Criterion B Details

# Default ecosystem code for template development.
# This line is replaced by build_ecosystem_pages.py for each ecosystem.
ecosystem_code = 'Zonas Pantanosas Salinas'

Import Python modules.

import os
import yaml
from pathlib import Path
from lonboard import Map
from rle.core import Ecosystems, criterion_b_status, rle_category
from rle.core.eoo import make_eoo
from rle.core.aoo import make_aoo_grid

Load the country config file.

project_root = os.environ.get('PIXI_PROJECT_ROOT', str(Path('..').resolve()))
config_path = Path(project_root) / 'config' / 'country_config.yaml'
with open(config_path) as f:
    config = yaml.safe_load(f)

# Ecosystem index (the COG pixel value for this ecosystem), looked up by code
# from the canonical index table. Absent if the index has not been built yet.
import csv
ecosystem_index = None
index_csv = Path(project_root) / 'config' / 'ecosystems' / 'index.csv'
if index_csv.exists():
    with open(index_csv) as f:
        for row in csv.DictReader(f):
            if row['code'] == ecosystem_code:
                ecosystem_index = int(row['index'])
                break

Load & Filter Ecosystem Data

Load data for all the ecosystems.

import sys
# Shared config helpers live in scripts/; make them importable from here.
sys.path.insert(0, str(Path(project_root) / 'scripts'))
from _config import ensure_vector_source

source = config['ecosystem_source']
# ecosystem_code_column is optional: fall back to the name column.
ecosystem_column = source.get('ecosystem_code_column') or source.get('ecosystem_name_column')
# Prefer the ecosystem-sorted `optimized_data` copy when configured, so that
# filtering to one ecosystem uses parquet predicate pushdown instead of loading
# the whole national map into memory. Falls back to `data`.
ecosystems = Ecosystems.from_file(
    ensure_vector_source(source.get('optimized_data') or source['data']),
    ecosystem_column=ecosystem_column,
    ecosystem_name_column=source.get('ecosystem_name_column'),
    functional_group_column=source.get('functional_group_column')
)

Filter by the Zonas Pantanosas Salinas and check the number of features.

ecosystem = ecosystems.filter(ecosystem_code)
has_data = ecosystem.size() > 0
print(f'{ecosystem.size() = }')
if not has_data:
    from IPython.display import Markdown, display
    display(Markdown(
        f'**No spatial data found for {ecosystem_code}.** '
        f'Criterion B calculations are skipped.'
    ))

ecosystem.size() = 94

Extent of Occurrence (EOO) (subcriterion B1)

Extent of occurrence (EOO). The EOO of an ecosystem is the area (km2) of a minimum convex polygon – the smallest polygon in which no internal angle exceeds 180° that encompasses all known current spatial occurrences of the ecosystem type.

The minimum convex polygon (also known as a convex hull) must not exclude any areas, discontinuities or disjunctions, regardless of whether the ecosystem can occur in those areas or not. Regions such as oceans (for terrestrial ecosystems), land (for coastal or marine ecosystems), or areas outside the study area (such as in a different country) must remain included within the minimum convex polygon to ensure that this standardised method is comparable across ecosystem types. In addition, these features contribute to spreading risks across the distribution of the ecosystem by making different parts of its distribution more spatially independent.

Calculate EOO

Start by calculating the convex hull of the ecosystem’s distribution.

import geopandas as gpd

if has_data:
    ecosystem_geometry = ecosystem.geometry.union_all()
    gdf_ecosystem_polygons = gpd.GeoDataFrame(geometry=[ecosystem_geometry], crs=ecosystem.geometry.crs)
    hull = ecosystem_geometry.convex_hull
    gdf_hull = gpd.GeoDataFrame(geometry=[hull], crs=ecosystem.geometry.crs)

Display the ecosystem’s distribution and the convex hull.

from lonboard import Map, PolygonLayer
from rle.core.viz import smart_map

if has_data:
    eoo_hull = make_eoo(ecosystem).compute()
    display(smart_map([eoo_hull, ecosystem]))

/home/runner/work/rle-tyler-colombia/rle-tyler-colombia/.pixi/envs/default/lib/python3.11/site-packages/lonboard/_geoarrow/ops/reproject.py:116: UserWarning: Input being reprojected to EPSG:4326 CRS.
Lonboard is only able to render data in EPSG:4326 projection.
  warnings.warn(
/home/runner/work/rle-tyler-colombia/rle-tyler-colombia/.pixi/envs/default/lib/python3.11/site-packages/lonboard/_geoarrow/ops/reproject.py:116: UserWarning: Input being reprojected to EPSG:4326 CRS.
Lonboard is only able to render data in EPSG:4326 projection.
  warnings.warn(

if has_data:
    hull_ea = gdf_hull.to_crs("ESRI:54034")
    eoo = hull_ea.geometry.iloc[0].area / 1e6
    print(f'EOO is {eoo:.1f} km2')

EOO is 8354.6 km2

Then calculate the area of the convex hull polygon.

