Criterion B Details

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

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 Platanero y Bananero 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() = 92

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 144007.5 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:

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_Platanero_y_Bananero
0	POLYGON ((-76.71613 7.98358, -76.71613 8.07488...	-855	88	0.005094
1	POLYGON ((-76.71613 8.07488, -76.71613 8.16621...	-855	89	0.003619
2	POLYGON ((-76.62629 7.52733, -76.62629 7.61854...	-854	83	0.012775
3	POLYGON ((-76.62629 7.61854, -76.62629 7.70977...	-854	84	0.065699
4	POLYGON ((-76.62629 7.70977, -76.62629 7.80102...	-854	85	0.057034
5	POLYGON ((-76.62629 7.80102, -76.62629 7.89229...	-854	86	0.246817
6	POLYGON ((-76.62629 7.89229, -76.62629 7.98358...	-854	87	0.014714
7	POLYGON ((-76.62629 7.98358, -76.62629 8.07488...	-854	88	0.284478
8	POLYGON ((-76.62629 8.07488, -76.62629 8.16621...	-854	89	0.014531
9	POLYGON ((-76.62629 8.25756, -76.62629 8.34893...	-854	91	0.001174
10	POLYGON ((-76.62629 8.62317, -76.62629 8.71462...	-854	95	0.040853
11	POLYGON ((-76.62629 8.71462, -76.62629 8.8061,...	-854	96	0.000856
12	POLYGON ((-76.53646 7.80102, -76.53646 7.89229...	-853	86	0.051403
13	POLYGON ((-76.53646 7.89229, -76.53646 7.98358...	-853	87	0.113540
14	POLYGON ((-76.53646 7.98358, -76.53646 8.07488...	-853	88	0.002424
15	POLYGON ((-76.53646 8.16621, -76.53646 8.25756...	-853	90	0.010637
16	POLYGON ((-76.53646 8.25756, -76.53646 8.34893...	-853	91	0.004750
17	POLYGON ((-76.53646 8.71462, -76.53646 8.8061,...	-853	96	0.017359
18	POLYGON ((-76.17714 9.17222, -76.17714 9.26381...	-849	101	0.001939
19	POLYGON ((-76.0873 8.89759, -76.0873 8.98911, ...	-848	98	0.020939
20	POLYGON ((-76.0873 9.08066, -76.0873 9.17222, ...	-848	100	0.054253
21	POLYGON ((-76.0873 9.17222, -76.0873 9.26381, ...	-848	101	0.131866
22	POLYGON ((-75.99747 9.08066, -75.99747 9.17222...	-847	100	0.063110
23	POLYGON ((-75.99747 9.17222, -75.99747 9.26381...	-847	101	0.002818
24	POLYGON ((-75.81781 4.43572, -75.81781 4.52643...	-845	49	0.005690
25	POLYGON ((-75.72798 4.43572, -75.72798 4.52643...	-844	49	0.009511
26	POLYGON ((-75.45848 9.35542, -75.45848 9.44706...	-841	103	0.002682
27	POLYGON ((-75.45848 9.44706, -75.45848 9.53872...	-841	104	0.000481
28	POLYGON ((-74.20084 10.45667, -74.20084 10.548...	-827	115	0.009199
29	POLYGON ((-74.20084 10.54861, -74.20084 10.640...	-827	116	0.000412
30	POLYGON ((-74.20084 10.73256, -74.20084 10.824...	-827	118	0.017339
31	POLYGON ((-74.20084 10.82458, -74.20084 10.916...	-827	119	0.116684
32	POLYGON ((-74.20084 10.91663, -74.20084 11.008...	-827	120	0.072097
33	POLYGON ((-74.11101 10.45667, -74.11101 10.548...	-826	115	0.005560
34	POLYGON ((-74.11101 10.54861, -74.11101 10.640...	-826	116	0.004766
35	POLYGON ((-74.11101 10.64057, -74.11101 10.732...	-826	117	0.012084
36	POLYGON ((-74.11101 10.73256, -74.11101 10.824...	-826	118	0.382028
37	POLYGON ((-74.11101 10.82458, -74.11101 10.916...	-826	119	0.442224
38	POLYGON ((-74.11101 10.91663, -74.11101 11.008...	-826	120	0.242610
39	POLYGON ((-74.02118 10.73256, -74.02118 10.824...	-825	118	0.026321
40	POLYGON ((-73.75168 8.53173, -73.75168 8.62317...	-822	94	0.000428
41	POLYGON ((-73.66185 8.53173, -73.66185 8.62317...	-821	94	0.009348
42	POLYGON ((-73.57202 11.19293, -73.57202 11.285...	-820	123	0.001818
43	POLYGON ((-73.48219 11.19293, -73.48219 11.285...	-819	123	0.005016
44	POLYGON ((-73.2127 11.19293, -73.2127 11.28509...	-816	123	0.009227
45	POLYGON ((-73.12286 11.19293, -73.12286 11.285...	-815	123	0.119841
46	POLYGON ((-73.12286 11.28509, -73.12286 11.377...	-815	124	0.073538
47	POLYGON ((-73.03303 11.19293, -73.03303 11.285...	-814	123	0.006589

