Criterion B Details

# Default ecosystem code for template development.
# This line is replaced by build_ecosystem_pages.py for each ecosystem.
ecosystem_code = 'Fondos Blandos con Vegetacion No Vascular'

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 Fondos Blandos con Vegetacion No Vascular 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() = 70

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 234302.8 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	Fondos_Blandos_con_Vegetacion_No_Vascular
0	POLYGON ((-77.43478 3.80106, -77.43478 3.8917,...	-863	42	0.190937
1	POLYGON ((-77.43478 3.8917, -77.43478 3.98235,...	-863	43	0.237355
2	POLYGON ((-77.43478 3.98235, -77.43478 4.073, ...	-863	44	0.074397
3	POLYGON ((-77.34495 3.80106, -77.34495 3.8917,...	-862	42	0.879010
4	POLYGON ((-77.34495 3.8917, -77.34495 3.98235,...	-862	43	0.722770
5	POLYGON ((-77.34495 3.98235, -77.34495 4.073, ...	-862	44	0.056245
6	POLYGON ((-77.34495 8.62317, -77.34495 8.71462...	-862	95	0.001035
7	POLYGON ((-77.25511 3.80106, -77.25511 3.8917,...	-861	42	0.527619
8	POLYGON ((-77.25511 3.8917, -77.25511 3.98235,...	-861	43	0.373268
9	POLYGON ((-77.25511 3.98235, -77.25511 4.073, ...	-861	44	0.491709
10	POLYGON ((-77.25511 8.53173, -77.25511 8.62317...	-861	94	0.013660
11	POLYGON ((-77.25511 8.62317, -77.25511 8.71462...	-861	95	0.004169
12	POLYGON ((-77.16528 3.8917, -77.16528 3.98235,...	-860	43	0.004273
13	POLYGON ((-77.16528 3.98235, -77.16528 4.073, ...	-860	44	0.369772
14	POLYGON ((-77.07545 8.25756, -77.07545 8.34893...	-859	91	0.026301
15	POLYGON ((-77.07545 8.34893, -77.07545 8.44032...	-859	92	0.034110
16	POLYGON ((-76.98562 8.25756, -76.98562 8.34893...	-858	91	0.070095
17	POLYGON ((-75.63815 9.6304, -75.63815 9.72211,...	-843	106	0.002910
18	POLYGON ((-75.63815 9.72211, -75.63815 9.81384...	-843	107	0.018503
19	POLYGON ((-75.54832 9.72211, -75.54832 9.81384...	-842	107	0.011249
20	POLYGON ((-75.54832 9.81384, -75.54832 9.9056,...	-842	108	0.002594
21	POLYGON ((-75.54832 9.9056, -75.54832 9.99738,...	-842	109	0.037019
22	POLYGON ((-74.47034 11.0087, -74.47034 11.1008...	-830	121	0.074121
23	POLYGON ((-74.38051 11.0087, -74.38051 11.1008...	-829	121	0.331935
24	POLYGON ((-74.38051 11.1008, -74.38051 11.1929...	-829	122	0.018344
25	POLYGON ((-74.29067 11.0087, -74.29067 11.1008...	-828	121	0.088944
26	POLYGON ((-74.29067 11.1008, -74.29067 11.1929...	-828	122	0.115512
27	POLYGON ((-74.02118 11.28509, -74.02118 11.377...	-825	124	0.003229
28	POLYGON ((-72.04489 12.2083, -72.04489 12.3007...	-803	134	0.003724
29	POLYGON ((-71.95505 12.2083, -71.95505 12.3007...	-802	134	0.063366
30	POLYGON ((-71.68556 12.39331, -71.68556 12.485...	-799	136	0.000196
31	POLYGON ((-71.59573 12.39331, -71.59573 12.485...	-798	136	0.000873
32	POLYGON ((-71.32623 12.30079, -71.32623 12.393...	-795	135	0.001667
33	POLYGON ((-71.2364 11.83865, -71.2364 11.93102...	-794	130	0.004086
34	POLYGON ((-71.2364 11.93102, -71.2364 12.02342...	-794	131	0.001276
35	POLYGON ((-71.2364 12.30079, -71.2364 12.39331...	-794	135	0.018784
36	POLYGON ((-71.14657 12.11584, -71.14657 12.208...	-793	133	0.000943
37	POLYGON ((-71.14657 12.2083, -71.14657 12.3007...	-793	134	0.003270
38	POLYGON ((-71.14657 12.30079, -71.14657 12.393...	-793	135	0.001029
39	POLYGON ((-71.05674 12.11584, -71.05674 12.208...	-792	133	0.000863

The column Fondos_Blandos_con_Vegetacion_No_Vascular 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(4.881160452166059)

