Criterion B Details

# Default ecosystem code for template development.
# This line is replaced by build_ecosystem_pages.py for each ecosystem.
ecosystem_code = 'Agroecosistema Papero'

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 Agroecosistema Papero 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() = 129

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 42402.3 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	Agroecosistema_Papero
0	POLYGON ((-77.70427 0.99486, -77.70427 1.08531...	-866	11	0.026580
1	POLYGON ((-77.61444 0.72351, -77.61444 0.81396...	-865	8	0.010646
2	POLYGON ((-77.61444 0.81396, -77.61444 0.90441...	-865	9	0.026314
3	POLYGON ((-77.61444 0.99486, -77.61444 1.08531...	-865	11	0.055049
4	POLYGON ((-77.61444 1.08531, -77.61444 1.17576...	-865	12	0.007856
5	POLYGON ((-77.52461 0.81396, -77.52461 0.90441...	-864	9	0.001172
6	POLYGON ((-77.52461 0.99486, -77.52461 1.08531...	-864	11	0.038173
7	POLYGON ((-77.43478 0.81396, -77.43478 0.90441...	-863	9	0.015783
8	POLYGON ((-77.43478 0.90441, -77.43478 0.99486...	-863	10	0.014637
9	POLYGON ((-77.34495 1.08531, -77.34495 1.17576...	-862	12	0.010996
10	POLYGON ((-74.29067 4.43572, -74.29067 4.52643...	-828	49	0.014283
11	POLYGON ((-74.29067 4.70788, -74.29067 4.79862...	-828	52	0.036830
12	POLYGON ((-74.29067 4.79862, -74.29067 4.88937...	-828	53	0.041434
13	POLYGON ((-74.20084 4.70788, -74.20084 4.79862...	-827	52	0.079973
14	POLYGON ((-74.20084 4.79862, -74.20084 4.88937...	-827	53	0.106018
15	POLYGON ((-74.20084 4.88937, -74.20084 4.98013...	-827	54	0.014226
16	POLYGON ((-74.11101 4.70788, -74.11101 4.79862...	-826	52	0.036026
17	POLYGON ((-74.11101 4.79862, -74.11101 4.88937...	-826	53	0.077521
18	POLYGON ((-74.11101 4.88937, -74.11101 4.98013...	-826	54	0.075013
19	POLYGON ((-74.02118 4.25434, -74.02118 4.34503...	-825	47	0.005741
20	POLYGON ((-74.02118 4.34503, -74.02118 4.43572...	-825	48	0.030949
21	POLYGON ((-74.02118 4.79862, -74.02118 4.88937...	-825	53	0.032679
22	POLYGON ((-74.02118 4.88937, -74.02118 4.98013...	-825	54	0.003224
23	POLYGON ((-74.02118 4.98013, -74.02118 5.07091...	-825	55	0.026987
24	POLYGON ((-74.02118 5.07091, -74.02118 5.1617,...	-825	56	0.016361
25	POLYGON ((-74.02118 5.1617, -74.02118 5.2525, ...	-825	57	0.035198
26	POLYGON ((-73.93135 4.34503, -73.93135 4.43572...	-824	48	0.005038
27	POLYGON ((-73.93135 4.61715, -73.93135 4.70788...	-824	51	0.002898
28	POLYGON ((-73.93135 4.70788, -73.93135 4.79862...	-824	52	0.002568
29	POLYGON ((-73.93135 5.07091, -73.93135 5.1617,...	-824	56	0.023284
30	POLYGON ((-73.93135 5.1617, -73.93135 5.2525, ...	-824	57	0.009842
31	POLYGON ((-73.93135 5.2525, -73.93135 5.34332,...	-824	58	0.003431
32	POLYGON ((-73.84152 4.70788, -73.84152 4.79862...	-823	52	0.014499
33	POLYGON ((-73.84152 4.79862, -73.84152 4.88937...	-823	53	0.011223
34	POLYGON ((-73.84152 4.88937, -73.84152 4.98013...	-823	54	0.002537
35	POLYGON ((-73.84152 4.98013, -73.84152 5.07091...	-823	55	0.002376
36	POLYGON ((-73.84152 5.07091, -73.84152 5.1617,...	-823	56	0.017428
37	POLYGON ((-73.84152 5.1617, -73.84152 5.2525, ...	-823	57	0.007356
38	POLYGON ((-73.84152 5.2525, -73.84152 5.34332,...	-823	58	0.030071
39	POLYGON ((-73.84152 5.52498, -73.84152 5.61584...	-823	61	0.007724
40	POLYGON ((-73.84152 5.61584, -73.84152 5.70671...	-823	62	0.001124
41	POLYGON ((-73.75168 4.79862, -73.75168 4.88937...	-822	53	0.005215
42	POLYGON ((-73.75168 4.88937, -73.75168 4.98013...	-822	54	0.005060
43	POLYGON ((-73.75168 4.98013, -73.75168 5.07091...	-822	55	0.016034
44	POLYGON ((-73.75168 5.07091, -73.75168 5.1617,...	-822	56	0.011231
45	POLYGON ((-73.66185 4.98013, -73.66185 5.07091...	-821	55	0.010959
46	POLYGON ((-73.66185 5.07091, -73.66185 5.1617,...	-821	56	0.008674
47	POLYGON ((-73.66185 5.61584, -73.66185 5.70671...	-821	62	0.052229
48	POLYGON ((-73.66185 5.70671, -73.66185 5.79759...	-821	63	0.046215
49	POLYGON ((-73.57202 4.98013, -73.57202 5.07091...	-820	55	0.002610
50	POLYGON ((-73.57202 5.07091, -73.57202 5.1617,...	-820	56	0.002322
51	POLYGON ((-73.57202 5.1617, -73.57202 5.2525, ...	-820	57	0.018008
52	POLYGON ((-73.57202 5.2525, -73.57202 5.34332,...	-820	58	0.003649
53	POLYGON ((-73.39236 5.1617, -73.39236 5.2525, ...	-818	57	0.007142
54	POLYGON ((-73.39236 5.43414, -73.39236 5.52498...	-818	60	0.002852
55	POLYGON ((-73.30253 5.2525, -73.30253 5.34332,...	-817	58	0.024365

