pydarkdiv 1.0.1

Python implementation of the DarkDiv R package for calculating dark diversity from co-occurrence data

PyDarkDiv

Python implementation of the DarkDiv R package for calculating dark diversity from species co-occurrence data.

Overview

PyDarkDiv estimates dark diversity - the unobserved portion of a site's species pool consisting of species that could potentially occur but are currently absent. This package implements the methods described in Carmona & Pärtel (2021).

Based on: DarkDiv R package by Carlos P. Carmona and Meelis Pärtel

Installation

pip install pydarkdiv

Quick Start

import pandas as pd
import pydarkdiv as pdd

# Load your species data (sites × species matrix)
data = pd.read_csv('species_data.csv', index_col=0)

# Calculate dark diversity
dd = pdd.DarkDiv(data, method='Hypergeometric')

# Get results as DataFrames
dfs = dd.to_dataframes()
dark_diversity = dfs['dark']
species_pool = dfs['pool']

# Summary statistics
dark_richness = (dark_diversity > 0.5).sum(axis=1)
print(f"Average dark diversity: {dark_richness.mean():.1f} species")

Methods

• Hypergeometric (recommended): Compares observed vs. expected co-occurrences
• RawBeals: Beals smoothing for occurrence probabilities
• Favorability: Favorability-corrected Beals values
• ThresholdBeals: Binary predictions using thresholds

Usage Examples

See usage_examples.py for detailed examples including:

• Basic usage
• Method comparisons
• Reference data usage
• Abundance weighting

Requirements

• Python ≥ 3.8
• numpy ≥ 1.21.0
• pandas ≥ 1.3.0
• scipy ≥ 1.7.0

Reference

Carmona, C.P. & Pärtel, M. (2021). Estimating probabilistic site-specific species pools and dark diversity from co-occurrence data. Global Ecology and Biogeography, 30(1), 316-326. DOI link

License

MIT License