In many dietary DNA metabarcoding studies, sampling and replication tends to be framed around predefined groups:

• Species A vs. Species B
• Dry season vs. wet season
• Treatment vs. control
• Population 1 vs. Population 2

We are taught to ask ourselves: How many samples do we need to collect per group for a statistically robust sampling design?

But what if group identity does not need to be the primary unit of analysis in the first place?

Recent analytical approaches — including the use of unsupervised and minimally supervised machine learning tools — allow ecological patterns to emerge directly from dietary data without requiring us to impose a priori sampling categories on the "groups' that we have under study. When that happens, the logic of replication changes.

Replication still matters.
But why it matters is different.

Jump to: Rules | Risks | Reasons for Concern | University Links & Policies

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... 

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.