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.

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.

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.

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.