Analyzing Paleoecological Data: Best Practices and Current Resources


Jessica L. Blois, UC Merced (

Edward B. Davis, University of Oregon Dept. of Geological Sciences


This workshop will provide 1) guidance on best practices in paleoecological data analysis and 2) training in use of the Neotoma Paleoecology Database ( to access and analyze paleoecological data. Neotoma is a multiproxy paleodatabase that stores multiple kinds of paleoecological & paleoenvironmental data. One of the strengths of Neotoma is the ability to compare one data type with other proxies; currently, fossil pollen, vertebrates, diatoms, ostracodes, insects, charcoal, and geochemical data are all archived by the database. In addition, the database is structured to relate absolute dates to taxon occurrences and to allow the creation and storage of age models built on absolute dates from stratigraphic sections. Neotoma is a public-access, community-supported database that is emerging as the standard repository for Pliocene and Quaternary paleoecological data.

This half-day workshop will include lecture material and hands-on work with paleoecological data, focusing on Quaternary mammals and pollen. The workshop will include an overview of key elements of paleoecological data and best practices and issues associated with using long-term spatio-temporal data.

Workshop participants will learn how to search and acquire data using web tools, and how to use online mapping functions. Participants will also learn how to use Neotoma’s APIs (Application Programming Interface) and the neotoma R package to write scripts to import Neotoma data into R for further analysis, and perform simple analyses with those scripts. Finally, Neotoma and the Paleobiology Database have a new project to facilitate cross- database queries, and reconciling results across the two platforms will be discussed.

Participants should bring a laptop computer. Participants should have downloaded and installed R and RStudio. Both are free to download and work on multiple platforms. Familiarity with R is helpful but not required. Early-career scientists are especially encouraged but all are welcome.