Criterion B Details

# Default ecosystem code for template development.
# This line is replaced by build_ecosystem_pages.py for each ecosystem.
ecosystem_code = 'Arbustal Inundable Costero'

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 Arbustal Inundable Costero 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() = 113

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 614228.2 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:

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	Arbustal_Inundable_Costero
0	POLYGON ((-78.33309 2.62356, -78.33309 2.7141,...	-873	29	0.045237
1	POLYGON ((-78.24326 2.62356, -78.24326 2.7141,...	-872	29	0.004482
2	POLYGON ((-77.16528 8.34893, -77.16528 8.44032...	-860	92	0.006048
3	POLYGON ((-77.16528 8.44032, -77.16528 8.53173...	-860	93	0.003364
4	POLYGON ((-77.07545 8.34893, -77.07545 8.44032...	-859	92	0.002004
...	...	...	...	...
60	POLYGON ((-70.24826 5.79759, -70.24826 5.88849...	-783	64	0.001256
61	POLYGON ((-70.15842 5.61584, -70.15842 5.70671...	-782	62	0.007092
62	POLYGON ((-70.15842 5.70671, -70.15842 5.79759...	-782	63	0.005158
63	POLYGON ((-70.06859 5.70671, -70.06859 5.79759...	-781	63	0.001160
64	POLYGON ((-69.7991 6.25222, -69.7991 6.34319, ...	-778	69	0.020059

65 rows × 4 columns

The column Arbustal_Inundable_Costero 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(2.061579232356151)

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	Arbustal_Inundable_Costero	cumulative_area
51	POLYGON ((-70.51775 5.61584, -70.51775 5.70671...	-786	62	0.000139	0.000139
45	POLYGON ((-71.14657 12.11584, -71.14657 12.208...	-793	133	0.000140	0.000279
24	POLYGON ((-73.66185 11.19293, -73.66185 11.285...	-821	123	0.000358	0.000637
30	POLYGON ((-73.03303 11.28509, -73.03303 11.377...	-814	124	0.000574	0.001211
47	POLYGON ((-71.14657 12.30079, -71.14657 12.393...	-793	135	0.000647	0.001858
...	...	...	...	...	...
56	POLYGON ((-70.42792 5.70671, -70.42792 5.79759...	-785	63	0.117546	1.218322
22	POLYGON ((-74.20084 10.82458, -74.20084 10.916...	-827	119	0.182995	1.401317
16	POLYGON ((-74.38051 10.64057, -74.38051 10.732...	-829	117	0.185992	1.587309
44	POLYGON ((-71.14657 12.02342, -71.14657 12.115...	-793	132	0.186599	1.773908
19	POLYGON ((-74.29067 10.73256, -74.29067 10.824...	-828	118	0.287671	2.061579

65 rows × 5 columns

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	Arbustal_Inundable_Costero	cumulative_area	cumulative_proportion
51	POLYGON ((-70.51775 5.61584, -70.51775 5.70671...	-786	62	0.000139	0.000139	0.000067
45	POLYGON ((-71.14657 12.11584, -71.14657 12.208...	-793	133	0.000140	0.000279	0.000135
24	POLYGON ((-73.66185 11.19293, -73.66185 11.285...	-821	123	0.000358	0.000637	0.000309
30	POLYGON ((-73.03303 11.28509, -73.03303 11.377...	-814	124	0.000574	0.001211	0.000587
47	POLYGON ((-71.14657 12.30079, -71.14657 12.393...	-793	135	0.000647	0.001858	0.000901
...	...	...	...	...	...	...
56	POLYGON ((-70.42792 5.70671, -70.42792 5.79759...	-785	63	0.117546	1.218322	0.590966
22	POLYGON ((-74.20084 10.82458, -74.20084 10.916...	-827	119	0.182995	1.401317	0.679730
16	POLYGON ((-74.38051 10.64057, -74.38051 10.732...	-829	117	0.185992	1.587309	0.769948
44	POLYGON ((-71.14657 12.02342, -71.14657 12.115...	-793	132	0.186599	1.773908	0.860461
19	POLYGON ((-74.29067 10.73256, -74.29067 10.824...	-828	118	0.287671	2.061579	1.000000

65 rows × 6 columns

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 51 cells

AOO Calculation (direct call)

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

AOO: 51 grid cells

Criterion B Summary

Criterion B status (spatial)

Least Concern (LC) — Arbustal Inundable Costero (Arbustal Inundable Costero), index 19

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	614228 km²	Least Concern (LC)
B2	AOO	51 cells	Least Concern (LC)
Overall B	—	—	Least Concern (LC)