Criterion B Details

# Default ecosystem code for template development.
# This line is replaced by build_ecosystem_pages.py for each ecosystem.
ecosystem_code = 'Glaciares y Nivales'

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 Glaciares y Nivales 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() = 226

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 229329.9 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:

Intersect AOO grid with the ecosystem’s distribution map.

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

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

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

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

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

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	Glaciares_y_Nivales
0	POLYGON ((-77.88394 0.81396, -77.88394 0.90441...	-868	9	0.016181
1	POLYGON ((-77.88394 0.90441, -77.88394 0.99486...	-868	10	0.063159
2	POLYGON ((-77.7941 0.90441, -77.7941 0.99486, ...	-867	10	0.015660
3	POLYGON ((-77.34495 1.17576, -77.34495 1.26622...	-862	13	0.015854
4	POLYGON ((-76.3568 2.2615, -76.3568 2.352, -76...	-851	25	0.093585
5	POLYGON ((-76.26697 2.2615, -76.26697 2.352, -...	-850	25	0.001499
6	POLYGON ((-75.99747 2.80464, -75.99747 2.89518...	-847	31	0.006715
7	POLYGON ((-75.99747 2.89518, -75.99747 2.98573...	-847	32	0.227638
8	POLYGON ((-75.45848 4.70788, -75.45848 4.79862...	-841	52	0.009875
9	POLYGON ((-75.45848 4.79862, -75.45848 4.88937...	-841	53	0.000953
10	POLYGON ((-75.36865 4.61715, -75.36865 4.70788...	-840	51	0.017305
11	POLYGON ((-75.36865 4.70788, -75.36865 4.79862...	-840	52	0.071388
12	POLYGON ((-75.36865 4.79862, -75.36865 4.88937...	-840	53	0.033613
13	POLYGON ((-75.36865 4.88937, -75.36865 4.98013...	-840	54	0.000431
14	POLYGON ((-75.27882 4.61715, -75.27882 4.70788...	-839	51	0.089579
15	POLYGON ((-75.27882 4.70788, -75.27882 4.79862...	-839	52	0.020462
16	POLYGON ((-75.27882 4.79862, -75.27882 4.88937...	-839	53	0.365401
17	POLYGON ((-75.27882 4.88937, -75.27882 4.98013...	-839	54	0.320805
18	POLYGON ((-73.75168 10.73256, -73.75168 10.824...	-822	118	0.045990
19	POLYGON ((-73.75168 10.82458, -73.75168 10.916...	-822	119	0.107430
20	POLYGON ((-73.66185 10.64057, -73.66185 10.732...	-821	117	0.018139
21	POLYGON ((-73.66185 10.73256, -73.66185 10.824...	-821	118	0.156456
22	POLYGON ((-73.66185 10.82458, -73.66185 10.916...	-821	119	0.407071
23	POLYGON ((-73.57202 10.64057, -73.57202 10.732...	-820	117	0.112249
24	POLYGON ((-73.57202 10.73256, -73.57202 10.824...	-820	118	0.426802
25	POLYGON ((-73.57202 10.82458, -73.57202 10.916...	-820	119	0.177805
26	POLYGON ((-73.48219 10.64057, -73.48219 10.732...	-819	117	0.013964
27	POLYGON ((-73.48219 10.73256, -73.48219 10.824...	-819	118	0.154408
28	POLYGON ((-73.48219 10.82458, -73.48219 10.916...	-819	119	0.033835
29	POLYGON ((-72.76354 6.88936, -72.76354 6.98044...	-811	76	0.001269
30	POLYGON ((-72.67371 6.88936, -72.67371 6.98044...	-810	76	0.098901
31	POLYGON ((-72.67371 6.98044, -72.67371 7.07154...	-810	77	0.043742
32	POLYGON ((-72.49404 6.16126, -72.49404 6.25222...	-808	68	0.003361
33	POLYGON ((-72.49404 6.25222, -72.49404 6.34319...	-808	69	0.007396
34	POLYGON ((-72.40421 6.16126, -72.40421 6.25222...	-807	68	0.002056
35	POLYGON ((-72.40421 6.25222, -72.40421 6.34319...	-807	69	0.031990
36	POLYGON ((-72.40421 6.34319, -72.40421 6.43418...	-807	70	0.003817
37	POLYGON ((-72.40421 6.52518, -72.40421 6.6162,...	-807	72	0.006836
38	POLYGON ((-72.40421 6.6162, -72.40421 6.70724,...	-807	73	0.000765
39	POLYGON ((-72.31438 6.16126, -72.31438 6.25222...	-806	68	0.016662
40	POLYGON ((-72.31438 6.25222, -72.31438 6.34319...	-806	69	0.019547
41	POLYGON ((-72.31438 6.34319, -72.31438 6.43418...	-806	70	0.028371
42	POLYGON ((-72.31438 6.43418, -72.31438 6.52518...	-806	71	0.078572
43	POLYGON ((-72.31438 6.52518, -72.31438 6.6162,...	-806	72	0.100837
44	POLYGON ((-72.31438 6.6162, -72.31438 6.70724,...	-806	73	0.191504
45	POLYGON ((-72.31438 6.70724, -72.31438 6.79829...	-806	74	0.033692
46	POLYGON ((-72.22455 6.25222, -72.22455 6.34319...	-805	69	0.027227
47	POLYGON ((-72.22455 6.34319, -72.22455 6.43418...	-805	70	0.494419
48	POLYGON ((-72.22455 6.43418, -72.22455 6.52518...	-805	71	0.548524
49	POLYGON ((-72.22455 6.52518, -72.22455 6.6162,...	-805	72	0.159196
50	POLYGON ((-72.22455 6.6162, -72.22455 6.70724,...	-805	73	0.015027
51	POLYGON ((-72.13472 6.34319, -72.13472 6.43418...	-804	70	0.008441
52	POLYGON ((-72.13472 6.43418, -72.13472 6.52518...	-804	71	0.159585
53	POLYGON ((-72.13472 6.52518, -72.13472 6.6162,...	-804	72	0.018963
54	POLYGON ((-72.04489 6.43418, -72.04489 6.52518...	-803	71	0.064992
55	POLYGON ((-72.04489 6.52518, -72.04489 6.6162,...	-803	72	0.021611

