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title GEO-INFER-BIO: Biodiversity and Ecology
description Species distribution, habitat analysis, and conservation planning
purpose Enable biodiversity monitoring and ecological analysis
module_type Domain Application
status Alpha
last_updated 2026-02-25
dependencies
SPACE
DATA
TIME
compatibility
GEO-INFER-SPACE
GEO-INFER-DATA
GEO-INFER-FOREST
tags
biodiversity
ecology
species
habitat
conservation
difficulty Intermediate
estimated_time 45

GEO-INFER-BIO: Biodiversity and Ecology

Overview

GEO-INFER-BIO provides biodiversity analysis, species distribution modeling (SDM), habitat quality assessment, ecological connectivity analysis, and conservation planning tools. The module supports the full workflow from species occurrence records through spatial biodiversity mapping to corridor identification and protected area prioritization. It operates on H3 hexagonal grids for consistent spatial aggregation and integrates with Bayesian methods for uncertainty-aware species distribution predictions.

Core Objectives

  • Species Richness Mapping: Aggregate species occurrence records onto H3 hexagonal cells to produce spatially explicit richness maps with rarefaction-corrected estimates
  • Habitat Connectivity Analysis: Identify viable wildlife corridors between habitat patches using graph-theoretic methods (least-cost paths, circuit theory) with species-specific dispersal parameters
  • Bayesian Species Distribution Modeling: Predict species occurrence probability across unsampled areas using environmental covariates and Bayesian inference (via GEO-INFER-BAYES)
  • Conservation Prioritization: Rank candidate areas for protection using systematic conservation planning methods that balance biodiversity value, cost, and connectivity

Features

Species Distribution Modeling

from geo_infer_bio import SpeciesModeler

# Model species distribution
modeler = SpeciesModeler()

distribution = modeler.predict(
    species="ursus_americanus",
    occurrences=sighting_data,
    predictors=environmental_layers
)

print(f"Suitable habitat: {distribution.area_km2} km2")

Habitat Analysis

from geo_infer_bio import HabitatAnalyzer

# Analyze habitat
analyzer = HabitatAnalyzer()

quality = analyzer.assess(
    area=study_region,
    metrics=["connectivity", "fragmentation"]
)

Conservation Planning

from geo_infer_bio import ConservationPlanner

# Prioritize conservation
planner = ConservationPlanner()

plan = planner.prioritize(
    targets=species_targets,
    cost=land_costs
)

Biodiversity Metrics

from geo_infer_bio import BiodiversityCalculator

# Calculate diversity
calc = BiodiversityCalculator()

metrics = calc.compute(
    data=survey_data,
    indices=["shannon", "simpson"]
)

API Reference

Class / Function Description
BiodiversityAnalyzer(h3_resolution) Primary analyzer for spatial biodiversity metrics on H3 grids
SpeciesDistributionModel(method, covariates) SDM with MaxEnt, GLM, or Bayesian methods
HabitatConnectivityAnalyzer() Graph-based corridor detection between habitat patches
BiodiversityAnalyzer.compute_species_richness(occurrences) Returns H3-aggregated species richness GeoDataFrame
HabitatConnectivityAnalyzer.find_corridors(patches, species_requirements) Returns GeoDataFrame of viable corridors with cost-distance values
SpeciesDistributionModel.fit(occurrences, environment) Fits SDM to occurrence and environmental data
SpeciesDistributionModel.predict(grid) Returns occurrence probability predictions across a spatial grid
BiodiversityAnalyzer.shannon_index(counts) Computes Shannon diversity index from species abundance counts

Methods

Method Application
MaxEnt Species distribution
Marxan Conservation planning
Circuitscape Connectivity

Working Code Examples

Example 1: Species Richness Estimation

from geo_infer_bio.core.biodiversity_analyzer import BiodiversityAnalyzer
import geopandas as gpd
from shapely.geometry import Point
import numpy as np

# Species occurrence records
np.random.seed(42)
n_records = 60
species_list = ["Quercus robur", "Fagus sylvatica", "Pinus sylvestris"] * 20
lats = 47.5 + np.random.uniform(0, 0.5, n_records)
lngs = -122.5 + np.random.uniform(0, 0.5, n_records)

occurrences = gpd.GeoDataFrame(
    {"species": species_list},
    geometry=[Point(lng, lat) for lat, lng in zip(lats, lngs)],
    crs="EPSG:4326",
)

analyzer = BiodiversityAnalyzer(h3_resolution=8)
richness_map = analyzer.compute_species_richness(occurrences)
print(f"Max species richness: {richness_map['richness'].max()} species")

Example 2: Habitat Connectivity Analysis

from geo_infer_bio.core.connectivity_analyzer import HabitatConnectivityAnalyzer
import geopandas as gpd

analyzer = HabitatConnectivityAnalyzer()
habitat_patches = gpd.read_file("habitat_polygons.geojson")
corridors = analyzer.find_corridors(
    patches=habitat_patches,
    species_requirements={"min_area_ha": 5.0, "max_gap_km": 2.0},
)
print(f"Found {len(corridors)} viable wildlife corridors")

Integration

GEO-INFER-BIO integrates with the following modules:

Module Direction Purpose
GEO-INFER-SPACE BIO <-- SPACE H3 grid operations for spatial biodiversity aggregation
GEO-INFER-DATA BIO <-- DATA Species occurrence datasets and environmental raster layers
GEO-INFER-BAYES BIO <-- BAYES Bayesian inference for uncertainty-aware species distribution models
GEO-INFER-FOREST BIO <-> FOREST Forest habitat structure informs biodiversity; biodiversity metrics guide forestry decisions
GEO-INFER-MARINE BIO <-> MARINE Marine biodiversity assessments and coastal habitat connectivity
GEO-INFER-CLIMATE BIO <-- CLIMATE Climate variables as environmental covariates for SDMs

Data flow: DATA provides occurrence records and environmental layers. SPACE provides H3 spatial operations. BAYES powers uncertainty quantification in SDMs. BIO produces richness maps, corridors, and conservation priorities that feed into FOREST and MARINE for domain-specific management.

Installation

uv pip install -e "./GEO-INFER-BIO"

Testing

# Run all BIO tests
uv run python -m pytest GEO-INFER-BIO/tests/ -v

# Run unit tests only
uv run python -m pytest GEO-INFER-BIO/tests/unit/ -v

# Run with coverage
uv run python -m pytest GEO-INFER-BIO/tests/ --cov=GEO-INFER-BIO/src --cov-report=html

Documentation Hub

Full framework documentation, guides, and tutorials are available in the GEO-INFER-INTRA documentation hub.

Resource Description
Getting Started Installation, first steps, quick start guides
Module Overview All 44 modules with descriptions and use cases
Integration Patterns How modules work together
Testing Guide Testing standards, fixtures, CI integration
API Standards Code conventions and contribution guidelines

Status: Alpha

Last Updated: 2026-02-25