Criterion B Details

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

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 Andino Seco 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() = 63

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 158276.5 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	Bosque_Andino_Seco
0	POLYGON ((-77.52461 0.81396, -77.52461 0.90441...	-864	9	0.002661
1	POLYGON ((-77.52461 0.99486, -77.52461 1.08531...	-864	11	0.025182
2	POLYGON ((-77.52461 1.08531, -77.52461 1.17576...	-864	12	0.012922
3	POLYGON ((-77.52461 1.17576, -77.52461 1.26622...	-864	13	0.000195
4	POLYGON ((-77.43478 0.99486, -77.43478 1.08531...	-863	11	0.059150
5	POLYGON ((-77.43478 1.08531, -77.43478 1.17576...	-863	12	0.021819
6	POLYGON ((-77.43478 1.17576, -77.43478 1.26622...	-863	13	0.008704
7	POLYGON ((-77.34495 0.99486, -77.34495 1.08531...	-862	11	0.006739
8	POLYGON ((-77.34495 1.08531, -77.34495 1.17576...	-862	12	0.005499
9	POLYGON ((-77.25511 1.17576, -77.25511 1.26622...	-861	13	0.009312
10	POLYGON ((-77.25511 1.26622, -77.25511 1.35668...	-861	14	0.006884
11	POLYGON ((-77.16528 1.26622, -77.16528 1.35668...	-860	14	0.036245
12	POLYGON ((-77.16528 1.35668, -77.16528 1.44714...	-860	15	0.007198
13	POLYGON ((-74.56017 3.98235, -74.56017 4.073, ...	-831	44	0.002998
14	POLYGON ((-74.47034 3.8917, -74.47034 3.98235,...	-830	43	0.017348
15	POLYGON ((-74.47034 3.98235, -74.47034 4.073, ...	-830	44	0.069540
16	POLYGON ((-74.29067 4.25434, -74.29067 4.34503...	-828	47	0.004164
17	POLYGON ((-74.29067 4.52643, -74.29067 4.61715...	-828	50	0.114501
18	POLYGON ((-74.29067 4.61715, -74.29067 4.70788...	-828	51	0.080218
19	POLYGON ((-74.20084 4.52643, -74.20084 4.61715...	-827	50	0.021774
20	POLYGON ((-73.84152 5.07091, -73.84152 5.1617,...	-823	56	0.014746
21	POLYGON ((-73.84152 5.1617, -73.84152 5.2525, ...	-823	57	0.000900
22	POLYGON ((-73.75168 5.07091, -73.75168 5.1617,...	-822	56	0.002908
23	POLYGON ((-73.75168 5.43414, -73.75168 5.52498...	-822	60	0.003695
24	POLYGON ((-73.66185 5.43414, -73.66185 5.52498...	-821	60	0.002983
25	POLYGON ((-73.66185 5.52498, -73.66185 5.61584...	-821	61	0.000009
26	POLYGON ((-73.57202 5.52498, -73.57202 5.61584...	-820	61	0.042607
27	POLYGON ((-73.57202 5.61584, -73.57202 5.70671...	-820	62	0.002645
28	POLYGON ((-73.48219 5.52498, -73.48219 5.61584...	-819	61	0.010768
29	POLYGON ((-73.48219 5.61584, -73.48219 5.70671...	-819	62	0.014762
30	POLYGON ((-73.30253 5.52498, -73.30253 5.61584...	-817	61	0.004234
31	POLYGON ((-73.2127 5.52498, -73.2127 5.61584, ...	-816	61	0.000070
32	POLYGON ((-73.2127 8.16621, -73.2127 8.25756, ...	-816	90	0.025348
33	POLYGON ((-73.2127 8.25756, -73.2127 8.34893, ...	-816	91	0.003603
34	POLYGON ((-73.12286 5.61584, -73.12286 5.70671...	-815	62	0.004302
35	POLYGON ((-73.12286 5.70671, -73.12286 5.79759...	-815	63	0.007296
36	POLYGON ((-73.12286 8.16621, -73.12286 8.25756...	-815	90	0.018497
37	POLYGON ((-72.76354 5.88849, -72.76354 5.9794,...	-811	65	0.005313
38	POLYGON ((-72.67371 10.73256, -72.67371 10.824...	-810	118	0.014985
39	POLYGON ((-72.67371 10.82458, -72.67371 10.916...	-810	119	0.002342
40	POLYGON ((-72.58387 6.25222, -72.58387 6.34319...	-809	69	0.002048
41	POLYGON ((-72.58387 6.34319, -72.58387 6.43418...	-809	70	0.007767
42	POLYGON ((-72.58387 6.52518, -72.58387 6.6162,...	-809	72	0.007937
43	POLYGON ((-72.58387 7.16266, -72.58387 7.2538,...	-809	79	0.018317
44	POLYGON ((-72.58387 7.2538, -72.58387 7.34496,...	-809	80	0.020115
45	POLYGON ((-72.58387 7.34496, -72.58387 7.43613...	-809	81	0.006023
46	POLYGON ((-72.58387 7.43613, -72.58387 7.52733...	-809	82	0.028529
47	POLYGON ((-72.58387 10.73256, -72.58387 10.824...	-809	118	0.001264
48	POLYGON ((-72.58387 10.82458, -72.58387 10.916...	-809	119	0.003918
49	POLYGON ((-72.49404 6.25222, -72.49404 6.34319...	-808	69	0.007228
50	POLYGON ((-72.49404 6.52518, -72.49404 6.6162,...	-808	72	0.004122

