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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.

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.

Over the years, our lab has contributed a number of essential reviews about how DNA sequence data can be accurately converted into dietary information. The science is clear: inappropriate assumptions about how to 'clean up' sequence data using bioinformatics can do more harm than good by warping our diet profiles and generating misleading assumptions. Nevertheless, we have to make some such assumptions to generate datasets that are useful and informative. How should we think about striking a balance between these competing imperatives?

Led by Dr. Bethan Littleford-Colquhoun, one of the more important reviews we've produced on this topic was published in Molecular Ecology: The Precautionary Principle. This review, and a follow-up reply describing Evidence-based Strategies to Navigate Complexity, tackle the challenge of identifying appropriate abundance-filtering strategies in DNA metabarcoding pipelines. This post provides an essential summary of what we found... 

One great application of DNA barcodes is the ability to generate accurate and relevant phylogenetic trees for ecological and evolutionary analyses. There are lots of ways to do this, but not all of them may be necessary or relevant to your end goals. What do you need to know before you get started? This post provides a simple road map that you can follow to decide whether and how to construct a phylogeny using DNA barcode data for your research.

Brown University's high performance computing cluster, called Oscar, is a keystone resource for research in the Kartzinel Lab. The resource and information about it are regularly updated; users may find the links provided in this post to be especially helpful as they are getting  started.