Past, present and future distributions of Oriental beech (Fagus orientalis) under climate change projections

Species distribution models can help predicting range shifts under climate change. The aim of this study is to investigate the late Quaternary distribution of Oriental beech (Fagus orientalis) and to project future distribution ranges under different climate change scenarios using a combined palaeobotanical, phylogeographic, and modelling approach. Five species distribution modelling algorithms under the R-package `biomod2`were applied to occurrence data of Fagus orientalis to predict distributions under present, past (Last Glacial Maximum, 21 ka, Mid-Holocene, 6 ka), and future climatic conditions with different scenarios obtained from MIROC-ESM and CCSM4 global climate models. Distribution models were compared to palaeobotanical and phylogeographic evidence. Pollen data indicate northern Turkey and the western Caucasus as refugia for Oriental beech during the Last Glacial Maximum. Although pollen records are missing, molecular data point to Last Glacial Maximum refugia in northern Iran. For the mid-Holocene, pollen data support the presence of beech in the study region. Species distribution models predicted present and Last Glacial Maximum distribution of Fagus orientalis moderately well yet underestimated mid-Holocene ranges. Future projections under various climate scenarios indicate northern Iran and the Caucasus region as major refugia for Oriental beech. Combining palaeobotanical, phylogeographic and modelling approaches is useful when making projections about distributions of plants. Palaeobotanical and molecular evidence reject some of the model projections. Nevertheless, the projected range reduction in the Caucasus region and northern Iran highlights their importance as long-term refugia, possibly related to higher humidity, stronger environmental and climatic heterogeneity and strong vertical zonation of the forest vegetation. Swedish Research Council (VR)Swedish Research Council [2015-03986] TD acknowledges funding from the Swedish Research Council (VR grant no. 2015-03986).