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	Fondos_Blandos_con_Vegetacion_No_Vascular	cumulative_area
30	POLYGON ((-71.68556 12.39331, -71.68556 12.485...	-799	136	0.000196	0.000196
39	POLYGON ((-71.05674 12.11584, -71.05674 12.208...	-792	133	0.000863	0.001059
31	POLYGON ((-71.59573 12.39331, -71.59573 12.485...	-798	136	0.000873	0.001933
36	POLYGON ((-71.14657 12.11584, -71.14657 12.208...	-793	133	0.000943	0.002876
38	POLYGON ((-71.14657 12.30079, -71.14657 12.393...	-793	135	0.001029	0.003904
6	POLYGON ((-77.34495 8.62317, -77.34495 8.71462...	-862	95	0.001035	0.004939
34	POLYGON ((-71.2364 11.93102, -71.2364 12.02342...	-794	131	0.001276	0.006216
32	POLYGON ((-71.32623 12.30079, -71.32623 12.393...	-795	135	0.001667	0.007882
20	POLYGON ((-75.54832 9.81384, -75.54832 9.9056,...	-842	108	0.002594	0.010477
17	POLYGON ((-75.63815 9.6304, -75.63815 9.72211,...	-843	106	0.002910	0.013386
27	POLYGON ((-74.02118 11.28509, -74.02118 11.377...	-825	124	0.003229	0.016615
37	POLYGON ((-71.14657 12.2083, -71.14657 12.3007...	-793	134	0.003270	0.019884
28	POLYGON ((-72.04489 12.2083, -72.04489 12.3007...	-803	134	0.003724	0.023608
33	POLYGON ((-71.2364 11.83865, -71.2364 11.93102...	-794	130	0.004086	0.027694
11	POLYGON ((-77.25511 8.62317, -77.25511 8.71462...	-861	95	0.004169	0.031863
12	POLYGON ((-77.16528 3.8917, -77.16528 3.98235,...	-860	43	0.004273	0.036135
19	POLYGON ((-75.54832 9.72211, -75.54832 9.81384...	-842	107	0.011249	0.047384
10	POLYGON ((-77.25511 8.53173, -77.25511 8.62317...	-861	94	0.013660	0.061045
24	POLYGON ((-74.38051 11.1008, -74.38051 11.1929...	-829	122	0.018344	0.079389
18	POLYGON ((-75.63815 9.72211, -75.63815 9.81384...	-843	107	0.018503	0.097892
35	POLYGON ((-71.2364 12.30079, -71.2364 12.39331...	-794	135	0.018784	0.116675
14	POLYGON ((-77.07545 8.25756, -77.07545 8.34893...	-859	91	0.026301	0.142976
15	POLYGON ((-77.07545 8.34893, -77.07545 8.44032...	-859	92	0.034110	0.177086
21	POLYGON ((-75.54832 9.9056, -75.54832 9.99738,...	-842	109	0.037019	0.214105
5	POLYGON ((-77.34495 3.98235, -77.34495 4.073, ...	-862	44	0.056245	0.270350
29	POLYGON ((-71.95505 12.2083, -71.95505 12.3007...	-802	134	0.063366	0.333717
16	POLYGON ((-76.98562 8.25756, -76.98562 8.34893...	-858	91	0.070095	0.403812
22	POLYGON ((-74.47034 11.0087, -74.47034 11.1008...	-830	121	0.074121	0.477932
2	POLYGON ((-77.43478 3.98235, -77.43478 4.073, ...	-863	44	0.074397	0.552329
25	POLYGON ((-74.29067 11.0087, -74.29067 11.1008...	-828	121	0.088944	0.641273
26	POLYGON ((-74.29067 11.1008, -74.29067 11.1929...	-828	122	0.115512	0.756785
0	POLYGON ((-77.43478 3.80106, -77.43478 3.8917,...	-863	42	0.190937	0.947722
1	POLYGON ((-77.43478 3.8917, -77.43478 3.98235,...	-863	43	0.237355	1.185077
23	POLYGON ((-74.38051 11.0087, -74.38051 11.1008...	-829	121	0.331935	1.517012
13	POLYGON ((-77.16528 3.98235, -77.16528 4.073, ...	-860	44	0.369772	1.886784
8	POLYGON ((-77.25511 3.8917, -77.25511 3.98235,...	-861	43	0.373268	2.260052
9	POLYGON ((-77.25511 3.98235, -77.25511 4.073, ...	-861	44	0.491709	2.751761
7	POLYGON ((-77.25511 3.80106, -77.25511 3.8917,...	-861	42	0.527619	3.279380
4	POLYGON ((-77.34495 3.8917, -77.34495 3.98235,...	-862	43	0.722770	4.002150
3	POLYGON ((-77.34495 3.80106, -77.34495 3.8917,...	-862	42	0.879010	4.881160

