Criterion B Details

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

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 Tectonica 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() = 22

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 91019.2 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	Laguna_Tectonica
0	POLYGON ((-77.16528 0.99486, -77.16528 1.08531...	-860	11	0.046567
1	POLYGON ((-77.16528 1.08531, -77.16528 1.17576...	-860	12	0.013276
2	POLYGON ((-77.07545 0.99486, -77.07545 1.08531...	-859	11	0.129713
3	POLYGON ((-77.07545 1.08531, -77.07545 1.17576...	-859	12	0.241891
4	POLYGON ((-75.54832 4.98013, -75.54832 5.07091...	-842	55	0.002874
5	POLYGON ((-74.20084 4.70788, -74.20084 4.79862...	-827	52	0.005083
6	POLYGON ((-74.11101 4.79862, -74.11101 4.88937...	-826	53	0.006178
7	POLYGON ((-73.93135 4.61715, -73.93135 4.70788...	-824	51	0.013624
8	POLYGON ((-73.93135 4.70788, -73.93135 4.79862...	-824	52	0.012706
9	POLYGON ((-73.93135 5.07091, -73.93135 5.1617,...	-824	56	0.049776
10	POLYGON ((-73.93135 5.1617, -73.93135 5.2525, ...	-824	57	0.036910
11	POLYGON ((-73.84152 4.88937, -73.84152 4.98013...	-823	54	0.069430
12	POLYGON ((-73.84152 5.1617, -73.84152 5.2525, ...	-823	57	0.002626
13	POLYGON ((-73.84152 5.2525, -73.84152 5.34332,...	-823	58	0.009160
14	POLYGON ((-73.75168 4.88937, -73.75168 4.98013...	-822	54	0.066568
15	POLYGON ((-73.75168 4.98013, -73.75168 5.07091...	-822	55	0.088427
16	POLYGON ((-73.75168 5.1617, -73.75168 5.2525, ...	-822	57	0.026782
17	POLYGON ((-73.75168 5.43414, -73.75168 5.52498...	-822	60	0.016956
18	POLYGON ((-73.66185 5.43414, -73.66185 5.52498...	-821	60	0.069146
19	POLYGON ((-73.57202 5.34332, -73.57202 5.43414...	-820	59	0.000045
20	POLYGON ((-73.48219 5.34332, -73.48219 5.43414...	-819	59	0.011419
21	POLYGON ((-73.48219 5.43414, -73.48219 5.52498...	-819	60	0.011162
22	POLYGON ((-73.2127 5.61584, -73.2127 5.70671, ...	-816	62	0.010351
23	POLYGON ((-73.2127 5.88849, -73.2127 5.9794, -...	-816	65	0.003728
24	POLYGON ((-73.03303 6.79829, -73.03303 6.88936...	-814	75	0.001425
25	POLYGON ((-72.9432 5.43414, -72.9432 5.52498, ...	-813	60	0.081713
26	POLYGON ((-72.9432 5.52498, -72.9432 5.61584, ...	-813	61	0.026764
27	POLYGON ((-72.9432 6.79829, -72.9432 6.88936, ...	-813	75	0.001119
28	POLYGON ((-72.85337 5.43414, -72.85337 5.52498...	-812	60	0.095186
29	POLYGON ((-72.85337 5.52498, -72.85337 5.61584...	-812	61	0.350842
30	POLYGON ((-72.85337 6.6162, -72.85337 6.70724,...	-812	73	0.000112
31	POLYGON ((-72.85337 6.70724, -72.85337 6.79829...	-812	74	0.002558
32	POLYGON ((-72.31438 5.9794, -72.31438 6.07032,...	-806	66	0.005352

The column Laguna_Tectonica 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(1.5094722890650065)