The column Agroecosistema_Papero 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(1.197629193678154)

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	Agroecosistema_Papero	cumulative_area
40	POLYGON ((-73.84152 5.61584, -73.84152 5.70671...	-823	62	0.001124	0.001124
5	POLYGON ((-77.52461 0.81396, -77.52461 0.90441...	-864	9	0.001172	0.002296
50	POLYGON ((-73.57202 5.07091, -73.57202 5.1617,...	-820	56	0.002322	0.004618
35	POLYGON ((-73.84152 4.98013, -73.84152 5.07091...	-823	55	0.002376	0.006994
34	POLYGON ((-73.84152 4.88937, -73.84152 4.98013...	-823	54	0.002537	0.009531
28	POLYGON ((-73.93135 4.70788, -73.93135 4.79862...	-824	52	0.002568	0.012098
49	POLYGON ((-73.57202 4.98013, -73.57202 5.07091...	-820	55	0.002610	0.014708
54	POLYGON ((-73.39236 5.43414, -73.39236 5.52498...	-818	60	0.002852	0.017561
27	POLYGON ((-73.93135 4.61715, -73.93135 4.70788...	-824	51	0.002898	0.020458
22	POLYGON ((-74.02118 4.88937, -74.02118 4.98013...	-825	54	0.003224	0.023682
31	POLYGON ((-73.93135 5.2525, -73.93135 5.34332,...	-824	58	0.003431	0.027113
52	POLYGON ((-73.57202 5.2525, -73.57202 5.34332,...	-820	58	0.003649	0.030762
26	POLYGON ((-73.93135 4.34503, -73.93135 4.43572...	-824	48	0.005038	0.035800
42	POLYGON ((-73.75168 4.88937, -73.75168 4.98013...	-822	54	0.005060	0.040861
41	POLYGON ((-73.75168 4.79862, -73.75168 4.88937...	-822	53	0.005215	0.046075
19	POLYGON ((-74.02118 4.25434, -74.02118 4.34503...	-825	47	0.005741	0.051817
53	POLYGON ((-73.39236 5.1617, -73.39236 5.2525, ...	-818	57	0.007142	0.058959
37	POLYGON ((-73.84152 5.1617, -73.84152 5.2525, ...	-823	57	0.007356	0.066315
39	POLYGON ((-73.84152 5.52498, -73.84152 5.61584...	-823	61	0.007724	0.074040
4	POLYGON ((-77.61444 1.08531, -77.61444 1.17576...	-865	12	0.007856	0.081895
46	POLYGON ((-73.66185 5.07091, -73.66185 5.1617,...	-821	56	0.008674	0.090569
30	POLYGON ((-73.93135 5.1617, -73.93135 5.2525, ...	-824	57	0.009842	0.100411
1	POLYGON ((-77.61444 0.72351, -77.61444 0.81396...	-865	8	0.010646	0.111057
45	POLYGON ((-73.66185 4.98013, -73.66185 5.07091...	-821	55	0.010959	0.122016
9	POLYGON ((-77.34495 1.08531, -77.34495 1.17576...	-862	12	0.010996	0.133012
33	POLYGON ((-73.84152 4.79862, -73.84152 4.88937...	-823	53	0.011223	0.144235
44	POLYGON ((-73.75168 5.07091, -73.75168 5.1617,...	-822	56	0.011231	0.155466
