Criterion B Details

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

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 Inundable Andino 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() = 66

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 181653.0 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	Bosque_Inundable_Andino
0	POLYGON ((-77.88394 0.99486, -77.88394 1.08531...	-868	11	0.022855
1	POLYGON ((-77.43478 1.26622, -77.43478 1.35668...	-863	14	0.004137
2	POLYGON ((-77.34495 0.90441, -77.34495 0.99486...	-862	10	0.010015
3	POLYGON ((-77.25511 0.81396, -77.25511 0.90441...	-861	9	0.082246
4	POLYGON ((-77.25511 0.99486, -77.25511 1.08531...	-861	11	0.011497
5	POLYGON ((-77.25511 1.17576, -77.25511 1.26622...	-861	13	0.000236
6	POLYGON ((-77.25511 1.26622, -77.25511 1.35668...	-861	14	0.015273
7	POLYGON ((-77.16528 0.81396, -77.16528 0.90441...	-860	9	0.052396
8	POLYGON ((-77.16528 0.90441, -77.16528 0.99486...	-860	10	0.013287
9	POLYGON ((-77.16528 0.99486, -77.16528 1.08531...	-860	11	0.048936
10	POLYGON ((-77.16528 1.08531, -77.16528 1.17576...	-860	12	0.049835
11	POLYGON ((-77.16528 1.17576, -77.16528 1.26622...	-860	13	0.021080
12	POLYGON ((-77.16528 1.26622, -77.16528 1.35668...	-860	14	0.029331
13	POLYGON ((-77.07545 0.81396, -77.07545 0.90441...	-859	9	0.003693
14	POLYGON ((-77.07545 0.90441, -77.07545 0.99486...	-859	10	0.004551
15	POLYGON ((-77.07545 0.99486, -77.07545 1.08531...	-859	11	0.025461
16	POLYGON ((-77.07545 1.08531, -77.07545 1.17576...	-859	12	0.000733
17	POLYGON ((-77.07545 1.17576, -77.07545 1.26622...	-859	13	0.007551
18	POLYGON ((-76.26697 1.44714, -76.26697 1.53761...	-850	16	0.018038
19	POLYGON ((-76.26697 1.53761, -76.26697 1.62808...	-850	17	0.020906
20	POLYGON ((-76.17714 1.53761, -76.17714 1.62808...	-849	17	0.021058
21	POLYGON ((-76.17714 6.43418, -76.17714 6.52518...	-849	71	0.006379
22	POLYGON ((-76.0873 2.44252, -76.0873 2.53304, ...	-848	27	0.020533
23	POLYGON ((-76.0873 2.89518, -76.0873 2.98573, ...	-848	32	0.006813
24	POLYGON ((-76.0873 6.43418, -76.0873 6.52518, ...	-848	71	0.018738
25	POLYGON ((-76.0873 6.52518, -76.0873 6.6162, -...	-848	72	0.003087
26	POLYGON ((-76.0873 6.6162, -76.0873 6.70724, -...	-848	73	0.003982
27	POLYGON ((-75.99747 6.25222, -75.99747 6.34319...	-847	69	0.003690
28	POLYGON ((-75.99747 6.34319, -75.99747 6.43418...	-847	70	0.003308
29	POLYGON ((-75.90764 6.79829, -75.90764 6.88936...	-846	75	0.005507
30	POLYGON ((-75.72798 5.52498, -75.72798 5.61584...	-844	61	0.008599
31	POLYGON ((-75.72798 6.70724, -75.72798 6.79829...	-844	74	0.015397
32	POLYGON ((-75.09916 6.25222, -75.09916 6.34319...	-837	69	0.010680
33	POLYGON ((-72.31438 5.88849, -72.31438 5.9794,...	-806	65	0.016960
34	POLYGON ((-72.31438 5.9794, -72.31438 6.07032,...	-806	66	0.019251

The column Bosque_Inundable_Andino 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(0.6060387768810456)

