Hot off the press: Code from Hoff et al. 2025 PNAS paper

Hot Off the Press: Code from Hoff et al. 2025 PNAS Paper

New feature on our Software & Data repository page: Hot off the press! Featuring code from Hannah Hoff's 2025 PNAS paper, The Apportionment of Dietary Diversity in Wildlife.

This paper presented a potentially paradigm-shifting strategy to quantify and characterize the number of unique 'diet types' that exist within a population or community. The strategy is based on a simple machine-learning algorithm and described in the Hoff et al. 2025 PNAS paper, which used the community of migratory large mammalian herbivores -- such as bison and elk -- as a prime example.

The strategy makes use of dietary DNA metabarcoding data and extensive local plant DNA reference library to characterize variation in animal diets. It then applies a machine-learning algorithm called "Partitioning Around Medoids" -- or "PAM" -- to help organize samples into an optimal number of clusters to maximize variation between groupings. This allows us to recognize patterns in the data, without having to apply a priori assumptions about the groupings (e.g., should we lump all samples from a species together?). 

After clusters are identified, the code also presents a strategy for performing Indicator Species Analysis to identify plant taxa that contribute strongly to the clustering pattern we observed.

You can link to the open-source code and data repositories are at:

• Dryad https://doi.org/10.5061/dryad.mgqnk99b8
• Zenodo https://doi.org/10.5281/zenodo.15634157