Criterion B Details

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

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 Laguna Glacial 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() = 50

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 227062.7 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	Laguna_Glacial
0	POLYGON ((-77.7941 0.90441, -77.7941 0.99486, ...	-867	10	0.023114
1	POLYGON ((-77.70427 0.99486, -77.70427 1.08531...	-866	11	0.000277
2	POLYGON ((-77.70427 1.08531, -77.70427 1.17576...	-866	12	0.002733
3	POLYGON ((-76.53646 1.80903, -76.53646 1.89951...	-853	20	0.003009
4	POLYGON ((-76.3568 2.17099, -76.3568 2.2615, -...	-851	24	0.009011
5	POLYGON ((-76.0873 2.80464, -76.0873 2.89518, ...	-848	31	0.002767
6	POLYGON ((-76.0873 3.16686, -76.0873 3.25744, ...	-848	35	0.002594
7	POLYGON ((-75.99747 2.98573, -75.99747 3.07629...	-847	33	0.004202
8	POLYGON ((-75.99747 3.16686, -75.99747 3.25744...	-847	35	0.004540
9	POLYGON ((-75.99747 3.25744, -75.99747 3.34802...	-847	36	0.010140
10	POLYGON ((-75.99747 3.34802, -75.99747 3.43861...	-847	37	0.001732
11	POLYGON ((-75.99747 3.43861, -75.99747 3.52921...	-847	38	0.010395
12	POLYGON ((-75.90764 3.07629, -75.90764 3.16686...	-846	34	0.002790
13	POLYGON ((-75.81781 3.71044, -75.81781 3.80106...	-845	41	0.003060
14	POLYGON ((-75.72798 3.80106, -75.72798 3.8917,...	-844	42	0.002559
15	POLYGON ((-75.36865 4.70788, -75.36865 4.79862...	-840	52	0.009629
16	POLYGON ((-75.27882 4.79862, -75.27882 4.88937...	-839	53	0.003138
17	POLYGON ((-74.29067 3.80106, -74.29067 3.8917,...	-828	42	0.002553
18	POLYGON ((-74.20084 4.25434, -74.20084 4.34503...	-827	47	0.004358
19	POLYGON ((-74.11101 3.80106, -74.11101 3.8917,...	-826	42	0.002879
20	POLYGON ((-74.11101 3.8917, -74.11101 3.98235,...	-826	43	0.020303
21	POLYGON ((-74.11101 3.98235, -74.11101 4.073, ...	-826	44	0.009757
22	POLYGON ((-74.11101 4.98013, -74.11101 5.07091...	-826	55	0.002870
23	POLYGON ((-74.02118 3.8917, -74.02118 3.98235,...	-825	43	0.004785
24	POLYGON ((-74.02118 4.98013, -74.02118 5.07091...	-825	55	0.003393
25	POLYGON ((-73.75168 4.43572, -73.75168 4.52643...	-822	49	0.000970
26	POLYGON ((-73.75168 4.52643, -73.75168 4.61715...	-822	50	0.005378
27	POLYGON ((-73.66185 4.43572, -73.66185 4.52643...	-821	49	0.002854
28	POLYGON ((-73.66185 4.52643, -73.66185 4.61715...	-821	50	0.002956
29	POLYGON ((-73.66185 10.73256, -73.66185 10.824...	-821	118	0.041296
30	POLYGON ((-73.57202 10.73256, -73.57202 10.824...	-820	118	0.003183
31	POLYGON ((-72.9432 7.61854, -72.9432 7.70977, ...	-813	84	0.003843
32	POLYGON ((-72.49404 5.9794, -72.49404 6.07032,...	-808	66	0.002519
33	POLYGON ((-72.49404 6.07032, -72.49404 6.16126...	-808	67	0.000031
34	POLYGON ((-72.49404 6.79829, -72.49404 6.88936...	-808	75	0.004600
35	POLYGON ((-72.49404 6.88936, -72.49404 6.98044...	-808	76	0.004686
36	POLYGON ((-72.40421 6.16126, -72.40421 6.25222...	-807	68	0.003114
37	POLYGON ((-72.31438 6.52518, -72.31438 6.6162,...	-806	72	0.003703
38	POLYGON ((-72.31438 6.6162, -72.31438 6.70724,...	-806	73	0.000115
39	POLYGON ((-72.31438 6.70724, -72.31438 6.79829...	-806	74	0.003408
40	POLYGON ((-72.22455 6.34319, -72.22455 6.43418...	-805	70	0.015378

The column Laguna_Glacial 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(0.24462301832070565)

