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.

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...

By Hannah Hoff

We have been doing a lot of plant DNA barcoding to build a library for the plants of the Greater Yellowstone Ecosystem. In the process, we have done some refinement to our lab's DNA barcoding protocols to increase clarity, efficiency, and reproducibility. Updated links to these protocols are available on the lab's wiki under the "Plant Barcoding" section for: trnL, rbcL, matK, and trnH-psbA markers.

Collaborator Nick Harvey has kindly provided a formatted version of our current Mpala Plant DNA Barcode Reference Library that is suitable for taxonomic assignments using the R package dada2. You can download the fasta file of reference library v.2.0 (corresponding to Gill et al. 2019) formatted for dada2 here.

​Thank you, Nick, for making this time-saving resource available to share!

We are often asked to provide advice or assistance building plant DNA reference libraries for use in dietary metabarcoding projects. To begin centralizing info on our methods and sharing some important lessons-learned from experience, I have created a section on the lab's wiki for building plant barcode libraries. I will treat the google docs that you can link to from there as living documents. All of the details provided are nested within two main goals. The first goal is to collect plant voucher specimens and plant DNA barcode samples that match in ways that can be clearly documented  through their respective metadata sheets. This is critical for the long-term value of the data. The second goal is to ensure work done by field biologists and molecular biologists are mutually informative -- the best reference libraries are developed through the meaningful engagement of expert botanists who are knowledgeable in a local flora and the researchers who will be analyzing the laboratory data. 

We love to archive relevant vouchers in the Brown University Herbarium. Please keep in mind that the herbarium is staffed by expert botanists. Properly collected specimens can be mounted, archived, and digitized by professional staff -- this greatly reduces the cost and complexity of fieldwork.