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	Bosque_Inundable_Andino	cumulative_area
5	POLYGON ((-77.25511 1.17576, -77.25511 1.26622...	-861	13	0.000236	0.000236
16	POLYGON ((-77.07545 1.08531, -77.07545 1.17576...	-859	12	0.000733	0.000968
25	POLYGON ((-76.0873 6.52518, -76.0873 6.6162, -...	-848	72	0.003087	0.004055
28	POLYGON ((-75.99747 6.34319, -75.99747 6.43418...	-847	70	0.003308	0.007363
27	POLYGON ((-75.99747 6.25222, -75.99747 6.34319...	-847	69	0.003690	0.011054
13	POLYGON ((-77.07545 0.81396, -77.07545 0.90441...	-859	9	0.003693	0.014747
26	POLYGON ((-76.0873 6.6162, -76.0873 6.70724, -...	-848	73	0.003982	0.018729
1	POLYGON ((-77.43478 1.26622, -77.43478 1.35668...	-863	14	0.004137	0.022866
14	POLYGON ((-77.07545 0.90441, -77.07545 0.99486...	-859	10	0.004551	0.027417
29	POLYGON ((-75.90764 6.79829, -75.90764 6.88936...	-846	75	0.005507	0.032924
21	POLYGON ((-76.17714 6.43418, -76.17714 6.52518...	-849	71	0.006379	0.039303
23	POLYGON ((-76.0873 2.89518, -76.0873 2.98573, ...	-848	32	0.006813	0.046116
17	POLYGON ((-77.07545 1.17576, -77.07545 1.26622...	-859	13	0.007551	0.053666
30	POLYGON ((-75.72798 5.52498, -75.72798 5.61584...	-844	61	0.008599	0.062265
2	POLYGON ((-77.34495 0.90441, -77.34495 0.99486...	-862	10	0.010015	0.072280
32	POLYGON ((-75.09916 6.25222, -75.09916 6.34319...	-837	69	0.010680	0.082960
4	POLYGON ((-77.25511 0.99486, -77.25511 1.08531...	-861	11	0.011497	0.094457
8	POLYGON ((-77.16528 0.90441, -77.16528 0.99486...	-860	10	0.013287	0.107744
6	POLYGON ((-77.25511 1.26622, -77.25511 1.35668...	-861	14	0.015273	0.123017
31	POLYGON ((-75.72798 6.70724, -75.72798 6.79829...	-844	74	0.015397	0.138414
33	POLYGON ((-72.31438 5.88849, -72.31438 5.9794,...	-806	65	0.016960	0.155374
18	POLYGON ((-76.26697 1.44714, -76.26697 1.53761...	-850	16	0.018038	0.173412
24	POLYGON ((-76.0873 6.43418, -76.0873 6.52518, ...	-848	71	0.018738	0.192150
34	POLYGON ((-72.31438 5.9794, -72.31438 6.07032,...	-806	66	0.019251	0.211401
22	POLYGON ((-76.0873 2.44252, -76.0873 2.53304, ...	-848	27	0.020533	0.231934
19	POLYGON ((-76.26697 1.53761, -76.26697 1.62808...	-850	17	0.020906	0.252840
20	POLYGON ((-76.17714 1.53761, -76.17714 1.62808...	-849	17	0.021058	0.273899
11	POLYGON ((-77.16528 1.17576, -77.16528 1.26622...	-860	13	0.021080	0.294979
0	POLYGON ((-77.88394 0.99486, -77.88394 1.08531...	-868	11	0.022855	0.317833
15	POLYGON ((-77.07545 0.99486, -77.07545 1.08531...	-859	11	0.025461	0.343295
12	POLYGON ((-77.16528 1.26622, -77.16528 1.35668...	-860	14	0.029331	0.372625
9	POLYGON ((-77.16528 0.99486, -77.16528 1.08531...	-860	11	0.048936	0.421562
10	POLYGON ((-77.16528 1.08531, -77.16528 1.17576...	-860	12	0.049835	0.471397
7	POLYGON ((-77.16528 0.81396, -77.16528 0.90441...	-860	9	0.052396	0.523793
3	POLYGON ((-77.25511 0.81396, -77.25511 0.90441...	-861	9	0.082246	0.606039