The column Bosque_Andino_Seco 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(0.8023323662422783)

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	Bosque_Andino_Seco	cumulative_area
25	POLYGON ((-73.66185 5.52498, -73.66185 5.61584...	-821	61	0.000009	0.000009
31	POLYGON ((-73.2127 5.52498, -73.2127 5.61584, ...	-816	61	0.000070	0.000079
3	POLYGON ((-77.52461 1.17576, -77.52461 1.26622...	-864	13	0.000195	0.000274
21	POLYGON ((-73.84152 5.1617, -73.84152 5.2525, ...	-823	57	0.000900	0.001174
47	POLYGON ((-72.58387 10.73256, -72.58387 10.824...	-809	118	0.001264	0.002438
40	POLYGON ((-72.58387 6.25222, -72.58387 6.34319...	-809	69	0.002048	0.004485
39	POLYGON ((-72.67371 10.82458, -72.67371 10.916...	-810	119	0.002342	0.006828
27	POLYGON ((-73.57202 5.61584, -73.57202 5.70671...	-820	62	0.002645	0.009473
0	POLYGON ((-77.52461 0.81396, -77.52461 0.90441...	-864	9	0.002661	0.012134
22	POLYGON ((-73.75168 5.07091, -73.75168 5.1617,...	-822	56	0.002908	0.015042
24	POLYGON ((-73.66185 5.43414, -73.66185 5.52498...	-821	60	0.002983	0.018025
13	POLYGON ((-74.56017 3.98235, -74.56017 4.073, ...	-831	44	0.002998	0.021023
33	POLYGON ((-73.2127 8.25756, -73.2127 8.34893, ...	-816	91	0.003603	0.024626
23	POLYGON ((-73.75168 5.43414, -73.75168 5.52498...	-822	60	0.003695	0.028321
48	POLYGON ((-72.58387 10.82458, -72.58387 10.916...	-809	119	0.003918	0.032239
50	POLYGON ((-72.49404 6.52518, -72.49404 6.6162,...	-808	72	0.004122	0.036362
16	POLYGON ((-74.29067 4.25434, -74.29067 4.34503...	-828	47	0.004164	0.040525
30	POLYGON ((-73.30253 5.52498, -73.30253 5.61584...	-817	61	0.004234	0.044759
34	POLYGON ((-73.12286 5.61584, -73.12286 5.70671...	-815	62	0.004302	0.049061
37	POLYGON ((-72.76354 5.88849, -72.76354 5.9794,...	-811	65	0.005313	0.054374
8	POLYGON ((-77.34495 1.08531, -77.34495 1.17576...	-862	12	0.005499	0.059874
45	POLYGON ((-72.58387 7.34496, -72.58387 7.43613...	-809	81	0.006023	0.065897
7	POLYGON ((-77.34495 0.99486, -77.34495 1.08531...	-862	11	0.006739	0.072635
10	POLYGON ((-77.25511 1.26622, -77.25511 1.35668...	-861	14	0.006884	0.079519
12	POLYGON ((-77.16528 1.35668, -77.16528 1.44714...	-860	15	0.007198	0.086717
49	POLYGON ((-72.49404 6.25222, -72.49404 6.34319...	-808	69	0.007228	0.093944
35	POLYGON ((-73.12286 5.70671, -73.12286 5.79759...	-815	63	0.007296	0.101240
41	POLYGON ((-72.58387 6.34319, -72.58387 6.43418...	-809	70	0.007767	0.109007
42	POLYGON ((-72.58387 6.52518, -72.58387 6.6162,...	-809	72	0.007937	0.116944
6	POLYGON ((-77.43478 1.17576, -77.43478 1.26622...	-863	13	0.008704	0.125649
9	POLYGON ((-77.25511 1.17576, -77.25511 1.26622...	-861	13	0.009312	0.134960
28	POLYGON ((-73.48219 5.52498, -73.48219 5.61584...	-819	61	0.010768	0.145728
2	POLYGON ((-77.52461 1.08531, -77.52461 1.17576...	-864	12	0.012922	0.158650
20	POLYGON ((-73.84152 5.07091, -73.84152 5.1617,...	-823	56	0.014746	0.173396
29	POLYGON ((-73.48219 5.61584, -73.48219 5.70671...	-819	62	0.014762	0.188158
38	POLYGON ((-72.67371 10.73256, -72.67371 10.824...	-810	118	0.014985	0.203143
14	POLYGON ((-74.47034 3.8917, -74.47034 3.98235,...	-830	43	0.017348	0.220491
43	POLYGON ((-72.58387 7.16266, -72.58387 7.2538,...	-809	79	0.018317	0.238808
36	POLYGON ((-73.12286 8.16621, -73.12286 8.25756...	-815	90	0.018497	0.257305
44	POLYGON ((-72.58387 7.2538, -72.58387 7.34496,...	-809	80	0.020115	0.277420
19	POLYGON ((-74.20084 4.52643, -74.20084 4.61715...	-827	50	0.021774	0.299194
5	POLYGON ((-77.43478 1.08531, -77.43478 1.17576...	-863	12	0.021819	0.321013
1	POLYGON ((-77.52461 0.99486, -77.52461 1.08531...	-864	11	0.025182	0.346195
32	POLYGON ((-73.2127 8.16621, -73.2127 8.25756, ...	-816	90	0.025348	0.371543
46	POLYGON ((-72.58387 7.43613, -72.58387 7.52733...	-809	82	0.028529	0.400071
11	POLYGON ((-77.16528 1.26622, -77.16528 1.35668...	-860	14	0.036245	0.436317
26	POLYGON ((-73.57202 5.52498, -73.57202 5.61584...	-820	61	0.042607	0.478924
4	POLYGON ((-77.43478 0.99486, -77.43478 1.08531...	-863	11	0.059150	0.538074
15	POLYGON ((-74.47034 3.98235, -74.47034 4.073, ...	-830	44	0.069540	0.607614
18	POLYGON ((-74.29067 4.61715, -74.29067 4.70788...	-828	51	0.080218	0.687832
17	POLYGON ((-74.29067 4.52643, -74.29067 4.61715...	-828	50	0.114501	0.802332