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	Laguna_Glacial	cumulative_area
33	POLYGON ((-72.49404 6.07032, -72.49404 6.16126...	-808	67	0.000031	0.000031
38	POLYGON ((-72.31438 6.6162, -72.31438 6.70724,...	-806	73	0.000115	0.000146
1	POLYGON ((-77.70427 0.99486, -77.70427 1.08531...	-866	11	0.000277	0.000423
25	POLYGON ((-73.75168 4.43572, -73.75168 4.52643...	-822	49	0.000970	0.001393
10	POLYGON ((-75.99747 3.34802, -75.99747 3.43861...	-847	37	0.001732	0.003124
32	POLYGON ((-72.49404 5.9794, -72.49404 6.07032,...	-808	66	0.002519	0.005644
17	POLYGON ((-74.29067 3.80106, -74.29067 3.8917,...	-828	42	0.002553	0.008197
14	POLYGON ((-75.72798 3.80106, -75.72798 3.8917,...	-844	42	0.002559	0.010755
6	POLYGON ((-76.0873 3.16686, -76.0873 3.25744, ...	-848	35	0.002594	0.013349
2	POLYGON ((-77.70427 1.08531, -77.70427 1.17576...	-866	12	0.002733	0.016082
5	POLYGON ((-76.0873 2.80464, -76.0873 2.89518, ...	-848	31	0.002767	0.018849
12	POLYGON ((-75.90764 3.07629, -75.90764 3.16686...	-846	34	0.002790	0.021639
27	POLYGON ((-73.66185 4.43572, -73.66185 4.52643...	-821	49	0.002854	0.024494
22	POLYGON ((-74.11101 4.98013, -74.11101 5.07091...	-826	55	0.002870	0.027364
19	POLYGON ((-74.11101 3.80106, -74.11101 3.8917,...	-826	42	0.002879	0.030243
28	POLYGON ((-73.66185 4.52643, -73.66185 4.61715...	-821	50	0.002956	0.033199
3	POLYGON ((-76.53646 1.80903, -76.53646 1.89951...	-853	20	0.003009	0.036208
13	POLYGON ((-75.81781 3.71044, -75.81781 3.80106...	-845	41	0.003060	0.039268
36	POLYGON ((-72.40421 6.16126, -72.40421 6.25222...	-807	68	0.003114	0.042382
16	POLYGON ((-75.27882 4.79862, -75.27882 4.88937...	-839	53	0.003138	0.045520
30	POLYGON ((-73.57202 10.73256, -73.57202 10.824...	-820	118	0.003183	0.048703
24	POLYGON ((-74.02118 4.98013, -74.02118 5.07091...	-825	55	0.003393	0.052095
39	POLYGON ((-72.31438 6.70724, -72.31438 6.79829...	-806	74	0.003408	0.055503
37	POLYGON ((-72.31438 6.52518, -72.31438 6.6162,...	-806	72	0.003703	0.059206
31	POLYGON ((-72.9432 7.61854, -72.9432 7.70977, ...	-813	84	0.003843	0.063049
7	POLYGON ((-75.99747 2.98573, -75.99747 3.07629...	-847	33	0.004202	0.067252
18	POLYGON ((-74.20084 4.25434, -74.20084 4.34503...	-827	47	0.004358	0.071610
8	POLYGON ((-75.99747 3.16686, -75.99747 3.25744...	-847	35	0.004540	0.076150
34	POLYGON ((-72.49404 6.79829, -72.49404 6.88936...	-808	75	0.004600	0.080750
35	POLYGON ((-72.49404 6.88936, -72.49404 6.98044...	-808	76	0.004686	0.085436
23	POLYGON ((-74.02118 3.8917, -74.02118 3.98235,...	-825	43	0.004785	0.090222
26	POLYGON ((-73.75168 4.52643, -73.75168 4.61715...	-822	50	0.005378	0.095600
4	POLYGON ((-76.3568 2.17099, -76.3568 2.2615, -...	-851	24	0.009011	0.104611
15	POLYGON ((-75.36865 4.70788, -75.36865 4.79862...	-840	52	0.009629	0.114239
21	POLYGON ((-74.11101 3.98235, -74.11101 4.073, ...	-826	44	0.009757	0.123996
9	POLYGON ((-75.99747 3.25744, -75.99747 3.34802...	-847	36	0.010140	0.134136
11	POLYGON ((-75.99747 3.43861, -75.99747 3.52921...	-847	38	0.010395	0.144532
40	POLYGON ((-72.22455 6.34319, -72.22455 6.43418...	-805	70	0.015378	0.159910
20	POLYGON ((-74.11101 3.8917, -74.11101 3.98235,...	-826	43	0.020303	0.180213
0	POLYGON ((-77.7941 0.90441, -77.7941 0.99486, ...	-867	10	0.023114	0.203327
29	POLYGON ((-73.66185 10.73256, -73.66185 10.824...	-821	118	0.041296	0.244623