The column Agroecosistema_Platanero_y_Bananero 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(2.798175661935643)

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_Platanero_y_Bananero	cumulative_area
29	POLYGON ((-74.20084 10.54861, -74.20084 10.640...	-827	116	0.000412	0.000412
40	POLYGON ((-73.75168 8.53173, -73.75168 8.62317...	-822	94	0.000428	0.000840
27	POLYGON ((-75.45848 9.44706, -75.45848 9.53872...	-841	104	0.000481	0.001321
11	POLYGON ((-76.62629 8.71462, -76.62629 8.8061,...	-854	96	0.000856	0.002177
9	POLYGON ((-76.62629 8.25756, -76.62629 8.34893...	-854	91	0.001174	0.003351
42	POLYGON ((-73.57202 11.19293, -73.57202 11.285...	-820	123	0.001818	0.005169
18	POLYGON ((-76.17714 9.17222, -76.17714 9.26381...	-849	101	0.001939	0.007107
14	POLYGON ((-76.53646 7.98358, -76.53646 8.07488...	-853	88	0.002424	0.009531
26	POLYGON ((-75.45848 9.35542, -75.45848 9.44706...	-841	103	0.002682	0.012213
23	POLYGON ((-75.99747 9.17222, -75.99747 9.26381...	-847	101	0.002818	0.015032
1	POLYGON ((-76.71613 8.07488, -76.71613 8.16621...	-855	89	0.003619	0.018651
16	POLYGON ((-76.53646 8.25756, -76.53646 8.34893...	-853	91	0.004750	0.023401
34	POLYGON ((-74.11101 10.54861, -74.11101 10.640...	-826	116	0.004766	0.028167
43	POLYGON ((-73.48219 11.19293, -73.48219 11.285...	-819	123	0.005016	0.033183
0	POLYGON ((-76.71613 7.98358, -76.71613 8.07488...	-855	88	0.005094	0.038277
33	POLYGON ((-74.11101 10.45667, -74.11101 10.548...	-826	115	0.005560	0.043838
24	POLYGON ((-75.81781 4.43572, -75.81781 4.52643...	-845	49	0.005690	0.049528
47	POLYGON ((-73.03303 11.19293, -73.03303 11.285...	-814	123	0.006589	0.056118
28	POLYGON ((-74.20084 10.45667, -74.20084 10.548...	-827	115	0.009199	0.065316
44	POLYGON ((-73.2127 11.19293, -73.2127 11.28509...	-816	123	0.009227	0.074544
41	POLYGON ((-73.66185 8.53173, -73.66185 8.62317...	-821	94	0.009348	0.083892
25	POLYGON ((-75.72798 4.43572, -75.72798 4.52643...	-844	49	0.009511	0.093403
15	POLYGON ((-76.53646 8.16621, -76.53646 8.25756...	-853	90	0.010637	0.104040
35	POLYGON ((-74.11101 10.64057, -74.11101 10.732...	-826	117	0.012084	0.116123
2	POLYGON ((-76.62629 7.52733, -76.62629 7.61854...	-854	83	0.012775	0.128899
8	POLYGON ((-76.62629 8.07488, -76.62629 8.16621...	-854	89	0.014531	0.143430
6	POLYGON ((-76.62629 7.89229, -76.62629 7.98358...	-854	87	0.014714	0.158144
30	POLYGON ((-74.20084 10.73256, -74.20084 10.824...	-827	118	0.017339	0.175483
17	POLYGON ((-76.53646 8.71462, -76.53646 8.8061,...	-853	96	0.017359	0.192842
19	POLYGON ((-76.0873 8.89759, -76.0873 8.98911, ...	-848	98	0.020939	0.213781
39	POLYGON ((-74.02118 10.73256, -74.02118 10.824...	-825	118	0.026321	0.240102
10	POLYGON ((-76.62629 8.62317, -76.62629 8.71462...	-854	95	0.040853	0.280955
12	POLYGON ((-76.53646 7.80102, -76.53646 7.89229...	-853	86	0.051403	0.332358
20	POLYGON ((-76.0873 9.08066, -76.0873 9.17222, ...	-848	100	0.054253	0.386611
4	POLYGON ((-76.62629 7.70977, -76.62629 7.80102...	-854	85	0.057034	0.443644
22	POLYGON ((-75.99747 9.08066, -75.99747 9.17222...	-847	100	0.063110	0.506754
3	POLYGON ((-76.62629 7.61854, -76.62629 7.70977...	-854	84	0.065699	0.572453
32	POLYGON ((-74.20084 10.91663, -74.20084 11.008...	-827	120	0.072097	0.644550
46	POLYGON ((-73.12286 11.28509, -73.12286 11.377...	-815	124	0.073538	0.718088
13	POLYGON ((-76.53646 7.89229, -76.53646 7.98358...	-853	87	0.113540	0.831627
31	POLYGON ((-74.20084 10.82458, -74.20084 10.916...	-827	119	0.116684	0.948311
45	POLYGON ((-73.12286 11.19293, -73.12286 11.285...	-815	123	0.119841	1.068152
21	POLYGON ((-76.0873 9.17222, -76.0873 9.26381, ...	-848	101	0.131866	1.200018
38	POLYGON ((-74.11101 10.91663, -74.11101 11.008...	-826	120	0.242610	1.442628
5	POLYGON ((-76.62629 7.80102, -76.62629 7.89229...	-854	86	0.246817	1.689445
7	POLYGON ((-76.62629 7.98358, -76.62629 8.07488...	-854	88	0.284478	1.973923
36	POLYGON ((-74.11101 10.73256, -74.11101 10.824...	-826	118	0.382028	2.355951
37	POLYGON ((-74.11101 10.82458, -74.11101 10.916...	-826	119	0.442224	2.798176

