Fungal literature records database of the Northern West Siberia (Russia)

Study Extent

The digitisation was aimed at summarizing the species occurrences of fungi and fungi-related organisms accumulated in the course of previous mycological studies and published in peer-reviewed scientific literature. The geography extended the Northern part of West Siberia, in the administrative borders of two regions (Yamalo-Nenets Autonomous Okrug and Khanty-Masi Autonomous Okrug-Yugra). Over 460 publications were reviewed and the species occurrence records were extracted from about 140 selected works. About 80% of species occurrences accumulated in the database were relatively recent, i.e. published in the literature since the beginning of 21th century.

Sampling

Methods of sampling vary in different reviewed publications, but generally follow the protocols of Mueller et al. (2004) for different taxonomical and ecological groups. The majority of the records were made using direct observation of fruiting structures. The exception are a few studies of micromycetes and yeasts where cultivation techniques were applied. No molecular (environmental sampling) methods were used until present to reveal molecular diversity of fungi in the region. Plot-based monitoring of terrestrial and lignicolous macrofungi was organized by some researchers, providing estimates of quantitative parameters and temporal dynamics of fungal communities. The majority of fungal occurrence records were accompanied by accessioning of specimens in fungaria, the specimen numbers are rarely reported in publications, however. The specimens are stored in different collections within and outside the region where the researchers working (mentioning the main collections of LE - the Komarov Botanical Institute, Saint-Petersburg; SVER - Institute of Ecology of Animals and Plants, Ekaterinburg; NSK - Central Siberian Botanical Garden, Novosibirsk, and others).

Quality Control

The original species identifications from the published works were recorded in the database, no attempt was made for the revision of the species identification accuracy. A single author revised the species list and corrected wrong original identifications: the corrected names were added in identificationRemarks field, totally 15 records in the database. The incorrect spelling of taxa was verified using GBIF Species Matching tool at the later stage of the database compilation. Possible georeference errors at the scale of the region were corrected using QGIS software (https://qgis.org/en/site) by eliminating the outliers. Depending on the quality of georeference provided in publications, the uncertainty was estimated as follows: 1) the coordinate of a fruiting structure or a plot provided in the publication gave the uncertainty about 3-10 meters; 2) the coordinate of the field work locality provided in publication gave the uncertainty about 500 m – 5 km; 3) the report of the species presence in the district or the region gave the central coordinate of the area with the uncertainty radius to include its borders. The occurrences with large uncertainties weren’t eliminated, as they can still be important in global context.

Method steps

1. The bibliography of related publications was compiled using Zotero bibliographic manager (https://www.zotero.org). Only published works (peer-reviewed papers, conference proceedings, PhD theses, monographs or book chapters) were selected. 
2. The layout of the FuNWS database was made using Google Sheets software. Such database could be filled simultaneously by several specialists, and a common data format will be provided (Filippova and Bolshakov 2017).
3. The Darwin Core standard was applied to the database structure to accomodate the relevant information extracted from the publications.
4. From the available bibliography of publications related to the region, only works with species occurrences were selected for the databasing purpose. We decided to include all different sorts of occurrence records, from a mere mention of the species within the administrative region, to the annotated species lists with exact locations of the records.
5. All occurrence records were georeferenced, either from the coordinates provided in the paper, or from the verbal description of the field work locality. The georeferencing of the verbal descriptions was made using Yandex (https://yandex.ru/maps) or Google (https://maps.google.ru/maps) maps services.
6. The coordinate uncertainty was estimated according to the algorithm described above (see Quality control paragraph).
7. The locality names reported in Russian were translated in English and written in the «locality» field. Russian descriptions were reserved in the field «verbatimLocality» for accuracy.
8. When possible, the «eventDate» was extracted from the species records annotation data. Whenever this information was absent, the date of the publication was used instead.
9. The ecological features, habitat and substrates preferences were written in the «habitat» and reserved in Russian.
10. The original scientific names reported in publications were filled in the «originalNameUsage». Correction of the spelling errors was made using the GBIF Species Matching tool.
11. The GBIF Species matching tool was used to create the additional fields of taxonomic hierarchy from species to kingdom, to fill in the «taxonRank» field and to make synonymization according to the GBIF backbone.
12. The taxonomic and spatial analyses of the final database were made using Microsoft Excel, QGIS and R software (https://www.r-project.org).
13. To track the digitization process, a working database was created. Each bibliographic record has a series of fields to describe the digitization process and its results: the total number of extracted occurrence records, general description of the occurrence quality, presence of observation date, presence of specimen number, and details of georeferencing.

According to the database summary report, there are about 3358 species revealed in Northern West Siberia up-to-date, representing 1020 genera, 293 families, 94 orders, 25 classes, 6 phyla and 2 kingdoms (Fungi, Protozoa). The richest studied classes by number of occurrences are Agaricomycetes (60%) and Lecanoromycetes (30%). The richest ten families by number of species are Parmeliaceae (144 species), Russulaceae (111), Physciaceae (99), Cortinariaceae (96), Tricholomataceae (93), Polyporaceae (84), Lecanoraceae (83), Cladoniaceae (81), Hymenogastraceae (79), Ramalinaceae (67 species).

1. Fungi
   rank: kingdom
2. Protozoa
   rank: kingdom

The dataset is limited by the administrative borders of two regions (Yamalo-Nenets Autonomous Okrug, Khanty-Masi Autonomous Okrug-Yugra). However, in cases when the digitized work contained records from other regions, they were also entered into the database (there are about 1300 such records in total). The region occupies the central to northern part of the West Siberian Plain. The area extends for about 1.5 thousand km from the West to the East from Eastern slopes of the Ural mountains to Yenisey river and from North to South – about 1.5 thousand km. The total area equals about 1.3 mln km2. The relief of the region is mainly a plain, but western part of the area is occupied by the Ural mountains with the highest points reaching up to 2000 m. The three vegetation zones (taiga, forest-thundra and thundra) and well developed peatland cover represent the plain, while the mountain vegetation of the Urals changes from taiga to alpine zones. In the southern half of the area (Yugra region), most administrative divisions were studied for fungi, but the intensity of the research varies. 80% of all records in the database have been made at three districts (Khanty-Mansiyskiy, Surgutskiy, Berezovskiy, Sovetskiy). Totally about 13 thousand records or 60% coming presently from Yugra region. The northern part of the region presented by less number of records in the database (about 6 thousand, or 27%). The research is mainly concentrated in two districts (Priuralskiy - 66% of records and Yamalskiy - 22%). Generally, localities of the studies are situated randomly, no attempts of regular studies using grid pattern have been done before. The areas under different kinds of nature protection are better studied compared to others: about half of all records in the database coming from 13 protected areas.