Team members: Adam Woodhouse1,2,* , Anshuman Swain3,4,5,6,* , 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.