DOI: 10.5061/dryad.pnvx0k6rq
title: Global diversity patterns of larger benthic foraminifera under future climate change

description: Global warming threatens the viability of tropical coral reefs and
 associated marine calcifiers, including symbiont-bearing larger benthic
 foraminifera (LBF). The impacts of current climate change on LBF are
 debated because they were particularly diverse and abundant during past
 warm periods. Studies on the responses of selected LBF species to changing
 environmental conditions reveal varying results. Based on a comprehensive
 review of the scientific literature on LBF species occurrences, we applied
 species distribution modeling using Maxent to estimate present-day and
 future species richness patterns on a global scale for the time periods
 2040–2050 and 2090–2100.  For our future projections, we focus on
 Representative Concentration Pathway 6.0 from the Intergovernmental Panel
 on Climate Change, which projects mean surface temperature changes of
 +2.2°C by the year 2100. This data set comprises all raw data and results.
 Our results suggest that species richness in the Central Indo-Pacific is
 two to three times higher than in the Bahamian ecoregion, which we have
 identified as the present-day center of LBF diversity in the Atlantic. Our
 future predictions project a dramatic temperature-driven decline in
 low-latitude species richness and an increasing widening bimodal
 latitudinal pattern of species diversity. While the central Indo-Pacific,
 now the stronghold of LBF diversity, is expected to be most pushed outside
 of the currently realized niches of most species, refugia may be largely
 preserved in the Atlantic. LBF species will face large-scale non-analogous
 climatic conditions compared to currently realized climate space in the
 near future, as reflected in the extensive areas of extrapolation,
 particularly in the Indo-Pacific. Our study supports hypotheses that
 species richness and biogeographical patterns of LBF will fundamentally
 change under future climate conditions, possibly initiating a faunal
 turnover by the late 21st century.
description: Species records were compiled based on literature review and
 examination of specimens. Distribution modelling was performed using
 environmental layers from Bio-ORACLE for current conditions and
 Representative Concentration Pathway (RCP) scenarios 2.6, 4.5, 6.0, and
 8.5 for the time periods 2040–2050 and 2090–2100 (Tyberghein et al. 2012;
 Assis et al., 2018). SDMs were computed
 using Maxent v. 3.4.4 (
 Phillips et al. 2006; Phillips et al. 2017). Additionally, the R-packages raster
 (Hijmans
 2016), dismo
 (Hijmans et al.
 2017) and ENMeval
 (Muscarella et al.
 2014) were used for
 further processing in R 4.0. 
 Assis J., Tyberghein L., Bosch S.,
 Verbruggen H., Serrão EA, De Clerck O (2017) Bio-ORACLE v2.0: Extending
 marine data layers for bioclimatic modelling. Global Ecology and Biogeography 27:
 277-284. Hijmans R.J. (2016) raster: Geographic Data
 Analysis and Modeling. R package version 2.5-8. https://CRAN.R-project.org/package=raster.  Hijmans R.J., Phillips S., Leathwick J., Elith J. (2017) dismo: Species Distribution Modeling. R package version 1.1-4. https://CRAN.R-project.org/package=dismo. Muscarella R., Galante P.J., Soley-Guardia M., Boria R.A., Kass J.M., Uriarte M.,  Anderson R.P. (2014) ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for MAXENT ecological niche models. Methods in Ecology and Evolution 5: 1198-1205; https://doi.org/10.1111/2041-210x.12261 Phillips S.J., Anderson R.P., Dudik M., Schapire R.E., Blair M.E. (2017) Opening the black box: an open-source release of Maxent. Ecography 40: 887-893; https://doi.org/ 10.1111/ecog.03049 Phillips S.J., Anderson R.P., Schapire R.E. (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling 190: 231-259. Tyberghein L., Verbruggen H., Pauly K., Troupin C., Mineur F., De Clerck O. (2012) Bio-ORACLE: a global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography 21: 272-281; https://doi.org/10.1111/j.1466-8238.2011.00656.x
description: Species records are stored as *.csv files, which can be processed
 in Microsoft Excel, the environmental variables can be processed in any
 GIS software, e.g. QGIS (https://www.qgis.org/en/site/), while the
 associated R scripts can be edited in any text editor. The lambda files
 can be projected using Maxent
 (https://biodiversityinformatics.amnh.org/open_source/maxent/).