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	Laguna_Glacial	cumulative_area	cumulative_proportion
33	POLYGON ((-72.49404 6.07032, -72.49404 6.16126...	-808	67	0.000031	0.000031	0.000128
38	POLYGON ((-72.31438 6.6162, -72.31438 6.70724,...	-806	73	0.000115	0.000146	0.000597
1	POLYGON ((-77.70427 0.99486, -77.70427 1.08531...	-866	11	0.000277	0.000423	0.001729
25	POLYGON ((-73.75168 4.43572, -73.75168 4.52643...	-822	49	0.000970	0.001393	0.005693
10	POLYGON ((-75.99747 3.34802, -75.99747 3.43861...	-847	37	0.001732	0.003124	0.012772
32	POLYGON ((-72.49404 5.9794, -72.49404 6.07032,...	-808	66	0.002519	0.005644	0.023070
17	POLYGON ((-74.29067 3.80106, -74.29067 3.8917,...	-828	42	0.002553	0.008197	0.033507
14	POLYGON ((-75.72798 3.80106, -75.72798 3.8917,...	-844	42	0.002559	0.010755	0.043966
6	POLYGON ((-76.0873 3.16686, -76.0873 3.25744, ...	-848	35	0.002594	0.013349	0.054570
2	POLYGON ((-77.70427 1.08531, -77.70427 1.17576...	-866	12	0.002733	0.016082	0.065742
5	POLYGON ((-76.0873 2.80464, -76.0873 2.89518, ...	-848	31	0.002767	0.018849	0.077053
12	POLYGON ((-75.90764 3.07629, -75.90764 3.16686...	-846	34	0.002790	0.021639	0.088460
27	POLYGON ((-73.66185 4.43572, -73.66185 4.52643...	-821	49	0.002854	0.024494	0.100129
22	POLYGON ((-74.11101 4.98013, -74.11101 5.07091...	-826	55	0.002870	0.027364	0.111862
19	POLYGON ((-74.11101 3.80106, -74.11101 3.8917,...	-826	42	0.002879	0.030243	0.123630
28	POLYGON ((-73.66185 4.52643, -73.66185 4.61715...	-821	50	0.002956	0.033199	0.135714
3	POLYGON ((-76.53646 1.80903, -76.53646 1.89951...	-853	20	0.003009	0.036208	0.148014
13	POLYGON ((-75.81781 3.71044, -75.81781 3.80106...	-845	41	0.003060	0.039268	0.160525
36	POLYGON ((-72.40421 6.16126, -72.40421 6.25222...	-807	68	0.003114	0.042382	0.173253
16	POLYGON ((-75.27882 4.79862, -75.27882 4.88937...	-839	53	0.003138	0.045520	0.186081
30	POLYGON ((-73.57202 10.73256, -73.57202 10.824...	-820	118	0.003183	0.048703	0.199093
24	POLYGON ((-74.02118 4.98013, -74.02118 5.07091...	-825	55	0.003393	0.052095	0.212961
39	POLYGON ((-72.31438 6.70724, -72.31438 6.79829...	-806	74	0.003408	0.055503	0.226894
37	POLYGON ((-72.31438 6.52518, -72.31438 6.6162,...	-806	72	0.003703	0.059206	0.242030
31	POLYGON ((-72.9432 7.61854, -72.9432 7.70977, ...	-813	84	0.003843	0.063049	0.257741
7	POLYGON ((-75.99747 2.98573, -75.99747 3.07629...	-847	33	0.004202	0.067252	0.274920
18	POLYGON ((-74.20084 4.25434, -74.20084 4.34503...	-827	47	0.004358	0.071610	0.292735
8	POLYGON ((-75.99747 3.16686, -75.99747 3.25744...	-847	35	0.004540	0.076150	0.311296
34	POLYGON ((-72.49404 6.79829, -72.49404 6.88936...	-808	75	0.004600	0.080750	0.330100
35	POLYGON ((-72.49404 6.88936, -72.49404 6.98044...	-808	76	0.004686	0.085436	0.349257
23	POLYGON ((-74.02118 3.8917, -74.02118 3.98235,...	-825	43	0.004785	0.090222	0.368819
26	POLYGON ((-73.75168 4.52643, -73.75168 4.61715...	-822	50	0.005378	0.095600	0.390806
4	POLYGON ((-76.3568 2.17099, -76.3568 2.2615, -...	-851	24	0.009011	0.104611	0.427640
15	POLYGON ((-75.36865 4.70788, -75.36865 4.79862...	-840	52	0.009629	0.114239	0.467001
21	POLYGON ((-74.11101 3.98235, -74.11101 4.073, ...	-826	44	0.009757	0.123996	0.506885
9	POLYGON ((-75.99747 3.25744, -75.99747 3.34802...	-847	36	0.010140	0.134136	0.548339
11	POLYGON ((-75.99747 3.43861, -75.99747 3.52921...	-847	38	0.010395	0.144532	0.590834
40	POLYGON ((-72.22455 6.34319, -72.22455 6.43418...	-805	70	0.015378	0.159910	0.653699
20	POLYGON ((-74.11101 3.8917, -74.11101 3.98235,...	-826	43	0.020303	0.180213	0.736697
0	POLYGON ((-77.7941 0.90441, -77.7941 0.99486, ...	-867	10	0.023114	0.203327	0.831184
29	POLYGON ((-73.66185 10.73256, -73.66185 10.824...	-821	118	0.041296	0.244623	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 37 cells

AOO Calculation (direct call)

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

AOO: 37 grid cells

Criterion B Summary

Criterion B status (spatial)

Vulnerable (VU) — Laguna Glacial (Laguna Glacial), index 59

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	227063 km²	Least Concern (LC)
B2	AOO	37 cells	Vulnerable (VU)
Overall B	—	—	Vulnerable (VU)