A dataset on the diversity and distribution of rodents and shrews in China
The protocols given here have been adapted from previously published literature retrieval efforts. A guide to our extraction has been included in Figure 1 and shows the overall process we followed to produce this dataset. We searched PubMed and ISI Web of Science for literature published in English, and China National Knowledge Infrastructure (CNKI), VIP Database and Wanfang Database as the main sources published in Chinese between 1950 and 2021. We used the following search terms: “Rodent”, “Mouse”, “Rat”, “Shrew” and “China”, in combination with each of the 18 families of rodent and shrew species (in English or Chinese). A secondary manual search of the references cited in these articles was also performed to find relevant articles. To complete, the historical collection of rodent-related documents that have been documented in Chinese publications, with the multiple books (Fauna sinica, mammal, vol.6(2), cricetidae15A guide to the mammals of China16Distribution of Mammal Species in China17Colored Atlas of Chinese Mammals18Mammals of Tibet19China Biodiversity Red List: Vertebrates, Volume I, Mammal20Atlas of Epidemiology of Diseases with Natural Focus in China21Beijing wildlife22Wildlife of Guizhou23, etc.), were also examined. In addition, we standardize the species lists of China mammals from a guide to China mammals16 and Catalog of Mammals in China (2021)24.
Two researchers independently reviewed the information for each rodent and shrew species, and entered the data into a standardized sheet to establish a database. Discrepancies were resolved through discussion between the two researchers and facilitated by a third principal investigator to reach consensus, and for records where the time of discovery and location were missing from the articles, we contacted the corresponding authors. for detailed information. Species lists have been reviewed by specialists in small mammal groups20. Detailed descriptions of the literature search and schematic procedures are provided in Fig. 1.
Using the keyword search, a total of 58,021 articles were retrieved for selection, comprising 15,959 articles from the English database and 42,062 articles from the Chinese database. For the second screening step, the abstracts of all returned references were screened to exclude those that reported only clinical cases or laboratory data or that reported diagnostic development, without mentioning any specific rodent species. For the third stage of selection, we reviewed in detail the full text of all the remaining 720 articles, from which a total of 430 Chinese articles and 290 English articles were determined as eligible for data extraction (Fig. 1 ). The first Chinese and English publications were published in 1958 and 1986, respectively, and a sharp increase in literature with records of rodents and shrews was observed after 1980 (Fig. 2). Key data were extracted from articles, books, field surveys and websites obtained: (i) name of rodent and shrew species, (ii) geographical location information reported at province, city and county, (iii) time of identification and reports, (iv) source of articles, books, field surveys and websites. All data was entered into an Excel spreadsheet for downstream analysis. After the initial data entry, a second edit by two people was performed to correct errors and remove duplicates. In particular, historical changes in rodent and shrew taxonomy have been taken into account and standardized terminology has been applied for the same species.24. Where necessary, the names of study sites that were historically used have been updated with the current name. A total of 13,911 records were compiled, with multiple records in each province presented over the years of study (Fig. 2).
Location information was manually extracted at the highest resolution for each rodent and shrew record and then categorized into three administrative levels such as province, city, and county. For a species, we remain only one record at the county level if various geographic locations (e.g., township, village) were reported within a county, then we remove duplicates for those records in the same county from different studies. For publications that did not report an administrative region or specific coordinates, for example, a scenic area or a mountain, we searched for the coordinates of survey sites using Baidu Map (https://map.baidu.com/) and recorded their coordinates, which were linked to the county map using ArcGIS 10.7 software (ESRI Inc., Redlands, CA, USA) to obtain county-level location information. Maps of Chinese administrative boundaries of province, city and county (2015) were collected from the Chinese Academy of Sciences Resource and Environment Data Cloud Platform (http://www.resdc.cn). A total of 13,911 records were identified, including 1,419, 1,087, and 11,405 records at the province, city, and county levels, respectively. A “Location Level” field was used to accurately demonstrate the spatial resolution such as province, city, or county for each record in our dataset. This classification allowed separate extraction and use at different spatial levels for various purposes. We used RStudio Version 1.4.1103 and ArcGIS 10.7 software to analyze and statistically visualize the geographical data obtained.