Niche modeling using sedimentary ancient DNA
Rasmus Nielsen, Professor
Integrative Biology
Closed. This professor is continuing with Fall 2025 apprentices on this project; no new apprentices needed for Spring 2026.
Understanding how species interact with their environment and how climate influences where species occur is integral to ecology. We work on questions about what factors influence the spatial distribution of species, which can be determined using mathematical models called 'species distribution models' or 'environmental niche models'. The data that go into these models are records of the past and present climate, and a record of where a species has occurred (either in the present, past, or both). There are many ways to get data like this, but the data we are using comes from the DNA left behind by organisms in the environment. Our collaborators collect sediment samples from lakes, which contain DNA from the surrounding organisms, which can then be sequenced to tell us what organisms were in the environment when that sediment was deposited. This DNA, which can be preserved for tens of thousands of years, is called sedimentary ancient DNA. Since this is a relatively new type of data, we are developing computational methods that will be able to better incorporate this data into niche models.
Role: Undergrads in this project will write computational methods to compare the niche models from ancient DNA to species-specific modern models that have been published elsewhere. This will involve using the API for sites like the IUCN red list, GBIF, and others. Learning Outcomes: We expect you will learn about the process of doing computational biology research, understand models of species distributions and why they matter, and develop your ability to apply coding skills to research questions.
Qualifications: You must know how to program in at least one language, such as R or Python. For example, you could have taken an introductory computer programming class like Data 8 or CS 61A or something similar. We expect that you either already have or intend to get some ecology and statistics background. Also, you must demonstrate an interest in ecology and the topic of the research. We encourage students from underrepresented backgrounds to apply.
Day-to-day supervisor for this project: Fiona Callahan, Ph.D. candidate
Hours: 9-11 hrs
Related website: http://nielsen-lab.github.io/
Related website: http://nielsen-lab.github.io/pdfs/Undergrad_mentorship_expectations.pdf