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.

Designing a dietary DNA metabarcoding study often begins with a deceptively simple question: How many samples do I really need to collect?

There is not a universally “correct” number. We all want to have a large enough sample size for a powerful analysis. But it can be extremely challenging to collect fresh scat samples from wild animals—especially when they are rare and widespread—and then we face the cost of analyzing what we get.

To answer this question, we need to focus mostly on the ecological inferences we want to make. Are we trying to compare groups? Estimate niche breadth? Detect rare food items? Describe seasonal shifts? The number of samples required to detect differences between sample sets is often very different from the number needed to perfectly catalog everything in a diet. So, I want to share some helpful rules of thumb based on experience across a wide variety of study systems...

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.

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... 

For more than 13 years, we have built and maintained a plant DNA Barcode library for the flora of Mpala Research Centre in Kenya. Many versions have been released, the most substantial of which was associated withe 2019 publication of the Plant DNA-barcode library and community phylogeny for a semi-arid East African savanna in Molecular Ecology. This post is intended to help you track significant developments in this long-term collaborative effort and find the most current version to use in your analyses.