15	POLYGON ((-74.20084 4.88937, -74.20084 4.98013...	-827	54	0.014226	0.169692
10	POLYGON ((-74.29067 4.43572, -74.29067 4.52643...	-828	49	0.014283	0.183975
32	POLYGON ((-73.84152 4.70788, -73.84152 4.79862...	-823	52	0.014499	0.198473
8	POLYGON ((-77.43478 0.90441, -77.43478 0.99486...	-863	10	0.014637	0.213110
7	POLYGON ((-77.43478 0.81396, -77.43478 0.90441...	-863	9	0.015783	0.228893
43	POLYGON ((-73.75168 4.98013, -73.75168 5.07091...	-822	55	0.016034	0.244927
24	POLYGON ((-74.02118 5.07091, -74.02118 5.1617,...	-825	56	0.016361	0.261288
36	POLYGON ((-73.84152 5.07091, -73.84152 5.1617,...	-823	56	0.017428	0.278716
51	POLYGON ((-73.57202 5.1617, -73.57202 5.2525, ...	-820	57	0.018008	0.296724
29	POLYGON ((-73.93135 5.07091, -73.93135 5.1617,...	-824	56	0.023284	0.320008
55	POLYGON ((-73.30253 5.2525, -73.30253 5.34332,...	-817	58	0.024365	0.344373
2	POLYGON ((-77.61444 0.81396, -77.61444 0.90441...	-865	9	0.026314	0.370686
0	POLYGON ((-77.70427 0.99486, -77.70427 1.08531...	-866	11	0.026580	0.397266
23	POLYGON ((-74.02118 4.98013, -74.02118 5.07091...	-825	55	0.026987	0.424253
38	POLYGON ((-73.84152 5.2525, -73.84152 5.34332,...	-823	58	0.030071	0.454323
20	POLYGON ((-74.02118 4.34503, -74.02118 4.43572...	-825	48	0.030949	0.485272
21	POLYGON ((-74.02118 4.79862, -74.02118 4.88937...	-825	53	0.032679	0.517951
25	POLYGON ((-74.02118 5.1617, -74.02118 5.2525, ...	-825	57	0.035198	0.553149
16	POLYGON ((-74.11101 4.70788, -74.11101 4.79862...	-826	52	0.036026	0.589175
11	POLYGON ((-74.29067 4.70788, -74.29067 4.79862...	-828	52	0.036830	0.626005
6	POLYGON ((-77.52461 0.99486, -77.52461 1.08531...	-864	11	0.038173	0.664178
12	POLYGON ((-74.29067 4.79862, -74.29067 4.88937...	-828	53	0.041434	0.705613
48	POLYGON ((-73.66185 5.70671, -73.66185 5.79759...	-821	63	0.046215	0.751828
47	POLYGON ((-73.66185 5.61584, -73.66185 5.70671...	-821	62	0.052229	0.804056
3	POLYGON ((-77.61444 0.99486, -77.61444 1.08531...	-865	11	0.055049	0.859105
18	POLYGON ((-74.11101 4.88937, -74.11101 4.98013...	-826	54	0.075013	0.934118
17	POLYGON ((-74.11101 4.79862, -74.11101 4.88937...	-826	53	0.077521	1.011639
13	POLYGON ((-74.20084 4.70788, -74.20084 4.79862...	-827	52	0.079973	1.091611
14	POLYGON ((-74.20084 4.79862, -74.20084 4.88937...	-827	53	0.106018	1.197629