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	Fondos_Blandos_con_Vegetacion_No_Vascular	cumulative_area	cumulative_proportion
30	POLYGON ((-71.68556 12.39331, -71.68556 12.485...	-799	136	0.000196	0.000196	0.000040
39	POLYGON ((-71.05674 12.11584, -71.05674 12.208...	-792	133	0.000863	0.001059	0.000217
31	POLYGON ((-71.59573 12.39331, -71.59573 12.485...	-798	136	0.000873	0.001933	0.000396
36	POLYGON ((-71.14657 12.11584, -71.14657 12.208...	-793	133	0.000943	0.002876	0.000589
38	POLYGON ((-71.14657 12.30079, -71.14657 12.393...	-793	135	0.001029	0.003904	0.000800
6	POLYGON ((-77.34495 8.62317, -77.34495 8.71462...	-862	95	0.001035	0.004939	0.001012
34	POLYGON ((-71.2364 11.93102, -71.2364 12.02342...	-794	131	0.001276	0.006216	0.001273
32	POLYGON ((-71.32623 12.30079, -71.32623 12.393...	-795	135	0.001667	0.007882	0.001615
20	POLYGON ((-75.54832 9.81384, -75.54832 9.9056,...	-842	108	0.002594	0.010477	0.002146
17	POLYGON ((-75.63815 9.6304, -75.63815 9.72211,...	-843	106	0.002910	0.013386	0.002742
27	POLYGON ((-74.02118 11.28509, -74.02118 11.377...	-825	124	0.003229	0.016615	0.003404
37	POLYGON ((-71.14657 12.2083, -71.14657 12.3007...	-793	134	0.003270	0.019884	0.004074
28	POLYGON ((-72.04489 12.2083, -72.04489 12.3007...	-803	134	0.003724	0.023608	0.004837
33	POLYGON ((-71.2364 11.83865, -71.2364 11.93102...	-794	130	0.004086	0.027694	0.005674
11	POLYGON ((-77.25511 8.62317, -77.25511 8.71462...	-861	95	0.004169	0.031863	0.006528
12	POLYGON ((-77.16528 3.8917, -77.16528 3.98235,...	-860	43	0.004273	0.036135	0.007403
19	POLYGON ((-75.54832 9.72211, -75.54832 9.81384...	-842	107	0.011249	0.047384	0.009708
10	POLYGON ((-77.25511 8.53173, -77.25511 8.62317...	-861	94	0.013660	0.061045	0.012506
24	POLYGON ((-74.38051 11.1008, -74.38051 11.1929...	-829	122	0.018344	0.079389	0.016264
18	POLYGON ((-75.63815 9.72211, -75.63815 9.81384...	-843	107	0.018503	0.097892	0.020055
35	POLYGON ((-71.2364 12.30079, -71.2364 12.39331...	-794	135	0.018784	0.116675	0.023903
14	POLYGON ((-77.07545 8.25756, -77.07545 8.34893...	-859	91	0.026301	0.142976	0.029291
15	POLYGON ((-77.07545 8.34893, -77.07545 8.44032...	-859	92	0.034110	0.177086	0.036279
21	POLYGON ((-75.54832 9.9056, -75.54832 9.99738,...	-842	109	0.037019	0.214105	0.043864
5	POLYGON ((-77.34495 3.98235, -77.34495 4.073, ...	-862	44	0.056245	0.270350	0.055387
29	POLYGON ((-71.95505 12.2083, -71.95505 12.3007...	-802	134	0.063366	0.333717	0.068368
16	POLYGON ((-76.98562 8.25756, -76.98562 8.34893...	-858	91	0.070095	0.403812	0.082729
22	POLYGON ((-74.47034 11.0087, -74.47034 11.1008...	-830	121	0.074121	0.477932	0.097914
2	POLYGON ((-77.43478 3.98235, -77.43478 4.073, ...	-863	44	0.074397	0.552329	0.113155
25	POLYGON ((-74.29067 11.0087, -74.29067 11.1008...	-828	121	0.088944	0.641273	0.131377
26	POLYGON ((-74.29067 11.1008, -74.29067 11.1929...	-828	122	0.115512	0.756785	0.155042
0	POLYGON ((-77.43478 3.80106, -77.43478 3.8917,...	-863	42	0.190937	0.947722	0.194159
1	POLYGON ((-77.43478 3.8917, -77.43478 3.98235,...	-863	43	0.237355	1.185077	0.242786
23	POLYGON ((-74.38051 11.0087, -74.38051 11.1008...	-829	121	0.331935	1.517012	0.310789
13	POLYGON ((-77.16528 3.98235, -77.16528 4.073, ...	-860	44	0.369772	1.886784	0.386544
8	POLYGON ((-77.25511 3.8917, -77.25511 3.98235,...	-861	43	0.373268	2.260052	0.463015
9	POLYGON ((-77.25511 3.98235, -77.25511 4.073, ...	-861	44	0.491709	2.751761	0.563751
7	POLYGON ((-77.25511 3.80106, -77.25511 3.8917,...	-861	42	0.527619	3.279380	0.671844
4	POLYGON ((-77.34495 3.8917, -77.34495 3.98235,...	-862	43	0.722770	4.002150	0.819918
3	POLYGON ((-77.34495 3.80106, -77.34495 3.8917,...	-862	42	0.879010	4.881160	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 23 cells

AOO Calculation (direct call)

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

AOO: 23 grid cells

Criterion B Summary

Criterion B status (spatial)

Vulnerable (VU) — Fondos Blandos con Vegetacion No Vascular (Fondos Blandos con Vegetacion No Vascular), index 46

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	234303 km²	Least Concern (LC)
B2	AOO	23 cells	Vulnerable (VU)
Overall B	—	—	Vulnerable (VU)