Criterion B Details

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

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 Subandino Humedo 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() = 7501

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(

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 558654.8 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:

  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]))

Calculate grid cell area and total area

  1. 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_Subandino_Humedo
0 POLYGON ((-78.24326 0.99486, -78.24326 1.08531... -872 11 0.046352
1 POLYGON ((-78.24326 1.08531, -78.24326 1.17576... -872 12 0.064043
2 POLYGON ((-78.24326 1.26622, -78.24326 1.35668... -872 14 0.001443
3 POLYGON ((-78.15343 0.90441, -78.15343 0.99486... -871 10 0.055749
4 POLYGON ((-78.15343 0.99486, -78.15343 1.08531... -871 11 0.465561
... ... ... ... ...
1921 POLYGON ((-71.86522 6.70724, -71.86522 6.79829... -801 74 0.399040
1922 POLYGON ((-71.86522 6.79829, -71.86522 6.88936... -801 75 0.047762
1923 POLYGON ((-71.77539 6.43418, -71.77539 6.52518... -800 71 0.021660
1924 POLYGON ((-71.77539 6.52518, -71.77539 6.6162,... -800 72 0.171480
1925 POLYGON ((-71.77539 6.6162, -71.77539 6.70724,... -800 73 0.001416

1926 rows × 4 columns

The column Bosque_Subandino_Humedo 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(302.17043309123176)

Calculate cumulative area

  1. 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_Subandino_Humedo cumulative_area
805 POLYGON ((-75.63815 5.70671, -75.63815 5.79759... -843 63 1.421576e-07 1.421576e-07
1197 POLYGON ((-74.65 3.43861, -74.65 3.52921, -74.... -832 38 3.702603e-07 5.124179e-07
1805 POLYGON ((-72.67371 6.43418, -72.67371 6.52518... -810 71 3.738419e-06 4.250837e-06
544 POLYGON ((-76.0873 1.53761, -76.0873 1.62808, ... -848 17 5.299667e-06 9.550504e-06
1205 POLYGON ((-74.65 4.98013, -74.65 5.07091, -74.... -832 55 6.843586e-06 1.639409e-05
... ... ... ... ... ...
472 POLYGON ((-76.26697 5.79759, -76.26697 5.88849... -850 64 9.259860e-01 2.983673e+02
396 POLYGON ((-76.3568 1.26622, -76.3568 1.35668, ... -851 14 9.289565e-01 2.992963e+02
252 POLYGON ((-76.80596 3.43861, -76.80596 3.52921... -856 38 9.381390e-01 3.002344e+02
1215 POLYGON ((-74.56017 2.89518, -74.56017 2.98573... -831 32 9.637192e-01 3.011981e+02
1390 POLYGON ((-73.93135 2.98573, -73.93135 3.07629... -824 33 9.723234e-01 3.021704e+02

1926 rows × 5 columns

Calculate cumulative proportion

  1. 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_Subandino_Humedo cumulative_area cumulative_proportion
805 POLYGON ((-75.63815 5.70671, -75.63815 5.79759... -843 63 1.421576e-07 1.421576e-07 4.704551e-10
1197 POLYGON ((-74.65 3.43861, -74.65 3.52921, -74.... -832 38 3.702603e-07 5.124179e-07 1.695791e-09
1805 POLYGON ((-72.67371 6.43418, -72.67371 6.52518... -810 71 3.738419e-06 4.250837e-06 1.406768e-08
544 POLYGON ((-76.0873 1.53761, -76.0873 1.62808, ... -848 17 5.299667e-06 9.550504e-06 3.160635e-08
1205 POLYGON ((-74.65 4.98013, -74.65 5.07091, -74.... -832 55 6.843586e-06 1.639409e-05 5.425445e-08
... ... ... ... ... ... ...
472 POLYGON ((-76.26697 5.79759, -76.26697 5.88849... -850 64 9.259860e-01 2.983673e+02 9.874139e-01
396 POLYGON ((-76.3568 1.26622, -76.3568 1.35668, ... -851 14 9.289565e-01 2.992963e+02 9.904882e-01
252 POLYGON ((-76.80596 3.43861, -76.80596 3.52921... -856 38 9.381390e-01 3.002344e+02 9.935929e-01
1215 POLYGON ((-74.56017 2.89518, -74.56017 2.98573... -831 32 9.637192e-01 3.011981e+02 9.967822e-01
1390 POLYGON ((-73.93135 2.98573, -73.93135 3.07629... -824 33 9.723234e-01 3.021704e+02 1.000000e+00

1926 rows × 6 columns

Count AOO cells

  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).
if has_data:
    aoo = len(gdf[gdf["cumulative_proportion"] > 0.01])
    print(f'AOO is {aoo} cells')
AOO is 1451 cells

AOO Calculation (direct call)

if has_data:
    aoo_count = ecosystem.aoo
    print(f'AOO: {aoo_count} grid cells')
AOO: 1451 grid cells

Criterion B Summary

Criterion B status (spatial)
Least Concern (LC) — Bosque Subandino Humedo (Bosque Subandino Humedo), index 37

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 558655 km² Least Concern (LC)
B2 AOO 1451 cells Least Concern (LC)
Overall B — — Least Concern (LC)