This repository contains data and codes to build and plot foraminifera’s thermal performance curve in the Last Glacial Maximum (LGM) and pre-industrial age.

Publication: Ying, R., Monteiro, F. M., Wilson, J. D., and Schmidt, D. N.: Marine zooplankton acclimated to geological warming but facing limits by the next century, EarthArxiv (preprint), https://doi.org/10.31223/X5D10G, 2023.

1. Clone the repository using git
2. Run the related scripts
3. (optional) reanalyse raw cGENIE output by downloading them from Zenodo and put under “model” directory

## clone the repository
git clone https://github.com/ruiying-ocean/lgm_foram_niche.git

1. The latest R and Python
2. R libraries (`tidyverse`, `quantregGrowth`, `ggpubr`, `patchwork`)
3. Python packages (`numpy`, `matplotlib`, `pandas`, `xarray`, `cgeniepy`v0.7.5)

• export genie_array/export_genie_timeseries.ipynb: convert GENIE output to csv data
• model_niche/obs_niche.R: build thermal performance curves (TPC) and assess optimal temperature
• anova_analysis: ANOVA of species-level optimal temperature change
• lib.R: most functions I used such as fit the quantile regression model
• Others (plot_xxx): as per name

Due to the size of model simulation output (which can be found in 10.5281/zenodo.8272885 ), I put my model-derived data and related analysed data here. The widely used abbreviation of foram group in the repo is as follow: bn=symbiont-barren non-spinose; bs=symbiont-barren spinose; sn=symbiont-facultative non-spinose; ss=symbiont-obligate spinose)

• genie_fg_raw/genie_fg_smooth/obs_raw/obs_smooth.Rdata: as in the Fig. 1
• obs_sp_Topt.csv: The estimated optimal temperature min/max/mean/sd in each age (LGM/PI/historical/future1.5/future2/future3/future4)
• ssp_co2/ssp_temp.Rdata: adapted from CMIP models
• xx_foramecogenie.csv: the model output (xx is the model experiment)