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	Laguna_Tectonica	cumulative_area
19	POLYGON ((-73.57202 5.34332, -73.57202 5.43414...	-820	59	0.000045	0.000045
30	POLYGON ((-72.85337 6.6162, -72.85337 6.70724,...	-812	73	0.000112	0.000157
27	POLYGON ((-72.9432 6.79829, -72.9432 6.88936, ...	-813	75	0.001119	0.001276
24	POLYGON ((-73.03303 6.79829, -73.03303 6.88936...	-814	75	0.001425	0.002701
31	POLYGON ((-72.85337 6.70724, -72.85337 6.79829...	-812	74	0.002558	0.005259
12	POLYGON ((-73.84152 5.1617, -73.84152 5.2525, ...	-823	57	0.002626	0.007884
4	POLYGON ((-75.54832 4.98013, -75.54832 5.07091...	-842	55	0.002874	0.010759
23	POLYGON ((-73.2127 5.88849, -73.2127 5.9794, -...	-816	65	0.003728	0.014487
5	POLYGON ((-74.20084 4.70788, -74.20084 4.79862...	-827	52	0.005083	0.019570
32	POLYGON ((-72.31438 5.9794, -72.31438 6.07032,...	-806	66	0.005352	0.024923
6	POLYGON ((-74.11101 4.79862, -74.11101 4.88937...	-826	53	0.006178	0.031101
13	POLYGON ((-73.84152 5.2525, -73.84152 5.34332,...	-823	58	0.009160	0.040261
22	POLYGON ((-73.2127 5.61584, -73.2127 5.70671, ...	-816	62	0.010351	0.050612
21	POLYGON ((-73.48219 5.43414, -73.48219 5.52498...	-819	60	0.011162	0.061774
20	POLYGON ((-73.48219 5.34332, -73.48219 5.43414...	-819	59	0.011419	0.073193
8	POLYGON ((-73.93135 4.70788, -73.93135 4.79862...	-824	52	0.012706	0.085900
1	POLYGON ((-77.16528 1.08531, -77.16528 1.17576...	-860	12	0.013276	0.099176
7	POLYGON ((-73.93135 4.61715, -73.93135 4.70788...	-824	51	0.013624	0.112799
17	POLYGON ((-73.75168 5.43414, -73.75168 5.52498...	-822	60	0.016956	0.129755
26	POLYGON ((-72.9432 5.52498, -72.9432 5.61584, ...	-813	61	0.026764	0.156520
16	POLYGON ((-73.75168 5.1617, -73.75168 5.2525, ...	-822	57	0.026782	0.183301
10	POLYGON ((-73.93135 5.1617, -73.93135 5.2525, ...	-824	57	0.036910	0.220212
0	POLYGON ((-77.16528 0.99486, -77.16528 1.08531...	-860	11	0.046567	0.266779
9	POLYGON ((-73.93135 5.07091, -73.93135 5.1617,...	-824	56	0.049776	0.316555
14	POLYGON ((-73.75168 4.88937, -73.75168 4.98013...	-822	54	0.066568	0.383123
18	POLYGON ((-73.66185 5.43414, -73.66185 5.52498...	-821	60	0.069146	0.452270
11	POLYGON ((-73.84152 4.88937, -73.84152 4.98013...	-823	54	0.069430	0.521700
25	POLYGON ((-72.9432 5.43414, -72.9432 5.52498, ...	-813	60	0.081713	0.603413
15	POLYGON ((-73.75168 4.98013, -73.75168 5.07091...	-822	55	0.088427	0.691840
28	POLYGON ((-72.85337 5.43414, -72.85337 5.52498...	-812	60	0.095186	0.787026
2	POLYGON ((-77.07545 0.99486, -77.07545 1.08531...	-859	11	0.129713	0.916739
3	POLYGON ((-77.07545 1.08531, -77.07545 1.17576...	-859	12	0.241891	1.158630
29	POLYGON ((-72.85337 5.52498, -72.85337 5.61584...	-812	61	0.350842	1.509472

