Criterion B Details

# Default ecosystem code for template development.
# This line is replaced by build_ecosystem_pages.py for each ecosystem.
ecosystem_code = 'Bosque Inundable Subandino'

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 Bosque Inundable Subandino 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() = 68

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 319641.6 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:

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	Bosque_Inundable_Subandino
0	POLYGON ((-77.16528 8.07488, -77.16528 8.16621...	-860	89	0.003415
1	POLYGON ((-77.07545 0.63307, -77.07545 0.72351...	-859	7	0.027903
2	POLYGON ((-77.07545 0.72351, -77.07545 0.81396...	-859	8	0.017704
3	POLYGON ((-77.07545 0.81396, -77.07545 0.90441...	-859	9	0.001126
4	POLYGON ((-76.98562 0.63307, -76.98562 0.72351...	-858	7	0.002668
5	POLYGON ((-76.62629 0.99486, -76.62629 1.08531...	-854	11	0.034231
6	POLYGON ((-76.62629 4.073, -76.62629 4.16367, ...	-854	45	0.023179
7	POLYGON ((-76.53646 4.073, -76.53646 4.16367, ...	-853	45	0.000013
8	POLYGON ((-76.53646 4.70788, -76.53646 4.79862...	-853	52	0.010988
9	POLYGON ((-76.53646 6.6162, -76.53646 6.70724,...	-853	73	0.055664
10	POLYGON ((-76.53646 6.70724, -76.53646 6.79829...	-853	74	0.020277
11	POLYGON ((-76.44663 6.6162, -76.44663 6.70724,...	-852	73	0.070227
12	POLYGON ((-76.44663 6.70724, -76.44663 6.79829...	-852	74	0.115757
13	POLYGON ((-76.3568 1.44714, -76.3568 1.53761, ...	-851	16	0.014531
14	POLYGON ((-76.26697 1.44714, -76.26697 1.53761...	-850	16	0.011955
15	POLYGON ((-76.26697 6.52518, -76.26697 6.6162,...	-850	72	0.000652
16	POLYGON ((-76.26697 6.6162, -76.26697 6.70724,...	-850	73	0.006363
17	POLYGON ((-76.17714 5.52498, -76.17714 5.61584...	-849	61	0.000492
18	POLYGON ((-76.17714 5.61584, -76.17714 5.70671...	-849	62	0.021376
19	POLYGON ((-76.17714 5.70671, -76.17714 5.79759...	-849	63	0.024700
20	POLYGON ((-76.17714 5.79759, -76.17714 5.88849...	-849	64	0.007110
21	POLYGON ((-76.17714 6.43418, -76.17714 6.52518...	-849	71	0.001240
22	POLYGON ((-76.17714 6.52518, -76.17714 6.6162,...	-849	72	0.002172
23	POLYGON ((-76.17714 6.6162, -76.17714 6.70724,...	-849	73	0.008999
24	POLYGON ((-76.17714 6.70724, -76.17714 6.79829...	-849	74	0.016014
25	POLYGON ((-76.17714 6.79829, -76.17714 6.88936...	-849	75	0.008811
26	POLYGON ((-76.17714 7.16266, -76.17714 7.2538,...	-849	79	0.007957
27	POLYGON ((-76.0873 6.6162, -76.0873 6.70724, -...	-848	73	0.001796
28	POLYGON ((-76.0873 6.70724, -76.0873 6.79829, ...	-848	74	0.004334
29	POLYGON ((-75.99747 1.26622, -75.99747 1.35668...	-847	14	0.004852
30	POLYGON ((-75.99747 5.79759, -75.99747 5.88849...	-847	64	0.005028
31	POLYGON ((-75.99747 5.88849, -75.99747 5.9794,...	-847	65	0.002545
32	POLYGON ((-75.99747 6.70724, -75.99747 6.79829...	-847	74	0.008857
33	POLYGON ((-74.91949 6.88936, -74.91949 6.98044...	-835	76	0.003907
34	POLYGON ((-74.20084 7.70977, -74.20084 7.80102...	-827	85	0.004770
35	POLYGON ((-74.20084 7.80102, -74.20084 7.89229...	-827	86	0.001045
36	POLYGON ((-74.02118 3.16686, -74.02118 3.25744...	-825	35	0.014095
37	POLYGON ((-73.93135 2.80464, -73.93135 2.89518...	-824	31	0.011688
38	POLYGON ((-73.93135 2.89518, -73.93135 2.98573...	-824	32	0.002010
39	POLYGON ((-73.84152 2.80464, -73.84152 2.89518...	-823	31	0.046898
40	POLYGON ((-73.84152 2.89518, -73.84152 2.98573...	-823	32	0.027811
41	POLYGON ((-72.76354 7.61854, -72.76354 7.70977...	-811	84	0.016068
42	POLYGON ((-72.13472 6.88936, -72.13472 6.98044...	-804	76	0.012582
43	POLYGON ((-72.04489 6.25222, -72.04489 6.34319...	-803	69	0.001378
44	POLYGON ((-72.04489 6.88936, -72.04489 6.98044...	-803	76	0.011890
45	POLYGON ((-72.04489 7.07154, -72.04489 7.16266...	-803	78	0.022860
46	POLYGON ((-71.95505 6.16126, -71.95505 6.25222...	-802	68	0.001702
47	POLYGON ((-71.95505 6.25222, -71.95505 6.34319...	-802	69	0.010130
48	POLYGON ((-71.86522 6.34319, -71.86522 6.43418...	-801	70	0.002928