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_Platanero_y_Bananero	cumulative_area	cumulative_proportion
29	POLYGON ((-74.20084 10.54861, -74.20084 10.640...	-827	116	0.000412	0.000412	0.000147
40	POLYGON ((-73.75168 8.53173, -73.75168 8.62317...	-822	94	0.000428	0.000840	0.000300
27	POLYGON ((-75.45848 9.44706, -75.45848 9.53872...	-841	104	0.000481	0.001321	0.000472
11	POLYGON ((-76.62629 8.71462, -76.62629 8.8061,...	-854	96	0.000856	0.002177	0.000778
9	POLYGON ((-76.62629 8.25756, -76.62629 8.34893...	-854	91	0.001174	0.003351	0.001198
42	POLYGON ((-73.57202 11.19293, -73.57202 11.285...	-820	123	0.001818	0.005169	0.001847
18	POLYGON ((-76.17714 9.17222, -76.17714 9.26381...	-849	101	0.001939	0.007107	0.002540
14	POLYGON ((-76.53646 7.98358, -76.53646 8.07488...	-853	88	0.002424	0.009531	0.003406
26	POLYGON ((-75.45848 9.35542, -75.45848 9.44706...	-841	103	0.002682	0.012213	0.004365
23	POLYGON ((-75.99747 9.17222, -75.99747 9.26381...	-847	101	0.002818	0.015032	0.005372
1	POLYGON ((-76.71613 8.07488, -76.71613 8.16621...	-855	89	0.003619	0.018651	0.006665
16	POLYGON ((-76.53646 8.25756, -76.53646 8.34893...	-853	91	0.004750	0.023401	0.008363
34	POLYGON ((-74.11101 10.54861, -74.11101 10.640...	-826	116	0.004766	0.028167	0.010066
43	POLYGON ((-73.48219 11.19293, -73.48219 11.285...	-819	123	0.005016	0.033183	0.011859
0	POLYGON ((-76.71613 7.98358, -76.71613 8.07488...	-855	88	0.005094	0.038277	0.013679
33	POLYGON ((-74.11101 10.45667, -74.11101 10.548...	-826	115	0.005560	0.043838	0.015667
24	POLYGON ((-75.81781 4.43572, -75.81781 4.52643...	-845	49	0.005690	0.049528	0.017700
47	POLYGON ((-73.03303 11.19293, -73.03303 11.285...	-814	123	0.006589	0.056118	0.020055
28	POLYGON ((-74.20084 10.45667, -74.20084 10.548...	-827	115	0.009199	0.065316	0.023343
44	POLYGON ((-73.2127 11.19293, -73.2127 11.28509...	-816	123	0.009227	0.074544	0.026640
41	POLYGON ((-73.66185 8.53173, -73.66185 8.62317...	-821	94	0.009348	0.083892	0.029981
25	POLYGON ((-75.72798 4.43572, -75.72798 4.52643...	-844	49	0.009511	0.093403	0.033380
15	POLYGON ((-76.53646 8.16621, -76.53646 8.25756...	-853	90	0.010637	0.104040	0.037181
35	POLYGON ((-74.11101 10.64057, -74.11101 10.732...	-826	117	0.012084	0.116123	0.041500
2	POLYGON ((-76.62629 7.52733, -76.62629 7.61854...	-854	83	0.012775	0.128899	0.046065
8	POLYGON ((-76.62629 8.07488, -76.62629 8.16621...	-854	89	0.014531	0.143430	0.051258