Direct calculation of EOO

EOO can also be calculated directly using …

if has_data:
    ecosystem.eoo

Verify that the area returned by calling make_eoo(ecosystem).compute().area_km2 is the same as the area of the convex hull polygon.

if has_data:
    assert ecosystem.eoo == eoo

Area of Occupancy (AOO) (subcriterion B2)

The protocol for this adjustment includes the following steps:

1. Intersect AOO grid with the ecosystem’s distribution map.
2. Calculate extent of the ecosystem type in each grid cell (area) and sum these areas to obtain the total ecosystem area (total area).
3. Arrange grid cells in ascending order based on their area (smaller first). Calculate accumulated sum of area per cell (cumulative area).
4. Calculate cumulative proportion by dividing cumulative area by total area (cumulative proportion takes values between 0 and 1)
5. Calculate AOO by counting the number of cells with a cumulative proportion greater than 0.01 (i.e. exclude cells that in combination account for up to 1% of the total mapped extent of the ecosystem type).

AOO Calculation Details

Intersect AOO grid and ecosystem map

1. Intersect AOO grid with the ecosystem’s distribution map

from pathlib import Path
from rle.core.aoo import make_aoo_grid_cached

if has_data:
    # Prefer a prebuilt grid cache (e.g. a gs:// URI) so the national AOO grid
    # is not recomputed during CI renders — computing it from the full national
    # ecosystem map peaks at many GB of RAM. Fall back to a local cache when no
    # prebuilt cache is configured. Build one with `pixi run build-caches`.
    cache_path = (
        source.get('aoo_grid_cache_url')
        or (Path(project_root) / '.cache' / 'aoo_grid.parquet')
    )
    aoo_grid = make_aoo_grid_cached(ecosystems, cache_path=cache_path)
    aoo_grid_filtered = aoo_grid.filter_by_ecosystem(ecosystem_code)

Visualize variations in the AOO grid.

from matplotlib.colors import LinearSegmentedColormap
from lonboard.colormap import apply_continuous_cmap
from rle.core.aoo import slugify_ecosystem_name

ecosystem_column = slugify_ecosystem_name(ecosystem_code)
if has_data:
    cmap = LinearSegmentedColormap.from_list("white_red", ["white", "red"])
    values = aoo_grid_filtered.grid_cells[ecosystem_column].values
    normalized = (values - values.min()) / (values.max() - values.min())
    colors = apply_continuous_cmap(normalized, cmap)
    display(smart_map([(aoo_grid_filtered, {"get_fill_color": colors}), ecosystem]))

/home/runner/work/rle-tyler-colombia/rle-tyler-colombia/.pixi/envs/default/lib/python3.11/site-packages/lonboard/_geoarrow/ops/reproject.py:116: UserWarning: Input being reprojected to EPSG:4326 CRS.
Lonboard is only able to render data in EPSG:4326 projection.
  warnings.warn(

Calculate grid cell area and total area

2. Calculate extent of the ecosystem type in each grid cell (area) and sum these areas to obtain the total ecosystem area (total area).

if has_data:
    keep = ['geometry', 'grid_col', 'grid_row', ecosystem_column]
    gdf = aoo_grid_filtered.grid_cells[keep]
    display(gdf)