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	Laguna_Tectonica	cumulative_area	cumulative_proportion
19	POLYGON ((-73.57202 5.34332, -73.57202 5.43414...	-820	59	0.000045	0.000045	0.000030
30	POLYGON ((-72.85337 6.6162, -72.85337 6.70724,...	-812	73	0.000112	0.000157	0.000104
27	POLYGON ((-72.9432 6.79829, -72.9432 6.88936, ...	-813	75	0.001119	0.001276	0.000845
24	POLYGON ((-73.03303 6.79829, -73.03303 6.88936...	-814	75	0.001425	0.002701	0.001789
31	POLYGON ((-72.85337 6.70724, -72.85337 6.79829...	-812	74	0.002558	0.005259	0.003484
12	POLYGON ((-73.84152 5.1617, -73.84152 5.2525, ...	-823	57	0.002626	0.007884	0.005223
4	POLYGON ((-75.54832 4.98013, -75.54832 5.07091...	-842	55	0.002874	0.010759	0.007128
23	POLYGON ((-73.2127 5.88849, -73.2127 5.9794, -...	-816	65	0.003728	0.014487	0.009598
5	POLYGON ((-74.20084 4.70788, -74.20084 4.79862...	-827	52	0.005083	0.019570	0.012965
32	POLYGON ((-72.31438 5.9794, -72.31438 6.07032,...	-806	66	0.005352	0.024923	0.016511
6	POLYGON ((-74.11101 4.79862, -74.11101 4.88937...	-826	53	0.006178	0.031101	0.020604
13	POLYGON ((-73.84152 5.2525, -73.84152 5.34332,...	-823	58	0.009160	0.040261	0.026672
22	POLYGON ((-73.2127 5.61584, -73.2127 5.70671, ...	-816	62	0.010351	0.050612	0.033530
21	POLYGON ((-73.48219 5.43414, -73.48219 5.52498...	-819	60	0.011162	0.061774	0.040924
20	POLYGON ((-73.48219 5.34332, -73.48219 5.43414...	-819	59	0.011419	0.073193	0.048489
8	POLYGON ((-73.93135 4.70788, -73.93135 4.79862...	-824	52	0.012706	0.085900	0.056907
1	POLYGON ((-77.16528 1.08531, -77.16528 1.17576...	-860	12	0.013276	0.099176	0.065702
7	POLYGON ((-73.93135 4.61715, -73.93135 4.70788...	-824	51	0.013624	0.112799	0.074728
17	POLYGON ((-73.75168 5.43414, -73.75168 5.52498...	-822	60	0.016956	0.129755	0.085961
26	POLYGON ((-72.9432 5.52498, -72.9432 5.61584, ...	-813	61	0.026764	0.156520	0.103692
16	POLYGON ((-73.75168 5.1617, -73.75168 5.2525, ...	-822	57	0.026782	0.183301	0.121434
10	POLYGON ((-73.93135 5.1617, -73.93135 5.2525, ...	-824	57	0.036910	0.220212	0.145887
0	POLYGON ((-77.16528 0.99486, -77.16528 1.08531...	-860	11	0.046567	0.266779	0.176736
9	POLYGON ((-73.93135 5.07091, -73.93135 5.1617,...	-824	56	0.049776	0.316555	0.209713
14	POLYGON ((-73.75168 4.88937, -73.75168 4.98013...	-822	54	0.066568	0.383123	0.253813
18	POLYGON ((-73.66185 5.43414, -73.66185 5.52498...	-821	60	0.069146	0.452270	0.299621
11	POLYGON ((-73.84152 4.88937, -73.84152 4.98013...	-823	54	0.069430	0.521700	0.345617
25	POLYGON ((-72.9432 5.43414, -72.9432 5.52498, ...	-813	60	0.081713	0.603413	0.399751
15	POLYGON ((-73.75168 4.98013, -73.75168 5.07091...	-822	55	0.088427	0.691840	0.458332
28	POLYGON ((-72.85337 5.43414, -72.85337 5.52498...	-812	60	0.095186	0.787026	0.521391
2	POLYGON ((-77.07545 0.99486, -77.07545 1.08531...	-859	11	0.129713	0.916739	0.607324
3	POLYGON ((-77.07545 1.08531, -77.07545 1.17576...	-859	12	0.241891	1.158630	0.767573
29	POLYGON ((-72.85337 5.52498, -72.85337 5.61584...	-812	61	0.350842	1.509472	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 25 cells

AOO Calculation (direct call)

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

AOO: 25 grid cells

Criterion B Summary

Criterion B status (spatial)

Vulnerable (VU) — Laguna Tectonica (Laguna Tectonica), index 60

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	91019 km²	Least Concern (LC)
B2	AOO	25 cells	Vulnerable (VU)
Overall B	—	—	Vulnerable (VU)