Distribution modelling of pre-Columbian California grasslands with soil phytoliths: New insights for prehistoric grassland ecology and restoration

• Post by: phytoliths@admin
• April 30, 2018
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Fick, S.E., and R.R. Evett. 2018.
Distribution modelling of pre-Columbian California grasslands with soil phytoliths: New insights for prehistoric grassland ecology and restoration. PLoS ONE 13(4):e0194315. https://doi.org/10.1371/journal.pone.0194315

This article opens a new frontier for phytolith research, for the first time combining phytolith data with a species distribution modeling (SDM) approach to estimate the prehistoric distribution of grass-dominated grassland in California.
Following the arrival of Spanish explorers in California in 1769, cover of native species has been replaced by a suite of invasive species on over 7 million hectares, largely annual grasses of Mediterranean origin; less than 1% of the original grassland is relatively intact.
The goal of our research was to estimate the spatial extent of grass-dominated grassland (until recently, assumed to mirror the current distribution) prior to 1769. SDM typically combines point-based species distribution data (often from herbarium specimens) with environmental (often climatic) data at each point to build a model of the environmental niche of a species; the model is then tested by using the model to map the current spatial distribution of the species and comparing this with the actual distribution.
The validated model can be used to estimate distribution of the species under different climate scenarios that occurred in the past or will occur in the future. While pollen data has often been used successfully to provide point-based past species distribution data, soil phytolith data has never been used as an input for an SDM. Using soil phytolith content data from a network of 120 sites, we built an SDM and mapped the estimated prehistoric distribution of grass-dominated grassland; the estimated distribution is much smaller than the current distribution. We believe our approach, using phytolith data to build SDM models, is an important addition to the paleoecological toolkit.