Apply conservation biology methods, databases, and assessment tools
A skill for conducting conservation biology research, covering species assessment methods, habitat modeling, population viability analysis, key biodiversity databases, and frameworks for conservation prioritization.
Extinction Risk Categories (from highest to lowest):
EX - Extinct
EW - Extinct in the Wild
CR - Critically Endangered
EN - Endangered
VU - Vulnerable
NT - Near Threatened
LC - Least Concern
DD - Data Deficient
NE - Not Evaluated
Classification criteria (any one triggers the category):
A: Population size reduction
B: Geographic range (extent of occurrence, area of occupancy)
C: Small population size and decline
D: Very small or restricted population
E: Quantitative extinction probability analysis
import os
import json
import urllib.request
def get_species_assessment(species_name: str) -> dict:
"""
Retrieve IUCN Red List assessment for a species.
Args:
species_name: Scientific name (e.g., 'Panthera tigris')
"""
api_token = os.environ["IUCN_API_TOKEN"]
encoded_name = urllib.parse.quote(species_name)
url = f"https://apiv3.iucnredlist.org/api/v3/species/{encoded_name}?token={api_token}"
req = urllib.request.Request(url)
response = urllib.request.urlopen(req)
data = json.loads(response.read())
if data.get("result"):
species = data["result"][0]
return {
"scientific_name": species.get("scientific_name"),
"common_name": species.get("main_common_name"),
"category": species.get("category"),
"population_trend": species.get("population_trend"),
"assessment_date": species.get("assessment_date"),
"criteria": species.get("criteria")
}
return {"error": "Species not found in IUCN Red List"}
def sdm_workflow(occurrence_data: list[tuple],
environmental_layers: list[str],
method: str = "maxent") -> dict:
"""
Outline a species distribution modeling workflow.
Args:
occurrence_data: List of (latitude, longitude) tuples
environmental_layers: List of environmental raster file paths
method: Modeling method (maxent, glm, rf, boosted_regression)
"""
return {
"data_preparation": {
"occurrences": len(occurrence_data),
"environmental_variables": len(environmental_layers),
"steps": [
"Clean occurrence records (remove duplicates, spatial outliers)",
"Thin records to reduce spatial autocorrelation (1 per grid cell)",
"Generate pseudo-absences or background points",
"Extract environmental values at occurrence/absence points",
"Check for multicollinearity (VIF < 10)"
]
},
"modeling": {
"method": method,
"methods_available": {
"maxent": "Maximum entropy (presence-only, widely used)",
"glm": "Generalized linear model (presence-absence)",
"rf": "Random forest (handles non-linearities)",
"boosted_regression": "BRT (good predictive performance)",
"ensemble": "Combine multiple methods for robustness"
}
},
"validation": {
"metrics": ["AUC-ROC", "TSS (True Skill Statistic)", "Boyce Index"],
"methods": [
"k-fold cross-validation",
"Spatial block cross-validation (reduces spatial autocorrelation bias)",
"Independent validation dataset (ideal)"
]
},
"projection": {
"current": "Map current suitable habitat",
"future": "Project under climate change scenarios (SSP1-2.6, SSP5-8.5)",
"note": "Report uncertainty across climate models and scenarios"
}
}
PVA simulates population dynamics to estimate extinction probability
over a given time horizon.
Key inputs:
- Current population size and structure (age/stage)
- Vital rates: survival, fecundity (with variance)
- Carrying capacity and density dependence
- Environmental and demographic stochasticity
- Catastrophe frequency and severity
- Genetic factors (inbreeding depression)
Common software:
- Vortex: Individual-based PVA simulation
- RAMAS GIS: Spatially explicit PVA
- R packages: popbio, lefko3, Compadre for matrix models
Outputs:
- Probability of extinction over T years
- Expected minimum population size
- Population growth rate (lambda) and its variance
- Sensitivity of persistence to management actions
| Database | Content | Access |
|---|---|---|
| GBIF | 2+ billion species occurrence records | Free (gbif.org) |
| IUCN Red List | Species assessments and distributions | API + download |
| BIEN | Plant occurrence and trait data (Americas) | Free (biendata.org) |
| eBird | Bird observations worldwide | Free (ebird.org) |
| Protected Planet (WDPA) | Global protected area boundaries | Free (protectedplanet.net) |
| WorldClim | Current and future climate layers | Free (worldclim.org) |
| CHELSA | High-resolution climate data | Free (chelsa-climate.org) |
| Global Forest Watch | Forest cover change | Free (globalforestwatch.org) |
Systematic Conservation Planning (Margules & Pressey):
1. Compile data on biodiversity features and their distributions
2. Set conservation targets for each feature
3. Review existing protected area coverage
4. Select additional areas using optimization (e.g., Marxan, Zonation)
5. Implement and manage conservation actions
6. Monitor outcomes and adapt
Key principles:
- Representativeness: All species/habitats should be represented
- Complementarity: Each new area should add maximum new coverage
- Efficiency: Minimize cost while meeting targets
- Connectivity: Corridors link protected areas
Report species names with taxonomic authority and reference to the taxonomic standard used (e.g., ITIS, Catalogue of Life). Deposit occurrence data in GBIF. Follow the Darwin Core standard for biodiversity data. Use IUCN criteria language when discussing threat status. Clearly state conservation implications and management recommendations, as conservation biology is an applied and mission-driven discipline.