nuee 0.1.5

Community Ecology Analysis in Python

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

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.

Installation

pip install nuee

Documentation

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

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()
• 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()

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()
• Ordiplots with ordiplot()
• 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

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"Gini-Simpson diversity: {simpson_div.mean():.3f}")

# RDA with environmental variables (auto-scales loadings/arrows)
rda_result = nuee.rda(species_data, env_data)
fig = nuee.ordiplot(rda_result, loading_factor=0, predictor_factor=0)
plt.show()

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

loading_factor and predictor_factor accept any numeric value. Set them to 0 to match the longest sample vector automatically; use other values to shrink or stretch species loadings and environmental arrows manually.

To explore the residual (unconstrained) component of an RDA, pass axes_source="unconstrained" when calling ordiplot.

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 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}"))

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 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.