nuee 0.1.1

Community Ecology Analysis in Python

nuee: Community Ecology Analysis in Python

nuee is a comprehensive Python implementation of the popular R package vegan for community ecology analysis. It provides tools for ordination, diversity analysis, dissimilarity measures, and statistical testing commonly used in ecological research.

Features

Ordination Methods

• NMDS (Non-metric Multidimensional Scaling) with metaMDS()
• RDA (Redundancy Analysis) with rda()
• CCA (Canonical Correspondence Analysis) with cca()
• PCA (Principal Component Analysis) with pca()
• Environmental fitting with envfit()
• Procrustes analysis with procrustes()

Diversity Analysis

• Shannon diversity with shannon()
• Simpson diversity with simpson()
• Fisher's alpha with fisher_alpha()
• Renyi entropy with renyi()
• Species richness with specnumber()
• Evenness measures with evenness()
• Rarefaction with rarefy() and rarecurve()

Dissimilarity Measures

• Bray-Curtis, Jaccard, Euclidean, and 15+ other distances with vegdist()
• PERMANOVA with adonis2()
• ANOSIM with anosim()
• MRPP with mrpp()
• Mantel test with mantel()
• Beta dispersion with betadisper()

Visualization

• Ordination plots with plot_ordination()
• Biplots with biplot()
• Diversity plots with plot_diversity()
• Rarefaction curves with plot_rarecurve()
• Confidence ellipses with ordiellipse()

Sample Datasets

• varespec & varechem: Lichen species and environmental data
• dune & dune_env: Dutch dune meadow vegetation
• BCI: Barro Colorado Island tree data
• mite & mite_env: Oribatid mite data

Installation

Option 1: Simple Installation (Recommended for Development)

git clone https://github.com/your-username/nuee.git
cd nuee
python install.py

Option 2: Using pip (if available)

# Install dependencies
pip install -r requirements.txt

# Add to PYTHONPATH for development
export PYTHONPATH="${PYTHONPATH}:$(pwd)"

Option 3: Manual Setup

# Install dependencies individually
pip install numpy scipy pandas matplotlib seaborn scikit-learn patsy statsmodels

# Test installation
python -c "import sys; sys.path.insert(0, '.'); import nuee; print('✓ nuee ready!')"

Verify Installation

python -c "
import sys; sys.path.insert(0, '.')
import nuee 
species = nuee.datasets.varespec()
nmds = nuee.metaMDS(species, k=2, trymax=5, trace=False)
print(f'✓ Installation successful! NMDS stress: {nmds.stress:.3f}')
"

Quick Start

import nuee 
import matplotlib.pyplot as plt

# Load sample data
species_data = nuee.datasets.varespec()
env_data = nuee.datasets.varechem()

# NMDS Ordination
nmds_result = nuee.metaMDS(species_data, k=2, distance="bray")
print(f"NMDS Stress: {nmds_result.stress:.3f}")

# Plot ordination
fig = nuee.plot_ordination(nmds_result, display="sites")
plt.show()

# Calculate diversity indices
shannon_div = nuee.shannon(species_data)
simpson_div = nuee.simpson(species_data)
print(f"Shannon diversity: {shannon_div.mean():.3f}")
print(f"Simpson diversity: {simpson_div.mean():.3f}")

# RDA with environmental variables
rda_result = nuee.rda(species_data, env_data)
fig = nuee.biplot(rda_result)
plt.show()

# PERMANOVA
distances = nuee.vegdist(species_data, method="bray")
permanova_result = nuee.adonis2(distances, env_data)
print(permanova_result)

Advanced Examples

Constrained Ordination with Formula Interface

import nuee 
import pandas as pd

# Load data
species = nuee.datasets.dune()
env = nuee.datasets.dune_env()

# RDA with formula
rda_result = nuee.rda(species, formula="~ A1 + Management", data=env)

# Plot with groups
fig = nuee.plot_ordination(rda_result, groups=env['Management'])
plt.show()

Diversity Analysis with Rarefaction

import nuee 
import matplotlib.pyplot as plt

# Load data
species = nuee.datasets.BCI()

# Calculate multiple diversity indices
diversity_indices = {
    'Shannon': nuee.shannon(species),
    'Simpson': nuee.simpson(species), 
    'Fisher': nuee.fisher_alpha(species),
    'Richness': nuee.specnumber(species)
}

# Rarefaction curve
rarefaction = nuee.rarecurve(species, step=10)
fig = nuee.plot_rarecurve(rarefaction)
plt.show()

Permutation Tests

import nuee 

# Load data
species = nuee.datasets.mite()
env = nuee.datasets.mite_env()

# PERMANOVA
dist_matrix = nuee.vegdist(species, method="bray")
permanova_result = nuee.adonis2(dist_matrix, env[['SubsDens', 'WatrCont']])

# ANOSIM
anosim_result = nuee.anosim(dist_matrix, env['Substrate'])

# Mantel test
env_dist = nuee.vegdist(env[['SubsDens', 'WatrCont']], method="euclidean")
mantel_result = nuee.mantel(dist_matrix, env_dist)

print(f"PERMANOVA R²: {permanova_result.r_squared:.3f}")
print(f"ANOSIM R: {anosim_result.r_statistic:.3f}")
print(f"Mantel r: {mantel_result.r_statistic:.3f}")

Dependencies

• numpy >= 1.20.0
• scipy >= 1.7.0
• pandas >= 1.3.0
• matplotlib >= 3.4.0
• seaborn >= 0.11.0
• scikit-learn >= 1.0.0
• patsy >= 0.5.0 (for formula interface)
• statsmodels >= 0.12.0

Contributing

We welcome contributions! Please see our contributing guidelines for details.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

nuee is inspired by the excellent R package nuee developed by Jari Oksanen and the nuee development team. We acknowledge their pioneering work in making community ecology analysis accessible to researchers worldwide.

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Happy analyzing! 🌱