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	Agroecosistema_Papero	cumulative_area	cumulative_proportion
40	POLYGON ((-73.84152 5.61584, -73.84152 5.70671...	-823	62	0.001124	0.001124	0.000938
5	POLYGON ((-77.52461 0.81396, -77.52461 0.90441...	-864	9	0.001172	0.002296	0.001917
50	POLYGON ((-73.57202 5.07091, -73.57202 5.1617,...	-820	56	0.002322	0.004618	0.003856
35	POLYGON ((-73.84152 4.98013, -73.84152 5.07091...	-823	55	0.002376	0.006994	0.005840
34	POLYGON ((-73.84152 4.88937, -73.84152 4.98013...	-823	54	0.002537	0.009531	0.007958
28	POLYGON ((-73.93135 4.70788, -73.93135 4.79862...	-824	52	0.002568	0.012098	0.010102
49	POLYGON ((-73.57202 4.98013, -73.57202 5.07091...	-820	55	0.002610	0.014708	0.012281
54	POLYGON ((-73.39236 5.43414, -73.39236 5.52498...	-818	60	0.002852	0.017561	0.014663
27	POLYGON ((-73.93135 4.61715, -73.93135 4.70788...	-824	51	0.002898	0.020458	0.017082
22	POLYGON ((-74.02118 4.88937, -74.02118 4.98013...	-825	54	0.003224	0.023682	0.019774
31	POLYGON ((-73.93135 5.2525, -73.93135 5.34332,...	-824	58	0.003431	0.027113	0.022639
52	POLYGON ((-73.57202 5.2525, -73.57202 5.34332,...	-820	58	0.003649	0.030762	0.025686
26	POLYGON ((-73.93135 4.34503, -73.93135 4.43572...	-824	48	0.005038	0.035800	0.029893
42	POLYGON ((-73.75168 4.88937, -73.75168 4.98013...	-822	54	0.005060	0.040861	0.034118
41	POLYGON ((-73.75168 4.79862, -73.75168 4.88937...	-822	53	0.005215	0.046075	0.038472
19	POLYGON ((-74.02118 4.25434, -74.02118 4.34503...	-825	47	0.005741	0.051817	0.043266
53	POLYGON ((-73.39236 5.1617, -73.39236 5.2525, ...	-818	57	0.007142	0.058959	0.049230
37	POLYGON ((-73.84152 5.1617, -73.84152 5.2525, ...	-823	57	0.007356	0.066315	0.055372
39	POLYGON ((-73.84152 5.52498, -73.84152 5.61584...	-823	61	0.007724	0.074040	0.061822
4	POLYGON ((-77.61444 1.08531, -77.61444 1.17576...	-865	12	0.007856	0.081895	0.068381
46	POLYGON ((-73.66185 5.07091, -73.66185 5.1617,...	-821	56	0.008674	0.090569	0.075624
30	POLYGON ((-73.93135 5.1617, -73.93135 5.2525, ...	-824	57	0.009842	0.100411	0.083842
1	POLYGON ((-77.61444 0.72351, -77.61444 0.81396...	-865	8	0.010646	0.111057	0.092731
45	POLYGON ((-73.66185 4.98013, -73.66185 5.07091...	-821	55	0.010959	0.122016	0.101882
9	POLYGON ((-77.34495 1.08531, -77.34495 1.17576...	-862	12	0.010996	0.133012	0.111063
33	POLYGON ((-73.84152 4.79862, -73.84152 4.88937...	-823	53	0.011223	0.144235	0.120434
44	POLYGON ((-73.75168 5.07091, -73.75168 5.1617,...	-822	56	0.011231	0.155466	0.129812
15	POLYGON ((-74.20084 4.88937, -74.20084 4.98013...	-827	54	0.014226	0.169692	0.141690
10	POLYGON ((-74.29067 4.43572, -74.29067 4.52643...	-828	49	0.014283	0.183975	0.153616
32	POLYGON ((-73.84152 4.70788, -73.84152 4.79862...	-823	52	0.014499	0.198473	0.165722
