qdiv: A Python package for microbial ecology analysis using the Hill number framework

Artikel i vetenskaplig tidskrift, 2026

Microorganisms are ubiquitous and play essential roles in biogeochemical cycles, human
health, and engineered systems. Microbial ecology research seeks to understand the structure
and function of microbial communities. Common approaches, such as amplicon sequencing
and metagenomics, generate tabular data that track the relative abundances of hundreds or
thousands of species or genes across time or space. To explore how communities assemble and
respond to environmental factors, microbial diversity is quantified using various metrics, some
incorporating phylogenetic and functional relationships.
The Hill number framework, also known as effective numbers, provides a unified and intuitive
methodology for assessing diversity and changes in community composition. However, few tools
systematically implement this framework across alpha and beta diversity, phylogenetic metrics,
multivariate statistics, and null models. The Python package qdiv fills this gap by applying
the Hill number framework to a broad range of ecological metrics, offering a streamlined and
versatile tool for investigating patterns of community assembly.