6	POLYGON ((-76.62629 7.89229, -76.62629 7.98358...	-854	87	0.014714	0.158144	0.056517
30	POLYGON ((-74.20084 10.73256, -74.20084 10.824...	-827	118	0.017339	0.175483	0.062713
17	POLYGON ((-76.53646 8.71462, -76.53646 8.8061,...	-853	96	0.017359	0.192842	0.068917
19	POLYGON ((-76.0873 8.89759, -76.0873 8.98911, ...	-848	98	0.020939	0.213781	0.076400
39	POLYGON ((-74.02118 10.73256, -74.02118 10.824...	-825	118	0.026321	0.240102	0.085807
10	POLYGON ((-76.62629 8.62317, -76.62629 8.71462...	-854	95	0.040853	0.280955	0.100407
12	POLYGON ((-76.53646 7.80102, -76.53646 7.89229...	-853	86	0.051403	0.332358	0.118777
20	POLYGON ((-76.0873 9.08066, -76.0873 9.17222, ...	-848	100	0.054253	0.386611	0.138165
4	POLYGON ((-76.62629 7.70977, -76.62629 7.80102...	-854	85	0.057034	0.443644	0.158548
22	POLYGON ((-75.99747 9.08066, -75.99747 9.17222...	-847	100	0.063110	0.506754	0.181102
3	POLYGON ((-76.62629 7.61854, -76.62629 7.70977...	-854	84	0.065699	0.572453	0.204581
32	POLYGON ((-74.20084 10.91663, -74.20084 11.008...	-827	120	0.072097	0.644550	0.230346
46	POLYGON ((-73.12286 11.28509, -73.12286 11.377...	-815	124	0.073538	0.718088	0.256627
13	POLYGON ((-76.53646 7.89229, -76.53646 7.98358...	-853	87	0.113540	0.831627	0.297203
31	POLYGON ((-74.20084 10.82458, -74.20084 10.916...	-827	119	0.116684	0.948311	0.338903
45	POLYGON ((-73.12286 11.19293, -73.12286 11.285...	-815	123	0.119841	1.068152	0.381731
21	POLYGON ((-76.0873 9.17222, -76.0873 9.26381, ...	-848	101	0.131866	1.200018	0.428857
38	POLYGON ((-74.11101 10.91663, -74.11101 11.008...	-826	120	0.242610	1.442628	0.515560
5	POLYGON ((-76.62629 7.80102, -76.62629 7.89229...	-854	86	0.246817	1.689445	0.603767
7	POLYGON ((-76.62629 7.98358, -76.62629 8.07488...	-854	88	0.284478	1.973923	0.705432
36	POLYGON ((-74.11101 10.73256, -74.11101 10.824...	-826	118	0.382028	2.355951	0.841960
37	POLYGON ((-74.11101 10.82458, -74.11101 10.916...	-826	119	0.442224	2.798176	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 36 cells

AOO Calculation (direct call)

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

AOO: 36 grid cells

Criterion B Summary

Criterion B status (spatial)

Vulnerable (VU) — Agroecosistema Platanero y Bananero (Agroecosistema Platanero y Bananero), index 14

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	144007 km²	Least Concern (LC)
B2	AOO	36 cells	Vulnerable (VU)
Overall B	—	—	Vulnerable (VU)