| General Information: |
| Title: | Biodiversity dataset of vascular plants and birds in Chinese urban greenspace |
| Identifier: | ERDP-2021-02.1.1 |
| Abstract: |
Urban greenspace plays an important role in buffering the threat of urbanization to biodiversity. Biodiversity research in urban greenspace is essential for understanding the impacts of human activities on biodiversity and the associated ecosystem functioning and services in urban ecosystems. However, we lack large-scale biodiversity datasets in urban greenspace for macroecological studies. In this paper, we provided a comprehensive biodiversity dataset of vascular plant and bird species in 286 university campuses and 118 urban parks in 143 cities in China, covering a wide range with different levels of urbanization (20–50°N in latitude and 81– 130°E in longitude). Plant species were classified into different growth forms, and bird species were identified as resident or migrant. In total, the dataset contains 46,558 occurrence records for 5,915 plant species (about 18% of all Chinese vascular plant species) and 6,423 records for 546 bird species (around 40% of all recorded bird species in China). This dataset provides an important data source for evaluating the anthropogenic effects of biodiversity changes and advancing urban ecology.
The complete data set for this abstract published in the Data Paper section of the journal is available in electronic format in MetaCat in JaLTER at http://db.cger.nies.go.jp/JaLTER/metacat/metacat/ERDP-2021-02.1/jalter-en.
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| Keywords: |
- Urban greenspace
- biodiversity conservation
- human activities
- urban biodiversity
- biotic homogenization
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Involved Parties
| Data Set Owners: |
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Individual: | Xin Wang |
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Organization: | Zhejiang Tiantong Forest Ecosystem National Observation and Research Station, School of Ecological and Environmental Sciences, East China Normal University |
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Address: |
| 500 Dongchuan Road, |
| Shanghai, 200241 China |
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Email Address:
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Individual: | Zhichun Zhao |
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Organization: | Zhejiang Tiantong Forest Ecosystem National Observation and Research Station, School of Ecological and Environmental Sciences, East China Normal University |
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Address: |
| 500 Dongchuan Road, |
| Shanghai, 200241 China |
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Email Address:
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Individual: | Jian Zhang |
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Organization: | Zhejiang Tiantong Forest Ecosystem National Observation and Research Station, School of Ecological and Environmental Sciences, East China Normal University |
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Address: |
| 500 Dongchuan Road, |
| Shanghai, 200241 China |
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Email Address:
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Individual: | Jun Liu |
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Organization: | Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau & Inner Mongolia Key Laboratory of Grassland Ecology, School of Ecology and |
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Address: |
| 235 University West Road, Hohhot, |
| Inner Mongolia 010021 China |
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Email Address:
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Individual: | Chenxia Liang |
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Organization: | Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau & Inner Mongolia Key Laboratory of Grassland Ecology, School of Ecology and |
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Address: |
| 235 University West Road, Hohhot, |
| Inner Mongolia 010021 China |
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Email Address:
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Individual: | Gang Feng |
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Organization: | Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau & Inner Mongolia Key Laboratory of Grassland Ecology, School of Ecology and |
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Address: |
| 235 University West Road, Hohhot, |
| Inner Mongolia 010021 China |
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Email Address:
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| Data Set Contacts: |
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Individual: | Jian Zhang |
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Organization: | Zhejiang Tiantong Forest Ecosystem National Observation and Research Station, School of Ecological and Environmental Sciences, East China Normal University |
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Address: |
| 500 Dongchuan Road, |
| Shanghai, 200241 China |
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Email Address:
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Individual: | Gang Feng |
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Organization: | Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau & Inner Mongolia Key Laboratory of Grassland Ecology, School of Ecology and |
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Address: |
| 235 University West Road, Hohhot, |
| Inner Mongolia 010021 China |
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Email Address:
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Data Set Characteristics
| Geographic Region: |
| Geographic Description: | Geographic coordinate system is WGS84 |
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Bounding Coordinates:
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| West: | 81.29 degrees
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| East: | 129.63 degrees
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| North: | 49.61 degrees
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| South: | 19.99 degrees
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Sampling, Processing and Quality Control Methods
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Step by Step Procedures
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| Step 1: |
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Description:
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Biodiversity data collection
For plant data, the literature search was conducted using China National Knowledge Infrastructure (CNKI, 2021) with the following search terms in Chinese: “University” or “Campus” or “College” and “plant” (“大学” or “校园” or “学院” and “植物”). We also collected data from published books, online databases and research reports which contain campus plant list. Finally, 120 papers, 34 published books, 3 eBooks, 37 online databases, and 18 research reports were screened out, including plant list data in 254 university campuses. For bird occurrence data, we used Baidu Xueshu (Baidu, 2021) to search for papers published about bird diversity in university campuses and urban parks, and 59 published papers were selected. Our own bird survey data in 15 parks within three cities in Inner Mongolia (Hohhot, Baotou,.29°E–129.63°E.99°N–49.61°N Geographic coordinate system is WGS84.5 Taxonomic coverage and Ordos) were also included. Finally, bird list data covered 53 university campuses and 118 urban parks.
