nuee has just been released, and is likely to contain errors and bugs. The code has been tested, but hasn't been thoroughly reviewed. Do not trust its results blindly.

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.

pip install nuee

Full documentation is available at: https://essicolo.github.io/nuee/

The documentation includes:

• Installation instructions
• Quick start guide
• User guide with detailed examples
• Complete API reference

To build the documentation locally:

cd docs
make html
# Open docs/_build/html/index.html in your browser

• 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()

• Shannon diversity with shannon()
• Gini-Simpson diversity with simpson() (1 - sum(p^2))
• Fisher's alpha with fisher_alpha()
• Renyi entropy with renyi()
• Species richness with specnumber()
• Evenness measures with evenness()
• Rarefaction with rarefy() and rarecurve()

• 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()

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

• 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

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"Gini-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)

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()

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()

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 R^2: {permanova_result["r_squared"]:.3f}")
print(f"ANOSIM R: {anosim_result["r_statistic"]:.3f}")
print(f"Mantel r: {mantel_result["r_statistic"]:.3f}"))

• 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

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

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

nuee is inspired by the excellent R package vegan developed by Jari Oksanen and the vegan development team. Both vegan and nuee were inspired by the book "Numerical Ecology", by Pierre Legendre and Louis Legendre (3rd edition, 2012). We acknowledge their pioneering work in developping the science of numerical ecology and making it accessible to researchers.