	geometry	grid_col	grid_row	Zonas_Pantanosas_Salinas
0	POLYGON ((-72.9432 11.46949, -72.9432 11.56174...	-813	126	0.011193
1	POLYGON ((-72.85337 11.46949, -72.85337 11.561...	-812	126	0.018349
2	POLYGON ((-72.85337 11.56174, -72.85337 11.654...	-812	127	0.011454
3	POLYGON ((-72.76354 11.46949, -72.76354 11.561...	-811	126	0.005184
4	POLYGON ((-72.76354 11.56174, -72.76354 11.654...	-811	127	0.052679
5	POLYGON ((-72.76354 11.65401, -72.76354 11.746...	-811	128	0.032612
6	POLYGON ((-72.67371 11.65401, -72.67371 11.746...	-810	128	0.059683
7	POLYGON ((-72.58387 11.65401, -72.58387 11.746...	-809	128	0.154781
8	POLYGON ((-72.49404 11.65401, -72.49404 11.746...	-808	128	0.066528
9	POLYGON ((-72.49404 11.74632, -72.49404 11.838...	-808	129	0.014462
10	POLYGON ((-72.40421 11.74632, -72.40421 11.838...	-807	129	0.014153
11	POLYGON ((-72.22455 11.83865, -72.22455 11.931...	-805	130	0.016290
12	POLYGON ((-72.13472 11.83865, -72.13472 11.931...	-804	130	0.048033
13	POLYGON ((-72.13472 11.93102, -72.13472 12.023...	-804	131	0.216110
14	POLYGON ((-72.13472 12.02342, -72.13472 12.115...	-804	132	0.143729
15	POLYGON ((-72.13472 12.11584, -72.13472 12.208...	-804	133	0.000440
16	POLYGON ((-72.13472 12.2083, -72.13472 12.3007...	-804	134	0.009282
17	POLYGON ((-72.04489 12.02342, -72.04489 12.115...	-803	132	0.054030
18	POLYGON ((-72.04489 12.11584, -72.04489 12.208...	-803	133	0.003763
19	POLYGON ((-72.04489 12.2083, -72.04489 12.3007...	-803	134	0.001848
20	POLYGON ((-71.95505 12.11584, -71.95505 12.208...	-802	133	0.136313
21	POLYGON ((-71.95505 12.2083, -71.95505 12.3007...	-802	134	0.006185
22	POLYGON ((-71.86522 12.11584, -71.86522 12.208...	-801	133	0.178797
23	POLYGON ((-71.86522 12.2083, -71.86522 12.3007...	-801	134	0.028378
24	POLYGON ((-71.86522 12.30079, -71.86522 12.393...	-801	135	0.013234
25	POLYGON ((-71.77539 12.11584, -71.77539 12.208...	-800	133	0.029527
26	POLYGON ((-71.77539 12.2083, -71.77539 12.3007...	-800	134	0.106712
27	POLYGON ((-71.77539 12.30079, -71.77539 12.393...	-800	135	0.077685
28	POLYGON ((-71.68556 12.2083, -71.68556 12.3007...	-799	134	0.002235
29	POLYGON ((-71.68556 12.30079, -71.68556 12.393...	-799	135	0.100297
30	POLYGON ((-71.59573 12.30079, -71.59573 12.393...	-798	135	0.051994
31	POLYGON ((-71.59573 12.39331, -71.59573 12.485...	-798	136	0.022101
32	POLYGON ((-71.41607 12.39331, -71.41607 12.485...	-796	136	0.004097
33	POLYGON ((-71.2364 11.83865, -71.2364 11.93102...	-794	130	0.027188

The column Zonas_Pantanosas_Salinas contains the (fractional) area of the ecosystem in each grid cell.

Sum up the areas of each grid cell to get the total area.

if has_data:
    total_area = gdf[ecosystem_column].sum()
    display(total_area)

np.float64(1.7193436720913144)

Calculate cumulative area

3. Arrange grid cells in ascending order based on their area (smaller first). Calculate accumulated sum of area per cell (cumulative area).

if has_data:
    gdf = gdf.sort_values(by=ecosystem_column)
    gdf["cumulative_area"] = gdf[ecosystem_column].cumsum()
    display(gdf)

