DOI: 10.5061/dryad.xpnvx0kgt
title: Data from: Drivers of global pre-industrial patterns of species turnover in planktonic foraminifera

description: Anthropogenic climate change is altering global biogeographical patterns.
 However, it remains difficult to quantify how bioregions are changing
 because pre-industrial records of species distributions are rare. Marine
 microfossils, such as planktonic foraminifera, are preserved in seafloor
 sediments and allow the quantification of bioregions in the past. Using a
 recently compiled data set of pre-industrial species composition of
 planktonic foraminifera in 3802 worldwide seafloor sediments, we employed
 multivariate and statistical model-based approaches to study spatial
 turnover in order to 1) quantify planktonic foraminifera bioregions and 2)
 understand the environmental drivers of species turnover. Four
 latitudinally banded bioregions emerge from the global assemblage data.
 The polar and temperate bioregions are bi-hemispheric, supporting the idea
 that planktonic foraminifera species are not limited by dispersal. The
 equatorial bioregion shows complex longitudinal patterns and overlaps in
 sea surface temperature (SST) range with the tropical bioregion.
 Compositional-turnover models (Bayesian bootstrap generalised
 dissimilarity models) identify SST as the strongest driver of species
 turnover. The turnover rate is constant across most of the SST gradient,
 showing no SST threshold values with rapid shifts in species composition,
 but decelerates above 25°C, suggesting SST is less predictive of species
 composition in warmer waters. Other environmental predictors affect
 species turnover non-linearly, and their importance differs across
 regions. In the Pacific ocean, net primary productivity below 500 mgC m−2
 day−1 drives fast compositional change. Water depth values below 3000 m
 (which affect calcareous microfossil preservation) increasingly drive
 changes in species composition among death assemblages in the Pacific and
 Indian oceans. Together, our results suggest that the dynamics of
 planktonic foraminifera bioregions are expected to be highly responsive to
 climate change; however, at lower latitudes, environmental drivers other
 than SST may affect these dynamics.
description: All data used in this paper have been published before elsewhere,
 please cite them appropriately. For planktonic foraminifera
 relative abundance data (ForCenS), see Siccha, M., & Kucera, M.
 2017 <i>PANGAEA</i> https://doi.org/10.1594/PANGAEA.873570. For environmental data, see: World Ocean Data (WOA) 2018: https://www.ncei.noaa.gov/access/world-ocean-atlas-2018/ Historical sea-surface temperature: https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v5.html Ocean Productivity: http://sites.science.oregonstate.edu/ocean.productivity/
description: Code "0_main.R" calls (source) the other codes, so
 start with this code. Most of the analyses require high-performance
 computing, so I suggest decreasing the size of the data or the length of
 analyses (e.g. bootstraps) when running codes on your personal
 computer. The code provided here (Zenodo) downloads and
 analyses these data, except for the Ocean Productivity data that is
 already provided as "forcens_pp.csv". The forcens_pp dataset
 contains net primary productivity (NPP) data for each of the ForCenS
 samples used. Please contact the first author if you would like the R code
 to obtain annual mean NPP data. Explanation of the forcens_pp
 columns: Sample_ID: identification code of
 the sample from the ForCenS dataset (see above)
 Latitude and Longitude: coordinates of the
 sample/site site: unique identification number of
 each ForCenS site given by us pp: annual mean values
 of NPP from the Ocean Productivity project (see above), using the standard
 VGPM algorithm for each site