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	Bosque_Andino_Seco	cumulative_area	cumulative_proportion
25	POLYGON ((-73.66185 5.52498, -73.66185 5.61584...	-821	61	0.000009	0.000009	0.000012
31	POLYGON ((-73.2127 5.52498, -73.2127 5.61584, ...	-816	61	0.000070	0.000079	0.000099
3	POLYGON ((-77.52461 1.17576, -77.52461 1.26622...	-864	13	0.000195	0.000274	0.000342
21	POLYGON ((-73.84152 5.1617, -73.84152 5.2525, ...	-823	57	0.000900	0.001174	0.001463
47	POLYGON ((-72.58387 10.73256, -72.58387 10.824...	-809	118	0.001264	0.002438	0.003038
40	POLYGON ((-72.58387 6.25222, -72.58387 6.34319...	-809	69	0.002048	0.004485	0.005591
39	POLYGON ((-72.67371 10.82458, -72.67371 10.916...	-810	119	0.002342	0.006828	0.008510
27	POLYGON ((-73.57202 5.61584, -73.57202 5.70671...	-820	62	0.002645	0.009473	0.011807
0	POLYGON ((-77.52461 0.81396, -77.52461 0.90441...	-864	9	0.002661	0.012134	0.015124
22	POLYGON ((-73.75168 5.07091, -73.75168 5.1617,...	-822	56	0.002908	0.015042	0.018748
24	POLYGON ((-73.66185 5.43414, -73.66185 5.52498...	-821	60	0.002983	0.018025	0.022465
13	POLYGON ((-74.56017 3.98235, -74.56017 4.073, ...	-831	44	0.002998	0.021023	0.026202
33	POLYGON ((-73.2127 8.25756, -73.2127 8.34893, ...	-816	91	0.003603	0.024626	0.030693
23	POLYGON ((-73.75168 5.43414, -73.75168 5.52498...	-822	60	0.003695	0.028321	0.035299
48	POLYGON ((-72.58387 10.82458, -72.58387 10.916...	-809	119	0.003918	0.032239	0.040182
50	POLYGON ((-72.49404 6.52518, -72.49404 6.6162,...	-808	72	0.004122	0.036362	0.045320
16	POLYGON ((-74.29067 4.25434, -74.29067 4.34503...	-828	47	0.004164	0.040525	0.050509
30	POLYGON ((-73.30253 5.52498, -73.30253 5.61584...	-817	61	0.004234	0.044759	0.055786
34	POLYGON ((-73.12286 5.61584, -73.12286 5.70671...	-815	62	0.004302	0.049061	0.061148
37	POLYGON ((-72.76354 5.88849, -72.76354 5.9794,...	-811	65	0.005313	0.054374	0.067770
8	POLYGON ((-77.34495 1.08531, -77.34495 1.17576...	-862	12	0.005499	0.059874	0.074624
45	POLYGON ((-72.58387 7.34496, -72.58387 7.43613...	-809	81	0.006023	0.065897	0.082131
7	POLYGON ((-77.34495 0.99486, -77.34495 1.08531...	-862	11	0.006739	0.072635	0.090530
10	POLYGON ((-77.25511 1.26622, -77.25511 1.35668...	-861	14	0.006884	0.079519	0.099110
12	POLYGON ((-77.16528 1.35668, -77.16528 1.44714...	-860	15	0.007198	0.086717	0.108081
49	POLYGON ((-72.49404 6.25222, -72.49404 6.34319...	-808	69	0.007228	0.093944	0.117089
35	POLYGON ((-73.12286 5.70671, -73.12286 5.79759...	-815	63	0.007296	0.101240	0.126182