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| Step 2: |
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Description:
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Environmental variables collection
The geographic coordinates, area and established years of university campuses and urban parks were extracted from the relevant primary data sources. If the geographic coordinates were missing in some literature, we extracted them from Google Earth (Google, 2021) by using the names of university campuses and urban parks. We also searched the area and established year from official homepages of university campuses and urban parks as a supplement of the primary data source. The rest missing area information was calculated based on urban park and campus area boundaries on Google Earth. Elevations were extracted from EarthEnv-DEM90 at 90-m spatial resolution (Robinson et al., 2014). Mean annual temperature and mean annual precipitation were extracted from WorldClim version 2 at l-km spatial resolution (Fick & Hijmans, 2017). The established year information for several parks was missing, and it was represented by ‘NA’ in the dataset.
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| Step 3: |
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Description:
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Taxonomy
To standardize the taxonomic names of plants, we followed The Plant List v1.1 (TPL, 2013) to search for the currently accepted names and synonyms. iPlant’s The Taxonomic Name Resolution Service (http://tnrs.iplantcollaborative.org/) and Flora Reipublicae Popularis Sinicae (http://frps.iplant.cn/) were also used to validate when species cannot be found in The Plant List. Data were collected at specie-level and records with genus- or family-level were excluded. As a result, 46,558 occurrences records of plant were compiled. To validate the taxonomic names of birds, we referred to the data from a checklist on the classification and distribution of the birds of China (Zheng et al., 2011) and from a paper about global diversity of birds (Jetz et al., 2012). In total, we compiled 6,423 occurrences records for birds. The growth forms of plant species were classified into trees, shrubs and herbs based on the descriptions in the Flora of China and relevant literature. Bryophytes and aquatic plants were excluded in this dataset. Among them, 19,017 records contained 1,819 tree species, 9,236 records included 1,156 shrub species, and 18,305 records were about 2,941 herb species.
To group the plant species into native and non-native species at regional level, we divided China into seven geographic regions, including eastern, southern, northern, central, northeastern, southwestern and northwestern China. Thess regions represent different climatic zones in China to a certain extent (Zhu et al., 2019). If species occurred in a region naturally, they were defined as native species in this region. Likewise, species that occurred beyond their native distribution ranges were classified as non-native species in this region (Avolio et al., 2020; Padullés Cubino et al., 2019). The nativeness status of plant species in a region was identified according to the Flora of China (http://foc.iplant.cn/), the Database of Invasive Alien Species in China (http://www.chinaias.cn), and the catalogue of cultivate plants in China (Lin 2018). Finally, 26,267 records of native plants and 20,291 records of non-native plants were aggregated.
Migrant status of birds was grouped into “resident” (documented as “R” in the dataset) or “migrant” (documented as “M” in the dataset) according to the descriptions of published papers. If the migrant status of a species cannot be classified, we reported it as ‘NA’ in the dataset. Lastly, 1,036 resident bird records in campuses and 2,030 resident bird records in urban parks were documented. Bird survey time of university campuses and urban parks were extracted from the relevant primary data sources, and a total of 145 university campuses and urban parks cover the whole-year bird survey.
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| Step 4: |
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Description:
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Data verification procedures
a) Data quality control
All data was digitized by extracting biodiversity information from original sources into the Excel spreadsheet. Before adding into the dataset, the survey methods of original literature were evaluated. Specifically, we excluded literature with sample surveys, only the literature with survey area covering the whole campus or parks was considered. To ensure the integrity of compiled data, we calculated species richness of each locality from compiled data, and compared it with the reported richness of the original literature. All species scientific names were carefully checked for typing errors and misspellings.
b) Taxonomic and status validation
The species names were standardized as described in the Methods section to ensure the identification validity based on the consistent standard. After having validated the taxonomic names, the species list was checked to avoid that synonym representing the same species appearing one locality.
c) Geographic location and variables validation
In order to improve the accuracy of the location, we deduced the descriptive position backward from longitude and latitude and compared it with the location name given in the original literature. In case of mismatch, the coordinates were double-checked. For the variables, we sorted the spreadsheet to identify extreme values of each variable, and double-checked them.
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Data Set Usage Rights
| License
This dataset is provided under a Creative Commons Attribution 4.0 International License (CC BY-NC 4.0; https://creativecommons.org/licenses/by-nc/4.0/legalcode).
Location of storage
https://db.cger.nies.go.jp/JaLTER/metacat/metacat/ERDP-2021-02.1/jalter-en |
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