The column Bosque_Inundable_Subandino 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(0.7346963693236319)

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	Bosque_Inundable_Subandino	cumulative_area
7	POLYGON ((-76.53646 4.073, -76.53646 4.16367, ...	-853	45	0.000013	0.000013
17	POLYGON ((-76.17714 5.52498, -76.17714 5.61584...	-849	61	0.000492	0.000505
15	POLYGON ((-76.26697 6.52518, -76.26697 6.6162,...	-850	72	0.000652	0.001157
35	POLYGON ((-74.20084 7.80102, -74.20084 7.89229...	-827	86	0.001045	0.002202
3	POLYGON ((-77.07545 0.81396, -77.07545 0.90441...	-859	9	0.001126	0.003329
21	POLYGON ((-76.17714 6.43418, -76.17714 6.52518...	-849	71	0.001240	0.004568
43	POLYGON ((-72.04489 6.25222, -72.04489 6.34319...	-803	69	0.001378	0.005946
46	POLYGON ((-71.95505 6.16126, -71.95505 6.25222...	-802	68	0.001702	0.007648
27	POLYGON ((-76.0873 6.6162, -76.0873 6.70724, -...	-848	73	0.001796	0.009444
38	POLYGON ((-73.93135 2.89518, -73.93135 2.98573...	-824	32	0.002010	0.011454
22	POLYGON ((-76.17714 6.52518, -76.17714 6.6162,...	-849	72	0.002172	0.013626
31	POLYGON ((-75.99747 5.88849, -75.99747 5.9794,...	-847	65	0.002545	0.016170
4	POLYGON ((-76.98562 0.63307, -76.98562 0.72351...	-858	7	0.002668	0.018838
48	POLYGON ((-71.86522 6.34319, -71.86522 6.43418...	-801	70	0.002928	0.021767
0	POLYGON ((-77.16528 8.07488, -77.16528 8.16621...	-860	89	0.003415	0.025182
33	POLYGON ((-74.91949 6.88936, -74.91949 6.98044...	-835	76	0.003907	0.029088
28	POLYGON ((-76.0873 6.70724, -76.0873 6.79829, ...	-848	74	0.004334	0.033422
34	POLYGON ((-74.20084 7.70977, -74.20084 7.80102...	-827	85	0.004770	0.038192
29	POLYGON ((-75.99747 1.26622, -75.99747 1.35668...	-847	14	0.004852	0.043044
30	POLYGON ((-75.99747 5.79759, -75.99747 5.88849...	-847	64	0.005028	0.048072
16	POLYGON ((-76.26697 6.6162, -76.26697 6.70724,...	-850	73	0.006363	0.054435
20	POLYGON ((-76.17714 5.79759, -76.17714 5.88849...	-849	64	0.007110	0.061545
26	POLYGON ((-76.17714 7.16266, -76.17714 7.2538,...	-849	79	0.007957	0.069502
25	POLYGON ((-76.17714 6.79829, -76.17714 6.88936...	-849	75	0.008811	0.078313
32	POLYGON ((-75.99747 6.70724, -75.99747 6.79829...	-847	74	0.008857	0.087170
23	POLYGON ((-76.17714 6.6162, -76.17714 6.70724,...	-849	73	0.008999	0.096169
47	POLYGON ((-71.95505 6.25222, -71.95505 6.34319...	-802	69	0.010130	0.106299
8	POLYGON ((-76.53646 4.70788, -76.53646 4.79862...	-853	52	0.010988	0.117287
37	POLYGON ((-73.93135 2.80464, -73.93135 2.89518...	-824	31	0.011688	0.128975
44	POLYGON ((-72.04489 6.88936, -72.04489 6.98044...	-803	76	0.011890	0.140865
14	POLYGON ((-76.26697 1.44714, -76.26697 1.53761...	-850	16	0.011955	0.152820
42	POLYGON ((-72.13472 6.88936, -72.13472 6.98044...	-804	76	0.012582	0.165402
36	POLYGON ((-74.02118 3.16686, -74.02118 3.25744...	-825	35	0.014095	0.179497
13	POLYGON ((-76.3568 1.44714, -76.3568 1.53761, ...	-851	16	0.014531	0.194028
24	POLYGON ((-76.17714 6.70724, -76.17714 6.79829...	-849	74	0.016014	0.210042
41	POLYGON ((-72.76354 7.61854, -72.76354 7.70977...	-811	84	0.016068	0.226110
2	POLYGON ((-77.07545 0.72351, -77.07545 0.81396...	-859	8	0.017704	0.243814
10	POLYGON ((-76.53646 6.70724, -76.53646 6.79829...	-853	74	0.020277	0.264092
18	POLYGON ((-76.17714 5.61584, -76.17714 5.70671...	-849	62	0.021376	0.285468
45	POLYGON ((-72.04489 7.07154, -72.04489 7.16266...	-803	78	0.022860	0.308328
6	POLYGON ((-76.62629 4.073, -76.62629 4.16367, ...	-854	45	0.023179	0.331507
19	POLYGON ((-76.17714 5.70671, -76.17714 5.79759...	-849	63	0.024700	0.356206
40	POLYGON ((-73.84152 2.89518, -73.84152 2.98573...	-823	32	0.027811	0.384017
1	POLYGON ((-77.07545 0.63307, -77.07545 0.72351...	-859	7	0.027903	0.411920
5	POLYGON ((-76.62629 0.99486, -76.62629 1.08531...	-854	11	0.034231	0.446151
39	POLYGON ((-73.84152 2.80464, -73.84152 2.89518...	-823	31	0.046898	0.493049
9	POLYGON ((-76.53646 6.6162, -76.53646 6.70724,...	-853	73	0.055664	0.548713
11	POLYGON ((-76.44663 6.6162, -76.44663 6.70724,...	-852	73	0.070227	0.618940
12	POLYGON ((-76.44663 6.70724, -76.44663 6.79829...	-852	74	0.115757	0.734696