The column Glaciares_y_Nivales 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(5.211551879519746)

Calculate cumulative area

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	Glaciares_y_Nivales	cumulative_area
13	POLYGON ((-75.36865 4.88937, -75.36865 4.98013...	-840	54	0.000431	0.000431
38	POLYGON ((-72.40421 6.6162, -72.40421 6.70724,...	-807	73	0.000765	0.001196
9	POLYGON ((-75.45848 4.79862, -75.45848 4.88937...	-841	53	0.000953	0.002149
29	POLYGON ((-72.76354 6.88936, -72.76354 6.98044...	-811	76	0.001269	0.003418
5	POLYGON ((-76.26697 2.2615, -76.26697 2.352, -...	-850	25	0.001499	0.004916
34	POLYGON ((-72.40421 6.16126, -72.40421 6.25222...	-807	68	0.002056	0.006972
32	POLYGON ((-72.49404 6.16126, -72.49404 6.25222...	-808	68	0.003361	0.010333
36	POLYGON ((-72.40421 6.34319, -72.40421 6.43418...	-807	70	0.003817	0.014150
6	POLYGON ((-75.99747 2.80464, -75.99747 2.89518...	-847	31	0.006715	0.020865
37	POLYGON ((-72.40421 6.52518, -72.40421 6.6162,...	-807	72	0.006836	0.027701
33	POLYGON ((-72.49404 6.25222, -72.49404 6.34319...	-808	69	0.007396	0.035097
51	POLYGON ((-72.13472 6.34319, -72.13472 6.43418...	-804	70	0.008441	0.043538
8	POLYGON ((-75.45848 4.70788, -75.45848 4.79862...	-841	52	0.009875	0.053412
26	POLYGON ((-73.48219 10.64057, -73.48219 10.732...	-819	117	0.013964	0.067376
50	POLYGON ((-72.22455 6.6162, -72.22455 6.70724,...	-805	73	0.015027	0.082403
2	POLYGON ((-77.7941 0.90441, -77.7941 0.99486, ...	-867	10	0.015660	0.098063
3	POLYGON ((-77.34495 1.17576, -77.34495 1.26622...	-862	13	0.015854	0.113916
0	POLYGON ((-77.88394 0.81396, -77.88394 0.90441...	-868	9	0.016181	0.130097
39	POLYGON ((-72.31438 6.16126, -72.31438 6.25222...	-806	68	0.016662	0.146759
10	POLYGON ((-75.36865 4.61715, -75.36865 4.70788...	-840	51	0.017305	0.164065
20	POLYGON ((-73.66185 10.64057, -73.66185 10.732...	-821	117	0.018139	0.182204
53	POLYGON ((-72.13472 6.52518, -72.13472 6.6162,...	-804	72	0.018963	0.201167
40	POLYGON ((-72.31438 6.25222, -72.31438 6.34319...	-806	69	0.019547	0.220714
15	POLYGON ((-75.27882 4.70788, -75.27882 4.79862...	-839	52	0.020462	0.241176
55	POLYGON ((-72.04489 6.52518, -72.04489 6.6162,...	-803	72	0.021611	0.262786
46	POLYGON ((-72.22455 6.25222, -72.22455 6.34319...	-805	69	0.027227	0.290013
41	POLYGON ((-72.31438 6.34319, -72.31438 6.43418...	-806	70	0.028371	0.318385