	geometry	grid_col	grid_row	Zonas_Pantanosas_Salinas	cumulative_area
15	POLYGON ((-72.13472 12.11584, -72.13472 12.208...	-804	133	0.000440	0.000440
19	POLYGON ((-72.04489 12.2083, -72.04489 12.3007...	-803	134	0.001848	0.002288
28	POLYGON ((-71.68556 12.2083, -71.68556 12.3007...	-799	134	0.002235	0.004523
18	POLYGON ((-72.04489 12.11584, -72.04489 12.208...	-803	133	0.003763	0.008286
32	POLYGON ((-71.41607 12.39331, -71.41607 12.485...	-796	136	0.004097	0.012382
3	POLYGON ((-72.76354 11.46949, -72.76354 11.561...	-811	126	0.005184	0.017566
21	POLYGON ((-71.95505 12.2083, -71.95505 12.3007...	-802	134	0.006185	0.023751
16	POLYGON ((-72.13472 12.2083, -72.13472 12.3007...	-804	134	0.009282	0.033033
0	POLYGON ((-72.9432 11.46949, -72.9432 11.56174...	-813	126	0.011193	0.044225
2	POLYGON ((-72.85337 11.56174, -72.85337 11.654...	-812	127	0.011454	0.055679
24	POLYGON ((-71.86522 12.30079, -71.86522 12.393...	-801	135	0.013234	0.068913
10	POLYGON ((-72.40421 11.74632, -72.40421 11.838...	-807	129	0.014153	0.083066
9	POLYGON ((-72.49404 11.74632, -72.49404 11.838...	-808	129	0.014462	0.097528
11	POLYGON ((-72.22455 11.83865, -72.22455 11.931...	-805	130	0.016290	0.113818
1	POLYGON ((-72.85337 11.46949, -72.85337 11.561...	-812	126	0.018349	0.132167
31	POLYGON ((-71.59573 12.39331, -71.59573 12.485...	-798	136	0.022101	0.154267
33	POLYGON ((-71.2364 11.83865, -71.2364 11.93102...	-794	130	0.027188	0.181455
23	POLYGON ((-71.86522 12.2083, -71.86522 12.3007...	-801	134	0.028378	0.209833
25	POLYGON ((-71.77539 12.11584, -71.77539 12.208...	-800	133	0.029527	0.239360
5	POLYGON ((-72.76354 11.65401, -72.76354 11.746...	-811	128	0.032612	0.271972
12	POLYGON ((-72.13472 11.83865, -72.13472 11.931...	-804	130	0.048033	0.320006
30	POLYGON ((-71.59573 12.30079, -71.59573 12.393...	-798	135	0.051994	0.372000
4	POLYGON ((-72.76354 11.56174, -72.76354 11.654...	-811	127	0.052679	0.424679
17	POLYGON ((-72.04489 12.02342, -72.04489 12.115...	-803	132	0.054030	0.478709
6	POLYGON ((-72.67371 11.65401, -72.67371 11.746...	-810	128	0.059683	0.538392
8	POLYGON ((-72.49404 11.65401, -72.49404 11.746...	-808	128	0.066528	0.604920
27	POLYGON ((-71.77539 12.30079, -71.77539 12.393...	-800	135	0.077685	0.682604
29	POLYGON ((-71.68556 12.30079, -71.68556 12.393...	-799	135	0.100297	0.782901
26	POLYGON ((-71.77539 12.2083, -71.77539 12.3007...	-800	134	0.106712	0.889613
20	POLYGON ((-71.95505 12.11584, -71.95505 12.208...	-802	133	0.136313	1.025927
14	POLYGON ((-72.13472 12.02342, -72.13472 12.115...	-804	132	0.143729	1.169656
7	POLYGON ((-72.58387 11.65401, -72.58387 11.746...	-809	128	0.154781	1.324437
22	POLYGON ((-71.86522 12.11584, -71.86522 12.208...	-801	133	0.178797	1.503234
13	POLYGON ((-72.13472 11.93102, -72.13472 12.023...	-804	131	0.216110	1.719344

Calculate cumulative proportion

4. Calculate cumulative proportion by dividing cumulative area by total area (cumulative proportion takes values between 0 and 1)

if has_data:
    gdf["cumulative_proportion"] = gdf["cumulative_area"] / total_area
    display(gdf)