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	Bosque_Inundable_Subandino	cumulative_area	cumulative_proportion
7	POLYGON ((-76.53646 4.073, -76.53646 4.16367, ...	-853	45	0.000013	0.000013	0.000018
17	POLYGON ((-76.17714 5.52498, -76.17714 5.61584...	-849	61	0.000492	0.000505	0.000687
15	POLYGON ((-76.26697 6.52518, -76.26697 6.6162,...	-850	72	0.000652	0.001157	0.001575
35	POLYGON ((-74.20084 7.80102, -74.20084 7.89229...	-827	86	0.001045	0.002202	0.002998
3	POLYGON ((-77.07545 0.81396, -77.07545 0.90441...	-859	9	0.001126	0.003329	0.004531
21	POLYGON ((-76.17714 6.43418, -76.17714 6.52518...	-849	71	0.001240	0.004568	0.006218
43	POLYGON ((-72.04489 6.25222, -72.04489 6.34319...	-803	69	0.001378	0.005946	0.008094
46	POLYGON ((-71.95505 6.16126, -71.95505 6.25222...	-802	68	0.001702	0.007648	0.010410
27	POLYGON ((-76.0873 6.6162, -76.0873 6.70724, -...	-848	73	0.001796	0.009444	0.012855
38	POLYGON ((-73.93135 2.89518, -73.93135 2.98573...	-824	32	0.002010	0.011454	0.015590
22	POLYGON ((-76.17714 6.52518, -76.17714 6.6162,...	-849	72	0.002172	0.013626	0.018546
31	POLYGON ((-75.99747 5.88849, -75.99747 5.9794,...	-847	65	0.002545	0.016170	0.022010
4	POLYGON ((-76.98562 0.63307, -76.98562 0.72351...	-858	7	0.002668	0.018838	0.025641
48	POLYGON ((-71.86522 6.34319, -71.86522 6.43418...	-801	70	0.002928	0.021767	0.029627
0	POLYGON ((-77.16528 8.07488, -77.16528 8.16621...	-860	89	0.003415	0.025182	0.034275
33	POLYGON ((-74.91949 6.88936, -74.91949 6.98044...	-835	76	0.003907	0.029088	0.039592
28	POLYGON ((-76.0873 6.70724, -76.0873 6.79829, ...	-848	74	0.004334	0.033422	0.045491
34	POLYGON ((-74.20084 7.70977, -74.20084 7.80102...	-827	85	0.004770	0.038192	0.051984
29	POLYGON ((-75.99747 1.26622, -75.99747 1.35668...	-847	14	0.004852	0.043044	0.058588
30	POLYGON ((-75.99747 5.79759, -75.99747 5.88849...	-847	64	0.005028	0.048072	0.065431
16	POLYGON ((-76.26697 6.6162, -76.26697 6.70724,...	-850	73	0.006363	0.054435	0.074092
20	POLYGON ((-76.17714 5.79759, -76.17714 5.88849...	-849	64	0.007110	0.061545	0.083769
26	POLYGON ((-76.17714 7.16266, -76.17714 7.2538,...	-849	79	0.007957	0.069502	0.094600
25	POLYGON ((-76.17714 6.79829, -76.17714 6.88936...	-849	75	0.008811	0.078313	0.106592
32	POLYGON ((-75.99747 6.70724, -75.99747 6.79829...	-847	74	0.008857	0.087170	0.118647
23	POLYGON ((-76.17714 6.6162, -76.17714 6.70724,...	-849	73	0.008999	0.096169	0.130896
