Criterion B Details

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

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 Subxerofitia Basal 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() = 3968

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 560493.4 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 Subxerofitia_Basal
0 POLYGON ((-77.43478 1.44714, -77.43478 1.53761... -863 16 0.051033
1 POLYGON ((-77.43478 1.53761, -77.43478 1.62808... -863 17 0.058148
2 POLYGON ((-77.43478 1.62808, -77.43478 1.71855... -863 18 0.000200
3 POLYGON ((-77.34495 1.44714, -77.34495 1.53761... -862 16 0.071470
4 POLYGON ((-77.34495 1.53761, -77.34495 1.62808... -862 17 0.075273
... ... ... ... ...
754 POLYGON ((-70.24826 6.88936, -70.24826 6.98044... -783 76 0.208206
755 POLYGON ((-70.15842 6.79829, -70.15842 6.88936... -782 75 0.482114
756 POLYGON ((-70.15842 6.88936, -70.15842 6.98044... -782 76 0.610837
757 POLYGON ((-70.06859 6.79829, -70.06859 6.88936... -781 75 0.006283
758 POLYGON ((-70.06859 6.88936, -70.06859 6.98044... -781 76 0.089861

759 rows × 4 columns

The column Subxerofitia_Basal 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(60.363905179632766)

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 Subxerofitia_Basal cumulative_area
32 POLYGON ((-76.89579 2.2615, -76.89579 2.352, -... -857 25 3.733686e-08 3.733686e-08
141 POLYGON ((-75.45848 2.352, -75.45848 2.44252, ... -841 26 6.803303e-06 6.840640e-06
505 POLYGON ((-74.02118 10.73256, -74.02118 10.824... -825 118 6.982803e-06 1.382344e-05
423 POLYGON ((-74.47034 9.9056, -74.47034 9.99738,... -830 109 7.302364e-06 2.112581e-05
403 POLYGON ((-74.56017 4.43572, -74.56017 4.52643... -831 49 2.325390e-05 4.437971e-05
... ... ... ... ... ...
749 POLYGON ((-70.33809 6.79829, -70.33809 6.88936... -784 75 5.973575e-01 5.766944e+01
756 POLYGON ((-70.15842 6.88936, -70.15842 6.98044... -782 76 6.108372e-01 5.828027e+01
750 POLYGON ((-70.33809 6.88936, -70.33809 6.98044... -784 76 6.326587e-01 5.891293e+01
747 POLYGON ((-70.42792 6.88936, -70.42792 6.98044... -785 76 6.394391e-01 5.955237e+01
753 POLYGON ((-70.24826 6.79829, -70.24826 6.88936... -783 75 8.115334e-01 6.036391e+01

759 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 Subxerofitia_Basal cumulative_area cumulative_proportion
32 POLYGON ((-76.89579 2.2615, -76.89579 2.352, -... -857 25 3.733686e-08 3.733686e-08 6.185295e-10
141 POLYGON ((-75.45848 2.352, -75.45848 2.44252, ... -841 26 6.803303e-06 6.840640e-06 1.133234e-07
505 POLYGON ((-74.02118 10.73256, -74.02118 10.824... -825 118 6.982803e-06 1.382344e-05 2.290018e-07
423 POLYGON ((-74.47034 9.9056, -74.47034 9.99738,... -830 109 7.302364e-06 2.112581e-05 3.499742e-07
403 POLYGON ((-74.56017 4.43572, -74.56017 4.52643... -831 49 2.325390e-05 4.437971e-05 7.352027e-07
... ... ... ... ... ... ...
749 POLYGON ((-70.33809 6.79829, -70.33809 6.88936... -784 75 5.973575e-01 5.766944e+01 9.553629e-01
756 POLYGON ((-70.15842 6.88936, -70.15842 6.98044... -782 76 6.108372e-01 5.828027e+01 9.654822e-01
750 POLYGON ((-70.33809 6.88936, -70.33809 6.98044... -784 76 6.326587e-01 5.891293e+01 9.759629e-01
747 POLYGON ((-70.42792 6.88936, -70.42792 6.98044... -785 76 6.394391e-01 5.955237e+01 9.865560e-01
753 POLYGON ((-70.24826 6.79829, -70.24826 6.88936... -783 75 8.115334e-01 6.036391e+01 1.000000e+00

759 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 593 cells

AOO Calculation (direct call)

if has_data:
    aoo_count = ecosystem.aoo
    print(f'AOO: {aoo_count} grid cells')
AOO: 593 grid cells

Criterion B Summary

Criterion B status (spatial)
Least Concern (LC) — Subxerofitia Basal (Subxerofitia Basal), index 72

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 560493 km² Least Concern (LC)
B2 AOO 593 cells Least Concern (LC)
Overall B — — Least Concern (LC)