8	POLYGON ((-77.43478 0.90441, -77.43478 0.99486...	-863	10	0.014637	0.213110	0.177944
7	POLYGON ((-77.43478 0.81396, -77.43478 0.90441...	-863	9	0.015783	0.228893	0.191122
43	POLYGON ((-73.75168 4.98013, -73.75168 5.07091...	-822	55	0.016034	0.244927	0.204510
24	POLYGON ((-74.02118 5.07091, -74.02118 5.1617,...	-825	56	0.016361	0.261288	0.218171
36	POLYGON ((-73.84152 5.07091, -73.84152 5.1617,...	-823	56	0.017428	0.278716	0.232723
51	POLYGON ((-73.57202 5.1617, -73.57202 5.2525, ...	-820	57	0.018008	0.296724	0.247760
29	POLYGON ((-73.93135 5.07091, -73.93135 5.1617,...	-824	56	0.023284	0.320008	0.267201
55	POLYGON ((-73.30253 5.2525, -73.30253 5.34332,...	-817	58	0.024365	0.344373	0.287545
2	POLYGON ((-77.61444 0.81396, -77.61444 0.90441...	-865	9	0.026314	0.370686	0.309517
0	POLYGON ((-77.70427 0.99486, -77.70427 1.08531...	-866	11	0.026580	0.397266	0.331710
23	POLYGON ((-74.02118 4.98013, -74.02118 5.07091...	-825	55	0.026987	0.424253	0.354244
38	POLYGON ((-73.84152 5.2525, -73.84152 5.34332,...	-823	58	0.030071	0.454323	0.379352
20	POLYGON ((-74.02118 4.34503, -74.02118 4.43572...	-825	48	0.030949	0.485272	0.405194
21	POLYGON ((-74.02118 4.79862, -74.02118 4.88937...	-825	53	0.032679	0.517951	0.432480
25	POLYGON ((-74.02118 5.1617, -74.02118 5.2525, ...	-825	57	0.035198	0.553149	0.461870
16	POLYGON ((-74.11101 4.70788, -74.11101 4.79862...	-826	52	0.036026	0.589175	0.491951
11	POLYGON ((-74.29067 4.70788, -74.29067 4.79862...	-828	52	0.036830	0.626005	0.522704
6	POLYGON ((-77.52461 0.99486, -77.52461 1.08531...	-864	11	0.038173	0.664178	0.554578
12	POLYGON ((-74.29067 4.79862, -74.29067 4.88937...	-828	53	0.041434	0.705613	0.589175
48	POLYGON ((-73.66185 5.70671, -73.66185 5.79759...	-821	63	0.046215	0.751828	0.627763
47	POLYGON ((-73.66185 5.61584, -73.66185 5.70671...	-821	62	0.052229	0.804056	0.671373
3	POLYGON ((-77.61444 0.99486, -77.61444 1.08531...	-865	11	0.055049	0.859105	0.717338
18	POLYGON ((-74.11101 4.88937, -74.11101 4.98013...	-826	54	0.075013	0.934118	0.779972
17	POLYGON ((-74.11101 4.79862, -74.11101 4.88937...	-826	53	0.077521	1.011639	0.844701
13	POLYGON ((-74.20084 4.70788, -74.20084 4.79862...	-827	52	0.079973	1.091611	0.911477
14	POLYGON ((-74.20084 4.79862, -74.20084 4.88937...	-827	53	0.106018	1.197629	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 51 cells

AOO Calculation (direct call)

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

AOO: 51 grid cells

Criterion B Summary

Criterion B status (spatial)

Vulnerable (VU) — Agroecosistema Papero (Agroecosistema Papero), index 13

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	42402 km²	Vulnerable (VU)
B2	AOO	51 cells	Least Concern (LC)
Overall B	—	—	Vulnerable (VU)