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	Bosque_Inundable_Andino	cumulative_area	cumulative_proportion
5	POLYGON ((-77.25511 1.17576, -77.25511 1.26622...	-861	13	0.000236	0.000236	0.000389
16	POLYGON ((-77.07545 1.08531, -77.07545 1.17576...	-859	12	0.000733	0.000968	0.001598
25	POLYGON ((-76.0873 6.52518, -76.0873 6.6162, -...	-848	72	0.003087	0.004055	0.006692
28	POLYGON ((-75.99747 6.34319, -75.99747 6.43418...	-847	70	0.003308	0.007363	0.012150
27	POLYGON ((-75.99747 6.25222, -75.99747 6.34319...	-847	69	0.003690	0.011054	0.018240
13	POLYGON ((-77.07545 0.81396, -77.07545 0.90441...	-859	9	0.003693	0.014747	0.024334
26	POLYGON ((-76.0873 6.6162, -76.0873 6.70724, -...	-848	73	0.003982	0.018729	0.030904
1	POLYGON ((-77.43478 1.26622, -77.43478 1.35668...	-863	14	0.004137	0.022866	0.037730
14	POLYGON ((-77.07545 0.90441, -77.07545 0.99486...	-859	10	0.004551	0.027417	0.045240
29	POLYGON ((-75.90764 6.79829, -75.90764 6.88936...	-846	75	0.005507	0.032924	0.054327
21	POLYGON ((-76.17714 6.43418, -76.17714 6.52518...	-849	71	0.006379	0.039303	0.064852
23	POLYGON ((-76.0873 2.89518, -76.0873 2.98573, ...	-848	32	0.006813	0.046116	0.076094
17	POLYGON ((-77.07545 1.17576, -77.07545 1.26622...	-859	13	0.007551	0.053666	0.088553
30	POLYGON ((-75.72798 5.52498, -75.72798 5.61584...	-844	61	0.008599	0.062265	0.102741
2	POLYGON ((-77.34495 0.90441, -77.34495 0.99486...	-862	10	0.010015	0.072280	0.119266
32	POLYGON ((-75.09916 6.25222, -75.09916 6.34319...	-837	69	0.010680	0.082960	0.136889
4	POLYGON ((-77.25511 0.99486, -77.25511 1.08531...	-861	11	0.011497	0.094457	0.155860
8	POLYGON ((-77.16528 0.90441, -77.16528 0.99486...	-860	10	0.013287	0.107744	0.177784
6	POLYGON ((-77.25511 1.26622, -77.25511 1.35668...	-861	14	0.015273	0.123017	0.202986
31	POLYGON ((-75.72798 6.70724, -75.72798 6.79829...	-844	74	0.015397	0.138414	0.228392
33	POLYGON ((-72.31438 5.88849, -72.31438 5.9794,...	-806	65	0.016960	0.155374	0.256376
18	POLYGON ((-76.26697 1.44714, -76.26697 1.53761...	-850	16	0.018038	0.173412	0.286140
24	POLYGON ((-76.0873 6.43418, -76.0873 6.52518, ...	-848	71	0.018738	0.192150	0.317059
34	POLYGON ((-72.31438 5.9794, -72.31438 6.07032,...	-806	66	0.019251	0.211401	0.348825
22	POLYGON ((-76.0873 2.44252, -76.0873 2.53304, ...	-848	27	0.020533	0.231934	0.382705
19	POLYGON ((-76.26697 1.53761, -76.26697 1.62808...	-850	17	0.020906	0.252840	0.417201
20	POLYGON ((-76.17714 1.53761, -76.17714 1.62808...	-849	17	0.021058	0.273899	0.451949
11	POLYGON ((-77.16528 1.17576, -77.16528 1.26622...	-860	13	0.021080	0.294979	0.486733
0	POLYGON ((-77.88394 0.99486, -77.88394 1.08531...	-868	11	0.022855	0.317833	0.524444
15	POLYGON ((-77.07545 0.99486, -77.07545 1.08531...	-859	11	0.025461	0.343295	0.566456
12	POLYGON ((-77.16528 1.26622, -77.16528 1.35668...	-860	14	0.029331	0.372625	0.614854
9	POLYGON ((-77.16528 0.99486, -77.16528 1.08531...	-860	11	0.048936	0.421562	0.695602
10	POLYGON ((-77.16528 1.08531, -77.16528 1.17576...	-860	12	0.049835	0.471397	0.777833
7	POLYGON ((-77.16528 0.81396, -77.16528 0.90441...	-860	9	0.052396	0.523793	0.864289
3	POLYGON ((-77.25511 0.81396, -77.25511 0.90441...	-861	9	0.082246	0.606039	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 32 cells

AOO Calculation (direct call)

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

AOO: 32 grid cells

Criterion B Summary

Criterion B status (spatial)

Vulnerable (VU) — Bosque Inundable Andino (Bosque Inundable Andino), index 32

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	181653 km²	Least Concern (LC)
B2	AOO	32 cells	Vulnerable (VU)
Overall B	—	—	Vulnerable (VU)