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Data from: The patterns of vascular plant discoveries in China

Citation

Lu, Muyang; Gao, Lianming; Li, Hongtao; He, Fangliang (2022), Data from: The patterns of vascular plant discoveries in China, Dryad, Dataset, https://doi.org/10.5061/dryad.4b8gthtd1

Abstract

Aim

1. To understand geographic patterns of species discovery by examining the effect of growth form, range size, and geographic distribution on discovery probability of vascular plant species in China.

2. To find out which taxa harbor the largest number of undiscovered species and where those species locate.

3. To find out the determinants of province-level mean discovery time and inventory completeness.

Location: China

Methods

We compiled the discovery time and province-level geographic distributions of ~31000 vascular plant species described between 1753 and 2013 from Flora of China. We used a Cox proportional hazard model to determine the biological and geographic correlates of discovery probability. Accumulation curves of species discoveries were fitted by a logistic discovery model to estimate inventory completeness of different growth forms and of different provinces. We then used linear regression to identify the determinants of mean discovery time, and beta regression to identify the determinants of inventory completeness.

Results

We found that species with larger range size and distributed in northeastern part of China have a higher discovery probability. Coastal species were discovered earlier than inland species. Trees and shrubs of seed plants have the highest discovery probability while ferns have the lowest discovery probability. Herbs have the largest number of undiscovered species in China. Most undiscovered species will be found in southwest China, where three global biodiversity hotspots locate. Spatial patterns of mean discovery time and inventory completeness are mainly driven by the total number of species, human population density in an area, latitude and longitude of a province.

Main conclusions

Socio-economic factors primarily determine the discovery patterns of vascular plants in China. Undiscovered species are most likely to be narrow-ranged, inconspicuous endemic species such as herbs and ferns, which are prone to extinctions and locate in biodiversity hotspots in southwestern China.

Methods

Description time, genus-level growth form and province-level geographic distributions are collected from Flora of China (2013; http://www.efloras.org/). Other geograhic variables including longitude, latitude, coastal distribution and range size are derived from province-level distributions.

Usage Notes

genus: genus name of the species.

species: scientific name of the species.

date: the first description time of the species. If a species was previously published in another name but later recognized as a synonym, the description time of the synonym was used.

growthform.genus: the dominant genus-level growth form of the species. If a genus have species with different growthforms, the most common growthfrom among the species is used. For simplicility of analysis, epiphytic and epilithic plants are merged to the group of herbs, bamboos and palms are merged to the group of shurbs, palm trees is merger to the group of trees.minlon, meanlon, maxlon: minimum, mean and maximum longtitude of a species range derived from its province-level distributions.

minlat, meanlat, maxlat: minimum, mean and maximum latitude of a species range derived from its province-level distributions.

coast: whether the species' distribution includes a coastal province.

rangesize: the geographic range size (km^2) of the species calculated from its province-level distribution.

from 'heilongjiang' to 'hainan' are the province-level presence index of the species: 1 indicates the species is distributed in the province, NA indicates not in the province

Funding

Natural Sciences and Engineering Research Council of Canada

Sun Yat-sen University

East China Normal University

Strategic Priority Research Program of Chinese Academy of Sciences, Award: XDB31000000

Science and Technology Basic Resources Investigation Program of China, Award: 2019FY100900

Strategic Priority Research Program of Chinese Academy of Sciences, Award: XDB31000000

Science and Technology Basic Resources Investigation Program of China, Award: 2019FY100900