2020** — 90 m gridded population count from the Global Human Settlement Layer. Use for "people living in / near" analyses.
+- **Copernicus Global Land Cover** (`cgls-lc100-2019`, 100 m, FAO LCCS classes) — global land cover. Categorical COG + H3 hex (res 9, `lc_class` is the per-cell mode; never sum/average). Also serves as a **land mask** (see SQL guidelines).
+- **Ramsar Wetlands** (`wetlands-ramsar`) — wetlands of international importance; a recognized conservation designation complementing WDPA/OECM. Overlapping boundaries can dupe hex rows — `SELECT DISTINCT h8` before joining.
+- **HydroBASINS v1c** (`hydrobasins-v1c`) — global watershed boundaries (levels 1–12; level 3 loaded as a map layer). Freshwater analysis unit.
 - **Overture countries / regions** — administrative context for country- or state-level breakdowns.
 
 ## What we do NOT have (don't fabricate)
@@ -60,3 +63,4 @@ If a user asks about HDI overlap, wild harvesting, farm size, or livestock syste
 - For PA coverage, deduplicate hex cells (`COUNT(DISTINCT h8)`) — multiple overlapping PAs inflate raw counts.
 - Population sums: GHS-POP hex stores a population count per H3 cell, so `SUM(population)` over the hex is directly valid (one row per h9; no GROUP BY or dedup needed). The 2020 world total is ~7.84 B.
 - For country-level breakdowns, prefer the `ISO3` column on WDPA over spatial joins to Overture, which is slower.
+- **Terrestrial coverage / land masking.** WDPA and WD-OECM include marine areas, so a raw h8 conserved-cell count covers ocean too. For *land* coverage, intersect with `cgls-lc100-2019` hex as a land mask: treat a cell as land when `lc_class NOT IN (0, 80, 200)` (no-data / permanent water / open sea). `% land conserved = conserved∩land cells ÷ land cells`. This reproduces the paper's ~17.2% PA+OECM figure (~17.5%).