Team members: Adam Woodhouse^1,2,* , Anshuman Swain^3,4,5,6,* , William F. Fagan³, Andrew J. Fraass⁷, and Christopher M. Lowery¹

¹University of Texas Institute for Geophysics, University of Texas at Austin, Austin, TX 78712, U.S.A. ²School of Earth and Environment, University of Leeds, Woodhouse, Leeds LS2 9JT, U.K. ³Department of Biology, University of Maryland, College Park, MD 20742, U.S.A. ⁴Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, U.S.A ⁵Museum of Comparative Zoology, Harvard University, Cambridge, MA 02138, U.S.A. ⁶Department of Paleobiology, National Museum of Natural History, Washington, D.C. 20013, U.S.A. ⁷School of Earth and Ocean Sciences, University of Victoria, Victoria, BC V8P 3E6, Canada

^* equal contribution, first co-authors

Keywords: Triton, Foraminifera, Bipartite networks, Cenozoic

This project aims to use foraminiferal global occurrence data for understanding marine biogeography dynamics using a bipartite network approach. We look at the problem by quantifying group diversity, latitudinal specialization, and latitudinal equitability for planktonic foraminifera over the last 8 Ma using Triton, a recently developed high-resolution global dataset of planktonic foraminiferal occurrences.

Base scripts in R

• data partition.R: Data partitioning script to categorize the original binned data from Triton based on 250 ky bin resolution.
• network properties.R: Calculate network properties at the node level using 'bipartite' R library, but we will only adapt Degree, Species specificity Index and Paired Different Index for our purpose.
• null models.R: Null models for distribution in each time bin based on 'econullnetr' R package.
• functional diversity.R: Functional and species Shannon doversity caclulation.
• linear models.R: base code for linear models used in the work.