35	POLYGON ((-72.40421 6.25222, -72.40421 6.34319...	-807	69	0.031990	0.350375
12	POLYGON ((-75.36865 4.79862, -75.36865 4.88937...	-840	53	0.033613	0.383987
45	POLYGON ((-72.31438 6.70724, -72.31438 6.79829...	-806	74	0.033692	0.417679
28	POLYGON ((-73.48219 10.82458, -73.48219 10.916...	-819	119	0.033835	0.451515
31	POLYGON ((-72.67371 6.98044, -72.67371 7.07154...	-810	77	0.043742	0.495257
18	POLYGON ((-73.75168 10.73256, -73.75168 10.824...	-822	118	0.045990	0.541246
1	POLYGON ((-77.88394 0.90441, -77.88394 0.99486...	-868	10	0.063159	0.604405
54	POLYGON ((-72.04489 6.43418, -72.04489 6.52518...	-803	71	0.064992	0.669397
11	POLYGON ((-75.36865 4.70788, -75.36865 4.79862...	-840	52	0.071388	0.740785
42	POLYGON ((-72.31438 6.43418, -72.31438 6.52518...	-806	71	0.078572	0.819357
14	POLYGON ((-75.27882 4.61715, -75.27882 4.70788...	-839	51	0.089579	0.908936
4	POLYGON ((-76.3568 2.2615, -76.3568 2.352, -76...	-851	25	0.093585	1.002521
30	POLYGON ((-72.67371 6.88936, -72.67371 6.98044...	-810	76	0.098901	1.101422
43	POLYGON ((-72.31438 6.52518, -72.31438 6.6162,...	-806	72	0.100837	1.202260
19	POLYGON ((-73.75168 10.82458, -73.75168 10.916...	-822	119	0.107430	1.309689
23	POLYGON ((-73.57202 10.64057, -73.57202 10.732...	-820	117	0.112249	1.421938
27	POLYGON ((-73.48219 10.73256, -73.48219 10.824...	-819	118	0.154408	1.576346
21	POLYGON ((-73.66185 10.73256, -73.66185 10.824...	-821	118	0.156456	1.732802
49	POLYGON ((-72.22455 6.52518, -72.22455 6.6162,...	-805	72	0.159196	1.891997
52	POLYGON ((-72.13472 6.43418, -72.13472 6.52518...	-804	71	0.159585	2.051582
25	POLYGON ((-73.57202 10.82458, -73.57202 10.916...	-820	119	0.177805	2.229387
44	POLYGON ((-72.31438 6.6162, -72.31438 6.70724,...	-806	73	0.191504	2.420891
7	POLYGON ((-75.99747 2.89518, -75.99747 2.98573...	-847	32	0.227638	2.648529
17	POLYGON ((-75.27882 4.88937, -75.27882 4.98013...	-839	54	0.320805	2.969335
16	POLYGON ((-75.27882 4.79862, -75.27882 4.88937...	-839	53	0.365401	3.334736
22	POLYGON ((-73.66185 10.82458, -73.66185 10.916...	-821	119	0.407071	3.741807
24	POLYGON ((-73.57202 10.73256, -73.57202 10.824...	-820	118	0.426802	4.168609
47	POLYGON ((-72.22455 6.34319, -72.22455 6.43418...	-805	70	0.494419	4.663028
48	POLYGON ((-72.22455 6.43418, -72.22455 6.52518...	-805	71	0.548524	5.211552