47	POLYGON ((-71.95505 6.25222, -71.95505 6.34319...	-802	69	0.010130	0.106299	0.144684
8	POLYGON ((-76.53646 4.70788, -76.53646 4.79862...	-853	52	0.010988	0.117287	0.159639
37	POLYGON ((-73.93135 2.80464, -73.93135 2.89518...	-824	31	0.011688	0.128975	0.175548
44	POLYGON ((-72.04489 6.88936, -72.04489 6.98044...	-803	76	0.011890	0.140865	0.191732
14	POLYGON ((-76.26697 1.44714, -76.26697 1.53761...	-850	16	0.011955	0.152820	0.208004
42	POLYGON ((-72.13472 6.88936, -72.13472 6.98044...	-804	76	0.012582	0.165402	0.225130
36	POLYGON ((-74.02118 3.16686, -74.02118 3.25744...	-825	35	0.014095	0.179497	0.244315
13	POLYGON ((-76.3568 1.44714, -76.3568 1.53761, ...	-851	16	0.014531	0.194028	0.264093
24	POLYGON ((-76.17714 6.70724, -76.17714 6.79829...	-849	74	0.016014	0.210042	0.285890
41	POLYGON ((-72.76354 7.61854, -72.76354 7.70977...	-811	84	0.016068	0.226110	0.307760
2	POLYGON ((-77.07545 0.72351, -77.07545 0.81396...	-859	8	0.017704	0.243814	0.331857
10	POLYGON ((-76.53646 6.70724, -76.53646 6.79829...	-853	74	0.020277	0.264092	0.359457
18	POLYGON ((-76.17714 5.61584, -76.17714 5.70671...	-849	62	0.021376	0.285468	0.388552
45	POLYGON ((-72.04489 7.07154, -72.04489 7.16266...	-803	78	0.022860	0.308328	0.419667
6	POLYGON ((-76.62629 4.073, -76.62629 4.16367, ...	-854	45	0.023179	0.331507	0.451216
19	POLYGON ((-76.17714 5.70671, -76.17714 5.79759...	-849	63	0.024700	0.356206	0.484835
40	POLYGON ((-73.84152 2.89518, -73.84152 2.98573...	-823	32	0.027811	0.384017	0.522688
1	POLYGON ((-77.07545 0.63307, -77.07545 0.72351...	-859	7	0.027903	0.411920	0.560667
5	POLYGON ((-76.62629 0.99486, -76.62629 1.08531...	-854	11	0.034231	0.446151	0.607259
39	POLYGON ((-73.84152 2.80464, -73.84152 2.89518...	-823	31	0.046898	0.493049	0.671092
9	POLYGON ((-76.53646 6.6162, -76.53646 6.70724,...	-853	73	0.055664	0.548713	0.746857
11	POLYGON ((-76.44663 6.6162, -76.44663 6.70724,...	-852	73	0.070227	0.618940	0.842443
12	POLYGON ((-76.44663 6.70724, -76.44663 6.79829...	-852	74	0.115757	0.734696	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 42 cells

AOO Calculation (direct call)

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

AOO: 42 grid cells

Criterion B Summary

Criterion B status (spatial)

Vulnerable (VU) — Bosque Inundable Subandino (Bosque Inundable Subandino), index 35

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	319642 km²	Least Concern (LC)
B2	AOO	42 cells	Vulnerable (VU)
Overall B	—	—	Vulnerable (VU)