	geometry	grid_col	grid_row	Zonas_Pantanosas_Salinas	cumulative_area	cumulative_proportion
15	POLYGON ((-72.13472 12.11584, -72.13472 12.208...	-804	133	0.000440	0.000440	0.000256
19	POLYGON ((-72.04489 12.2083, -72.04489 12.3007...	-803	134	0.001848	0.002288	0.001331
28	POLYGON ((-71.68556 12.2083, -71.68556 12.3007...	-799	134	0.002235	0.004523	0.002631
18	POLYGON ((-72.04489 12.11584, -72.04489 12.208...	-803	133	0.003763	0.008286	0.004819
32	POLYGON ((-71.41607 12.39331, -71.41607 12.485...	-796	136	0.004097	0.012382	0.007202
3	POLYGON ((-72.76354 11.46949, -72.76354 11.561...	-811	126	0.005184	0.017566	0.010217
21	POLYGON ((-71.95505 12.2083, -71.95505 12.3007...	-802	134	0.006185	0.023751	0.013814
16	POLYGON ((-72.13472 12.2083, -72.13472 12.3007...	-804	134	0.009282	0.033033	0.019212
0	POLYGON ((-72.9432 11.46949, -72.9432 11.56174...	-813	126	0.011193	0.044225	0.025722
2	POLYGON ((-72.85337 11.56174, -72.85337 11.654...	-812	127	0.011454	0.055679	0.032384
24	POLYGON ((-71.86522 12.30079, -71.86522 12.393...	-801	135	0.013234	0.068913	0.040081
10	POLYGON ((-72.40421 11.74632, -72.40421 11.838...	-807	129	0.014153	0.083066	0.048313
9	POLYGON ((-72.49404 11.74632, -72.49404 11.838...	-808	129	0.014462	0.097528	0.056724
11	POLYGON ((-72.22455 11.83865, -72.22455 11.931...	-805	130	0.016290	0.113818	0.066198
1	POLYGON ((-72.85337 11.46949, -72.85337 11.561...	-812	126	0.018349	0.132167	0.076870
31	POLYGON ((-71.59573 12.39331, -71.59573 12.485...	-798	136	0.022101	0.154267	0.089725
33	POLYGON ((-71.2364 11.83865, -71.2364 11.93102...	-794	130	0.027188	0.181455	0.105537
23	POLYGON ((-71.86522 12.2083, -71.86522 12.3007...	-801	134	0.028378	0.209833	0.122042
25	POLYGON ((-71.77539 12.11584, -71.77539 12.208...	-800	133	0.029527	0.239360	0.139216
5	POLYGON ((-72.76354 11.65401, -72.76354 11.746...	-811	128	0.032612	0.271972	0.158184
12	POLYGON ((-72.13472 11.83865, -72.13472 11.931...	-804	130	0.048033	0.320006	0.186121
30	POLYGON ((-71.59573 12.30079, -71.59573 12.393...	-798	135	0.051994	0.372000	0.216361
4	POLYGON ((-72.76354 11.56174, -72.76354 11.654...	-811	127	0.052679	0.424679	0.247001
17	POLYGON ((-72.04489 12.02342, -72.04489 12.115...	-803	132	0.054030	0.478709	0.278425
6	POLYGON ((-72.67371 11.65401, -72.67371 11.746...	-810	128	0.059683	0.538392	0.313138
8	POLYGON ((-72.49404 11.65401, -72.49404 11.746...	-808	128	0.066528	0.604920	0.351832
27	POLYGON ((-71.77539 12.30079, -71.77539 12.393...	-800	135	0.077685	0.682604	0.397014
29	POLYGON ((-71.68556 12.30079, -71.68556 12.393...	-799	135	0.100297	0.782901	0.455349
26	POLYGON ((-71.77539 12.2083, -71.77539 12.3007...	-800	134	0.106712	0.889613	0.517415
20	POLYGON ((-71.95505 12.11584, -71.95505 12.208...	-802	133	0.136313	1.025927	0.596697
14	POLYGON ((-72.13472 12.02342, -72.13472 12.115...	-804	132	0.143729	1.169656	0.680292
7	POLYGON ((-72.58387 11.65401, -72.58387 11.746...	-809	128	0.154781	1.324437	0.770316
22	POLYGON ((-71.86522 12.11584, -71.86522 12.208...	-801	133	0.178797	1.503234	0.874307
13	POLYGON ((-72.13472 11.93102, -72.13472 12.023...	-804	131	0.216110	1.719344	1.000000

Count AOO cells

5. Calculate AOO by counting the number of cells with a cumulative proportion greater than 0.01 (i.e. exclude cells that in combination account for up to 1% of the total mapped extent of the ecosystem type).

if has_data:
    aoo = len(gdf[gdf["cumulative_proportion"] > 0.01])
    print(f'AOO is {aoo} cells')

AOO is 29 cells

AOO Calculation (direct call)

if has_data:
    aoo_count = ecosystem.aoo
    print(f'AOO: {aoo_count} grid cells')

AOO: 29 grid cells

Criterion B Summary

Criterion B status (spatial)

Endangered (EN) — Zonas Pantanosas Salinas (Zonas Pantanosas Salinas), index 87

Status reflects the spatial thresholds for EOO (B1) and AOO (B2) only. A final listing under B1/B2 additionally requires at least one of: (a) an observed or inferred continuing decline; (b) threatening processes likely to cause continuing decline within 20 years; or (c) few threat-defined locations — none of which are derived from the spatial metrics.

Sub-criterion	Metric	Value	Category
B1	EOO	8355 km²	Endangered (EN)
B2	AOO	29 cells	Vulnerable (VU)
Overall B	—	—	Endangered (EN)