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Influence of elevation on bioregionalisation: A case study of the Sino-Himalayan flora

Citation

Liu, Yun et al. (2021), Influence of elevation on bioregionalisation: A case study of the Sino-Himalayan flora, Dryad, Dataset, https://doi.org/10.5061/dryad.hhmgqnkh0

Abstract

Aim: Elevation is an important factor that influences bioregionalisation in mountainous areas, but its effects are not well known. Taking the Sino-Himalayan flora as a case, we aimed to test the effect of elevation on bioregionalisation and provide a regionalisation scheme of the Sino-Himalayan flora.

Location: The Sino-Himalaya (East Himalaya, the Hengduan Mountains and the Yunnan Plateau in China).

Taxon: Angiosperms.

Methods: We compiled distribution data and elevation ranges of angiosperms in the Sino-Himalaya and adjacent areas and reconstructed a species-level phylogenetic tree of 19,313 angiosperm species. The area was divided into 398 grid cells, each 1 × 1°. Nine datasets of different elevation ranges were then used to delineate the flora of the Sino-Himalaya and adjacent areas using the phylogenetic dissimilarity approach.

Results: A comparison of nine regionalisation schemes of the Sino-Himalayan flora based on different elevation range datasets revealed that more than half of grid cells were allocated to more than one subregion. Most of these grid cells were located in areas with a wide range of elevation and/or at the boundaries between subregions. After revising the subregion allocations of eight shifting grid cells, we generated a phylogeny- and elevation-based regionalisation scheme of three regions, comprising eight subregions, for the Sino-Himalayan flora.

Main conclusions: By integrating phylogenetic and elevational information, the Sino-Himalaya can be divided into three floristic regions: the Yunnan Plateau region, the Hengduan Mountains region, and the East Himalaya region. Our study provides novel insights into the regionalisation of the flora and highlights the importance of incorporating elevation data in the bioregionalisation of areas with a broad elevational range.

Methods

We compiled distribution data and elevation ranges of angiosperms in the Sino-Himalaya and adjacent areas from national and local floras (Appendix S1: Table S1.1), specimen records in the Chinese Virtual Herbarium (CVH, http://www.cvh.ac.cn/) and the studies of Zhang, Ye, et al. (2016) and Lu et al. (2018). Species names were standardised following the Flora of China (Wu et al., 1994–2013). Intraspecific categories were integrated into respective species. Non-native species were excluded. The distribution data for all species were standardised to the level of administrative county.

To reduce the effects of unbalanced sampling within counties, we converted county data into grid data. The geographical distribution layer was set as a spatial gridded layer with a resolution of one degree (~110 × 110 km at the equator). We generated a species-level presence–absence matrix for the 398 grid cells of the Sino-Himalaya and adjacent areas after matching counties and grids. Elevation range and average elevation were obtained for each grid cell. After excluding species lacking elevation data, we obtained the gridded distribution database containing 550,451 grid-level distribution records of 19,313 angiosperm species (dataset A).

Usage Notes

README.txt contains parameter descriptions for each of the following files:

fulltree.tree file contains the phylogenetic tree of 19,313 angiosperm species in the Sino-Himalaya and adjacent areas.

distribution_elevation_data.csv file contains the gridded distribution information and the elevation information for the angiosperms in the Sino-Himalaya and adjacent areas.

Funding

National Natural Science Foundation of China, Award: NSFC31590822

National Natural Science Foundation of China, Award: NSFC31800178

Strategic Priority Research Programme of the Chinese Academy of Sciences, Award: XDB31000000

Strategic Priority Research Programme of the Chinese Academy of Sciences, Award: XDA19050103

International Partnership Programme of the Chinese Academy of Sciences, Award: 151853KYSB20190027

Youth Innovation Promotion Association of the Chinese Academy of Sciences, Award: 2021077

Young Elite Scientist Sponsorship Programme by China Association for Science and Technology, Award: 2018QNRC001