Calculate cumulative proportion

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	Glaciares_y_Nivales	cumulative_area	cumulative_proportion
13	POLYGON ((-75.36865 4.88937, -75.36865 4.98013...	-840	54	0.000431	0.000431	0.000083
38	POLYGON ((-72.40421 6.6162, -72.40421 6.70724,...	-807	73	0.000765	0.001196	0.000230
9	POLYGON ((-75.45848 4.79862, -75.45848 4.88937...	-841	53	0.000953	0.002149	0.000412
29	POLYGON ((-72.76354 6.88936, -72.76354 6.98044...	-811	76	0.001269	0.003418	0.000656
5	POLYGON ((-76.26697 2.2615, -76.26697 2.352, -...	-850	25	0.001499	0.004916	0.000943
34	POLYGON ((-72.40421 6.16126, -72.40421 6.25222...	-807	68	0.002056	0.006972	0.001338
32	POLYGON ((-72.49404 6.16126, -72.49404 6.25222...	-808	68	0.003361	0.010333	0.001983
36	POLYGON ((-72.40421 6.34319, -72.40421 6.43418...	-807	70	0.003817	0.014150	0.002715
6	POLYGON ((-75.99747 2.80464, -75.99747 2.89518...	-847	31	0.006715	0.020865	0.004004
37	POLYGON ((-72.40421 6.52518, -72.40421 6.6162,...	-807	72	0.006836	0.027701	0.005315
33	POLYGON ((-72.49404 6.25222, -72.49404 6.34319...	-808	69	0.007396	0.035097	0.006734
51	POLYGON ((-72.13472 6.34319, -72.13472 6.43418...	-804	70	0.008441	0.043538	0.008354
8	POLYGON ((-75.45848 4.70788, -75.45848 4.79862...	-841	52	0.009875	0.053412	0.010249
26	POLYGON ((-73.48219 10.64057, -73.48219 10.732...	-819	117	0.013964	0.067376	0.012928
50	POLYGON ((-72.22455 6.6162, -72.22455 6.70724,...	-805	73	0.015027	0.082403	0.015812
2	POLYGON ((-77.7941 0.90441, -77.7941 0.99486, ...	-867	10	0.015660	0.098063	0.018816
3	POLYGON ((-77.34495 1.17576, -77.34495 1.26622...	-862	13	0.015854	0.113916	0.021858
0	POLYGON ((-77.88394 0.81396, -77.88394 0.90441...	-868	9	0.016181	0.130097	0.024963
39	POLYGON ((-72.31438 6.16126, -72.31438 6.25222...	-806	68	0.016662	0.146759	0.028160
10	POLYGON ((-75.36865 4.61715, -75.36865 4.70788...	-840	51	0.017305	0.164065	0.031481
20	POLYGON ((-73.66185 10.64057, -73.66185 10.732...	-821	117	0.018139	0.182204	0.034962
53	POLYGON ((-72.13472 6.52518, -72.13472 6.6162,...	-804	72	0.018963	0.201167	0.038600
40	POLYGON ((-72.31438 6.25222, -72.31438 6.34319...	-806	69	0.019547	0.220714	0.042351
15	POLYGON ((-75.27882 4.70788, -75.27882 4.79862...	-839	52	0.020462	0.241176	0.046277
55	POLYGON ((-72.04489 6.52518, -72.04489 6.6162,...	-803	72	0.021611	0.262786	0.050424
46	POLYGON ((-72.22455 6.25222, -72.22455 6.34319...	-805	69	0.027227	0.290013	0.055648
41	POLYGON ((-72.31438 6.34319, -72.31438 6.43418...	-806	70	0.028371	0.318385	0.061092
35	POLYGON ((-72.40421 6.25222, -72.40421 6.34319...	-807	69	0.031990	0.350375	0.067230
12	POLYGON ((-75.36865 4.79862, -75.36865 4.88937...	-840	53	0.033613	0.383987	0.073680
45	POLYGON ((-72.31438 6.70724, -72.31438 6.79829...	-806	74	0.033692	0.417679	0.080145
