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Artificial intelligence is increasingly useful as a tool to improve our research and learning. We use it to troubleshoot code, polish writing, get good ideas about how to visualize data, create document templates that save time on busywork… But at the same time, we must be cognizant of legitimate concerns about the accuracy of information it can provide, its ability to reuse confidential information that we disclosed in chats, and the risk of short-circuiting our own creative uses of the scientific method.

This post summarizes rules that lab members should follow when using AI in their work. I do not want to regurgitate the types of dry, legalese we are provided by our employer--rather I will attempt to illustrate the fine-line we have to walk to ensure we are using the tool appropriately while minimizing the risk of unintended harm. I will summarize reasons for concern using language familiar to biologists and conservationists broadly. Some of the details are specific to researchers at Brown, but I believe the information is readily transferable and I welcome others to use this document as a template for their own policies.

​Please read on... 

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 detailed new protocols describing our methods to sequence key mammalian DNA barcodes. They can be found together with a growing number of field and lab protocols on the Kartzinel Lab's centralized protocol page.

You will find protocols for 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. 

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.

Since June 2025, 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 changes have improved how we share the software and data that we generate in the lab, necessitated in part by the growing popularity of our work. We recognized a substantial benefit to enhancing our commitment to providing easy access and open-source principles and therefore made it easier to find and access our most in-demand repositories across a variety of platforms that include GitHub, Zenodo, NCBI, BOLD, etc. You can now easily find links with descriptions from our main Software & Data landing page. Check it out!

Our most recent map-making tutorial focuses on unlocking the potential of rasters. You can open Map-making for ecologists, tutorial 3: Rasters: combining them, adding hillshade, and suppressing legends here​ or access the Map-making in R GitHub repository to download the data and code directly.