Working with dietary DNA metabarcoding data? Unsure how to concisely summarize your workflow for publication? Tired of all the effort required to format your data tables for archiving in Dryad, supplementary materials, or other archives? The lab has posted new code to our GitHub repository that will help you solve all of these problems.

We have posted a couple detailed new protocols describing our methods to sequence key mammalian DNA barcodes. Both the D-loop of the mitochondrial control region and the 16S marker are useful for identifying a diversity of mammals, and can be routinely amplified from degraded material such as fecal DNA. We have frequently used these protocols to confirm the identity of mammals in studies involving dietary DNA metabarcoding and/or host-microbiome interactions. They are also very useful for phylogenetic analyses. We have used various polymerases over the years, so these protocols may depart slightly from previously published versions (e.g., Kartzinel et al. 2019 PNAS). However, they reflect our current state-of-the-art strategy for routine work and should be generally more cost or time effective as a result of the changes. They can be found on our lab's protocol page.

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.

Fans of the lab will be very excited to see this much-anticipated release of our standard dietary DNA metabarcoding pipeline, with a walk-through easily accessible on the centralized "Software & Data" section of our webpage. Until now, people would have to access code repositories associated with each of our publications or contact us directly to model their analysis after our well-established workflow. That led to multiple versions of the pipeline in circulation, since we are constantly improving it and published versions quickly ended up out of date. We have tried to solve that problem by...

Beginning in June, we have increasingly made our internal lab methods publicly visible on the "Protocols" section of our webpage. We began with some of the most frequently requested protocols that speak to the unique strengths of our lab's work and experience, featuring field-to-lab protocols for collecting and banking dietary samples, parasite samples, and plant barcode samples. We have expanded to include...

Featured Software from the Kartzinel Lab: Geographic Coverage of DNA Barcodes. The inaugural code repository to be highlighted in our Featured Software section of the Software & Data page presents the Quarto Code Book published in association with our Molecular Ecology Review Paper, "Global Availability of Plant DNA Barcodes as Genomic Resources to Support Basic and Policy-Relevant Biodiversity Research" can be easily modified to evaluate the geographic coverage of other data sets. Although the featured code emphasizes geographic coverage from our work in Yellowstone National Park...

Over the past couple of years, a lot of things have changed. Some of those changes have brought improvements to the way we share the software and data that we generate in the lab, necessitated in part by the growing popularity of our work to a point that we recognized a substantial benefit to enhancing our commitment to easy access and open-source principles. 

To make it easier to find and access our most in-demand repositories -- often spread across a variety of platforms including GitHub, Zenodo, NCBI, BOLD, etc. -- we have created a main landing page on the website where we have curated a mix of both recent and timeless contributions to the field. Check it out!

You might also want to check out our lab's main GitHub repository, where we are increasingly making our projects publicly available so you can benefit from any updates we've made since posting a version of record for the code with a prior publication (e.g., a static record with a Zenodo DOI). 

I will periodically post updates to this blog in order to highlight and summarize important updates to help ensure the community is aware, but we will no longer be using these pages to share data or code directly as we have in the past.

By Hannah Hoff

The most recent map-making tutorial focuses on unlocking the potential of rasters. We start by adding hillshade, which provides a clearer picture of the topography of the area by mimicking the sun's effects (illumination, shadows) on hills and mountains, which is particularly useful when mapping a place like Yellowstone National Park. However, when adding multiple layers onto a map, we suddenly end up with a number of different legends which may vary in utility and make the map look "busy." Simple functions allow us to suppress legends and guides that aren't useful, while retaining those that are. As usual, using well-documented and flexible packages like ggplot2 allow us to easily incorporate those functions, which may make downstream figure formatting more efficient. Thanks to the Kartzinel Lab for sharing ideas about useful map elements in a recent lab meeting :) 

A link to this PDF tutorial is available here. A link is also be posted on the Lab Wiki, where we are making an effort at compiling useful computing resources. Hannah has also set up a GitHub repository for these tutorials (https://github.com/hkhoff/Map-making-in-R-tutorials-), which includes the R code used in each tutorial, and the ('dummy') data that was created as an example of what sorts of data might be fed into the code; both files can be downloaded for a more interactive tutorial experience :) 

By Hannah Hoff

​Our last map-making tutorial provided code for basic sampling maps in R. In a continued effort to demystify the world of spatial analysis, Hannah has created a tutorial that builds on that foundation to share some useful functions for tailoring map designs to different types of studies. This tutorial uses a "finalized" map from Hannah's earlier post to create a map inset, which allows us to zoom in on areas of interest (sampling points, population ranges, etc.). This may be particularly useful for studies where sampling occurs in a very small area relative to the broader landscape, or in cases where there is substantial visual overlap between sample points. As some of our work in Yellowstone is focused on smaller areas, map insets have become a necessary part of our map-making pipeline!

A link to this PDF tutorial is available here. A link is also be posted on the Lab Wiki, where we are making an effort at compiling useful computing resourcesHannah has also set up a GitHub repository for these tutorials (https://github.com/hkhoff/Map-making-in-R-tutorials-), which includes the R code used in each tutorial, and the ('dummy') data that was created as an example of what sorts of data might be fed into the code; both files can be downloaded for a more interactive tutorial experience :) 

By Bethan Littleford-Colquhoun

A huge debt of gratitude to Beth for organizing a google drive folder that shares our strategy for initial processing of dietary DNA metabarcoding data. This directory provides template documents and code that we use to download paired-end read data from our dietary metabarcoding workflows, assemble forward and reverse reads, perform initial quality controls, and evaluate sequencing success. There is also a tutorial for how to upload sample data to SRA for archiving, which we have started to do routinely. Some details are specific to our dietary analysis sequencing workflow and/or the specific sequencing service that we use, plus we have particular sample naming schemes that we use, but it should be pretty straightforward to adapt for a variety of similar goals.