Conservation Biology GuideSAFE

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---
name: conservation-biology-guide
description: "Apply conservation biology methods, databases, and assessment tools"
metadata:
  openclaw:
    emoji: "🌳"
    category: "domains"
    subcategory: "ecology"
    keywords: ["conservation biology", "biodiversity", "IUCN Red List", "species assessment", "habitat modeling", "wildlife"]
    source: "wentor-research-plugins"
---

# Conservation Biology Guide

A skill for conducting conservation biology research, covering species assessment methods, habitat modeling, population viability analysis, key biodiversity databases, and frameworks for conservation prioritization.

## Species Assessment and Red List

### IUCN Red List Categories

```
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
```

### Querying the IUCN API

```python
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"}
```

## Habitat Modeling

### Species Distribution Models (SDMs)

```python
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"
        }
    }
```

## Population Viability Analysis (PVA)

### Estimating Extinction Risk

```
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
```

## Key Databases

### Biodiversity Data Sources

| 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

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

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