41	POLYGON ((-72.58387 6.34319, -72.58387 6.43418...	-809	70	0.007767	0.109007	0.135863
42	POLYGON ((-72.58387 6.52518, -72.58387 6.6162,...	-809	72	0.007937	0.116944	0.145755
6	POLYGON ((-77.43478 1.17576, -77.43478 1.26622...	-863	13	0.008704	0.125649	0.156604
9	POLYGON ((-77.25511 1.17576, -77.25511 1.26622...	-861	13	0.009312	0.134960	0.168210
28	POLYGON ((-73.48219 5.52498, -73.48219 5.61584...	-819	61	0.010768	0.145728	0.181631
2	POLYGON ((-77.52461 1.08531, -77.52461 1.17576...	-864	12	0.012922	0.158650	0.197736
20	POLYGON ((-73.84152 5.07091, -73.84152 5.1617,...	-823	56	0.014746	0.173396	0.216115
29	POLYGON ((-73.48219 5.61584, -73.48219 5.70671...	-819	62	0.014762	0.188158	0.234513
38	POLYGON ((-72.67371 10.73256, -72.67371 10.824...	-810	118	0.014985	0.203143	0.253190
14	POLYGON ((-74.47034 3.8917, -74.47034 3.98235,...	-830	43	0.017348	0.220491	0.274813
43	POLYGON ((-72.58387 7.16266, -72.58387 7.2538,...	-809	79	0.018317	0.238808	0.297642
36	POLYGON ((-73.12286 8.16621, -73.12286 8.25756...	-815	90	0.018497	0.257305	0.320696
44	POLYGON ((-72.58387 7.2538, -72.58387 7.34496,...	-809	80	0.020115	0.277420	0.345767
19	POLYGON ((-74.20084 4.52643, -74.20084 4.61715...	-827	50	0.021774	0.299194	0.372905
5	POLYGON ((-77.43478 1.08531, -77.43478 1.17576...	-863	12	0.021819	0.321013	0.400099
1	POLYGON ((-77.52461 0.99486, -77.52461 1.08531...	-864	11	0.025182	0.346195	0.431486
32	POLYGON ((-73.2127 8.16621, -73.2127 8.25756, ...	-816	90	0.025348	0.371543	0.463078
46	POLYGON ((-72.58387 7.43613, -72.58387 7.52733...	-809	82	0.028529	0.400071	0.498635
11	POLYGON ((-77.16528 1.26622, -77.16528 1.35668...	-860	14	0.036245	0.436317	0.543810
26	POLYGON ((-73.57202 5.52498, -73.57202 5.61584...	-820	61	0.042607	0.478924	0.596915
4	POLYGON ((-77.43478 0.99486, -77.43478 1.08531...	-863	11	0.059150	0.538074	0.670637
15	POLYGON ((-74.47034 3.98235, -74.47034 4.073, ...	-830	44	0.069540	0.607614	0.757309
18	POLYGON ((-74.29067 4.61715, -74.29067 4.70788...	-828	51	0.080218	0.687832	0.857290
17	POLYGON ((-74.29067 4.52643, -74.29067 4.61715...	-828	50	0.114501	0.802332	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 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) — Bosque Andino Seco (Bosque Andino Seco), index 23

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	158277 km²	Least Concern (LC)
B2	AOO	44 cells	Vulnerable (VU)
Overall B	—	—	Vulnerable (VU)