SYKO Herbarium Marchantiophyta Collection

Study Extent

Bryophytes subdivision of SYKO is divided into two collections: mosses and liverworts. According to SYKO bryophytes subdivision register (maintained manually since 1969) there were 58,184 specimens (45,198 mosses and 12,986 liverworts) at the beginning of August 2020.

Sampling

Bryophyte herbarium samples were collected during two main types of field work: floristic explorations and vegetation studies. Field samples are separated into storage specimens during the species identification in a way that in each specimen was a minimum number of bryophyte species. Two label copies are generated for each sample. One copy of the label was fixed on a bag with a dried sample, the second was stored in a separate storage for labels (library card catalog cabinet is used). Each sample was assigned a catalog number. The catalog numbers were incrementing since the organization of the bryophyte subdivision in the SYKO herbarium. Information about the label catalog number, date of collection, name of the collection place, species name, field number, and habitat were entered in the register books. The labels from label storage were used for digitization. The label images were obtained with digital camera. Images were uploaded to server and their filenames to the label database. The database web interface written specifically for this project was used for manual label data recognition and interpretation. The following minimum set of data were deciphered (in DarwinCore terms): scientificName, recordedBy, identifiedBy, day, month, year, catalogNumber, decimalLatitude, decimalLongitude.

Quality Control

Species identification. The species were identified by bryologists from the Institute of Biology of Komi Scientific Centre of the Ural Branch of the Russian Academy of Sciences. The correctness of species identification and confirmation for many taxa was carried out by well-known taxonomy specialists. Label images quality. Each image of the label was checked for readability by operators who deciphered label data. Images that were out of focus or had extraneous objects in the frame were deleted from the database. It was possible to recapture bad label images only if the catalog number of the label was detectable on discarded images. In other cases (about 6% of the total number of labels in the moss collection), the second round of label image capturing will be performed later (after forming a list of missed labels with help of label register). Check of georeferencing. Occurrences locations were added to map with the OpenStreetMap layer and with Russian regions borders polygon layers in QGIS software. The names of regions were assigned to each occurrence with help of “Point Sampling Tool” QGIS plugin. The occurrences located out of land border of any Russia region and occurrences located far from the borders of Komi Republic were subject to verification. Text recognition quality. All label data recognized by operators were checked visually for each label image. Special boolean-like fields were added to database table with main label information: the check was carried out (yes / no), data clarification is required (yes / no). The label data need to checked were divided in two groups: 1) the collection date and catalog number, 2) names of taxa indicated on the label and the names of people who collected the sample and who identified the species. Additional verification of collection dates and collectors names was carried out during labels georeferencing. It is known that one collector could not be in the points located more than several kilometers from each over during the same day. After main array of labels digitizing and recognition it became possible to compare series of labels to identify and correct obvious errors that were made not only during image data recognition but also errors that were made by laboratory technicians during manual filling out label blanks. In the latter case corrected information was added in database and label was marked for replacement in near future. Taxonomy validation. Verbatim taxon names indicated in labels in many cases were out date and not valid. In our case, only professional bryologists were the operators for taxon name recognition so verbatim names were corrected on the fly during data entering in database. The next step of taxon name checking was normalizing species names against the GBIF backbone (https://www.gbif.org/tools/species-lookup). The GBIF backbone normalized species names and higher taxonomy were updated manually by our bryologists to bring the taxon name usage in concordance with the latest moss checklists. Dataset validation. The publication ready Darwin Core compliant dataset was generated as csv-file by Python script which included SQL queries to the database. This file was checked for errors manually with data filtering function of spreadsheet software and automatically with the GBIF Data Validator service (https://www.gbif.org/tools/data-validator).

Method steps

1. The database and web application for database administration were created with MariaDB (https://mariadb.com) and Django framework (https://www.djangoproject.com).
2. Batch of labels images up to several thousands JPEG files were processed simultaneously. Each image was cropped to remove most of the background so the image size became approximately 2000×1500 pixels. White balance of all images was automatically adjusted with Fred Weinhaus ‘autowhite’ script for ImageMagick software (http://www.fmwconcepts.com/imagemagick/autowhite).
3. Cropped images were uploaded to server and their file path names were added in label database.
4. Operator decrypted label data with web application. Different web forms for different types of data were used: entering catalog number and collection date; entering the names of taxa; entering the names of the collectors and persons who carried out the identification of taxa; input of geographic coordinates. Dates were entered as three separate numbers: day, month and year. This format of dates storage allowed the processing of labels with omitted days or month in collection date. Qualified bryologists entered the names of taxa, the names of the collectors and the persons identified the species of mosses. Georeferencing of labels was performed by an engineer with cartographic skills. In some cases, for a more accurate determination of coordinates, it was possible to question the collector of the sample.
5. All entered data (excluding geographic coordinates) were checked with special forms in web application. Label images were compared with entered data and errors were corrected simultaneously or marked for correction later.

1. Bryophyta
   rank: phylum
2. Marchantiophyta
   rank: phylum
3. Bryopsida
   rank: class
4. Jungermanniopsida
   rank: class
5. Marchantiopsida
   rank: class

The first portion (850 labels) of SYKO Herbarium Marchantiophyta collection were collected in small area - 2-3 square kilometers on the territory of Pechora-Ilych Nature Reserve.