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Global diversity patterns of larger benthic foraminifera under future climate change

Cite this dataset

Rödder, Dennis; Förderer, Esther-Meena; Langer, Martin (2022). Global diversity patterns of larger benthic foraminifera under future climate change [Dataset]. Dryad.


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.


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.
  • Hijmans R.J., Phillips S., Leathwick J., Elith J. (2017) dismo: Species Distribution Modeling. R package version 1.1-4.
  • 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;
  • 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; 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;

Usage notes

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 (, while the associated R scripts can be edited in any text editor. The lambda files can be projected using Maxent (


Deutsche Forschungsgemeinschaft, Award: 426127743