28	POLYGON ((-73.48219 10.82458, -73.48219 10.916...	-819	119	0.033835	0.451515	0.086637
31	POLYGON ((-72.67371 6.98044, -72.67371 7.07154...	-810	77	0.043742	0.495257	0.095031
18	POLYGON ((-73.75168 10.73256, -73.75168 10.824...	-822	118	0.045990	0.541246	0.103855
1	POLYGON ((-77.88394 0.90441, -77.88394 0.99486...	-868	10	0.063159	0.604405	0.115974
54	POLYGON ((-72.04489 6.43418, -72.04489 6.52518...	-803	71	0.064992	0.669397	0.128445
11	POLYGON ((-75.36865 4.70788, -75.36865 4.79862...	-840	52	0.071388	0.740785	0.142143
42	POLYGON ((-72.31438 6.43418, -72.31438 6.52518...	-806	71	0.078572	0.819357	0.157219
14	POLYGON ((-75.27882 4.61715, -75.27882 4.70788...	-839	51	0.089579	0.908936	0.174408
4	POLYGON ((-76.3568 2.2615, -76.3568 2.352, -76...	-851	25	0.093585	1.002521	0.192365
30	POLYGON ((-72.67371 6.88936, -72.67371 6.98044...	-810	76	0.098901	1.101422	0.211342
43	POLYGON ((-72.31438 6.52518, -72.31438 6.6162,...	-806	72	0.100837	1.202260	0.230691
19	POLYGON ((-73.75168 10.82458, -73.75168 10.916...	-822	119	0.107430	1.309689	0.251305
23	POLYGON ((-73.57202 10.64057, -73.57202 10.732...	-820	117	0.112249	1.421938	0.272843
27	POLYGON ((-73.48219 10.73256, -73.48219 10.824...	-819	118	0.154408	1.576346	0.302471
21	POLYGON ((-73.66185 10.73256, -73.66185 10.824...	-821	118	0.156456	1.732802	0.332492
49	POLYGON ((-72.22455 6.52518, -72.22455 6.6162,...	-805	72	0.159196	1.891997	0.363039
52	POLYGON ((-72.13472 6.43418, -72.13472 6.52518...	-804	71	0.159585	2.051582	0.393660
25	POLYGON ((-73.57202 10.82458, -73.57202 10.916...	-820	119	0.177805	2.229387	0.427778
44	POLYGON ((-72.31438 6.6162, -72.31438 6.70724,...	-806	73	0.191504	2.420891	0.464524
7	POLYGON ((-75.99747 2.89518, -75.99747 2.98573...	-847	32	0.227638	2.648529	0.508204
17	POLYGON ((-75.27882 4.88937, -75.27882 4.98013...	-839	54	0.320805	2.969335	0.569760
16	POLYGON ((-75.27882 4.79862, -75.27882 4.88937...	-839	53	0.365401	3.334736	0.639874
22	POLYGON ((-73.66185 10.82458, -73.66185 10.916...	-821	119	0.407071	3.741807	0.717983
24	POLYGON ((-73.57202 10.73256, -73.57202 10.824...	-820	118	0.426802	4.168609	0.799879
47	POLYGON ((-72.22455 6.34319, -72.22455 6.43418...	-805	70	0.494419	4.663028	0.894748
48	POLYGON ((-72.22455 6.43418, -72.22455 6.52518...	-805	71	0.548524	5.211552	1.000000

Count AOO cells

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 44 cells

AOO Calculation (direct call)

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

AOO: 44 grid cells

Criterion B Summary

Criterion B status (spatial)

Vulnerable (VU) — Glaciares y Nivales (Glaciares y Nivales), index 49

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	229330 km²	Least Concern (LC)
B2	AOO	44 cells	Vulnerable (VU)
Overall B	—	—	Vulnerable (VU)