Raster and original working data for the paper Holocene matters: landscape history accounts for current species richness of vascular plants in forests and grasslands of eastern Central Europe ...

• Main Authors: Hájek, Michal, Divíšek, Jan
• Format: Dataset
• Language: English
• Published: Dryad 2020
• Subjects: biodiversity models; Habitats; Quaternary history; refugia; taiga
• Online Access: https://dx.doi.org/10.5061/dryad.cjsxksn28; https://datadryad.org/stash/dataset/doi:10.5061/dryad.cjsxksn28

Aim: Current species-richness patterns are sometimes interpreted as a legacy of landscape history, but historical processes shaping the distribution of species during the Holocene are frequently omitted in biodiversity models. Here, we test their importance in modelling current species richness of vascular plants in forest and grassland vegetation. Location: Western Carpathians and adjacent regions. Taxon: Vascular plants. Methods: Numbers of all species and of habitat specialists were extracted from plot records of forest and grassland vegetation. For each plot, environmental and historical data were derived from thematic maps. Historical data related to the persistence of (i) temperate taxa during the Late Glacial and Early Holocene, (ii) open-landscape taxa during the Middle Holocene, and (iii) taiga species during the Late Holocene were based on 112 fossil pollen profiles. Boosted regression trees were used to model spatial patterns in species richness. Results: Historical variables always appeared among ... : Numbers of all species and of habitat specialists were extracted from plot records of forest and grassland vegetation (the EVA database). Environmental data were derived from GIS-based thematic maps. As historical data we used the pollen site scores on the first axis of principal coordinate analysis (PCoA) performed by Jamrichová et al. (2017 J Biog) and interpolated them using ordinary kriging with an exponential semi-variogram model. We also weighted each spatial interpolation by the regional relationship between PCoA scores and elevation. In the regions where this relationship was relatively stronger (as measured by the R2 of linear regression model), the interpolated PCoA scores depend more on elevation, whereas in regions where this relationship was weak, the interpolated PCoA scores depend more on spatial similarity among sites with pollen profiles. ...