DOI: 10.5281/zenodo.1491224
title: Chronicles of Nature Calendar: A long-term and large-scale multitaxon database on phenology

description: We present a large-scale and long-term dataset that can be used to examine community-level spatial variation in phenological dynamics and its climatic drivers. The database consists of 510,165 observation dates collected in 472 localities in the Russian Federation, Ukraine, Uzbekistan, Belarus and Kyrgyzstan (Fig. 1) over a 129-year period (from 1890 to 2018). During this period, researchers intensively conducted regular observations to record dates at which a predefined list of phenological and climatic events (Table 1) occurred. Although 96% of the observations were acquired from 1960 onwards, a few time series are very long. Events measured for plants include e.g. the onset days of leaf unfolding, first flowering time, and leaf fall; for birds they include e.g. days for first spring and last autumn occurrences; for insects, amphibians, reptiles and fungi they include e.g. day of first occurrence in the spring. The plant data were acquired in fixed plots, and the bird data along established routes. Climatic events were recorded as calendar dates when those events took place. Of all phenological dates, 88% were collected by research personnel of nature protected areas and national parks, who followed a systematic protocol. Thus, sampling effort remained nearly constant over time. The remaining 12% of the observations came from a well-established volunteer phenological network of volunteers, who followed a similar systematic protocol.

The recording scheme implemented at nature reserves offers unique opportunities for addressing community-level change across replicate local communities. These data have been systematically collected not as independent monitoring efforts, but using a shared and carefully standardized protocol adapted for each local community. Thus, variability in observation effort is of much less concern than in most other distributed cross-taxon phenological monitoring schemes. To enable analyses of higher-level taxonomical groups, we have included taxonomic classifications for the species in the database.

The compilation of the data into a common database was conducted by the database coordinators (EM and CL) in Helsinki (Finland). Those participants that already held the data in digital format submitted it in the original format, and those that had the data only in paper format digitized it using Excel-based templates developed in the project meetings. Submitted data were processed by the database coordinators according to the following steps:

 


	The data were formatted so that each observation (the phenological date of a particular event in a particular locality and year) formed one row in the data table (e.g. un-pivoting tables that involved several years as the columns). The phenological event names were split into event type (e.g. “first occurrence“) and species name.
	The event type names (provided originally typically in Russian) were translated into English and the species names (usually provided in Russian) were identified to scientific names, using dictionaries that were partly developed and verified in the project meetings. All scientific names were periodically verified by mapping them to the Global Biodiversity Information Facility (GBIF) backbone taxonomy<sup>25</sup>.
	We associated each data record with the following set of information fields: (1) project name, i.e. the source organization, (2) dataset name, (3) locality name, (4) unique taxon identifier, (5) scientific taxon name, and (6) event type.
	We imported the data records in the main database (maintained as an EarthCape database at https://ecn.ecdb.io). During the import, the taxonomic names, locality names, and dataset names were matched against already existing records.


The database is organized in six datasets: (A) a classification of taxa included; (B) a classification of climatic events included; (C) information on the study sites; (D) the phenology data, (E) an information sources table for classification data, and (F) an information sources table for phenology data. All tables are in csv format (columns separated by comas) (Data Citation 1). The tables can be linked to each other using the unique study site names and the unique identifiers for species and climatic evens.


	The taxonomy table has twelve columns: (1) the unique identifier of the taxon, (2) the scientific name of the taxon, (3) the highest taxonomical level to which the taxon is described, (4) kingdom, (5) phylum, (6) class, (7) order, (8) family, (9) genus, (10) species, (11) The GBIF key for the taxon, and (12) the GBIF status of the taxon.
	The climatic events table has two columns: (1) the type of the climatic event, (2) the type of the climatic event (e.g. related to temperature, snow or ice).
	The study sites table has three columns: (1) the name of the study site, (2) latitude, (3) longitude.
	The phenology table has nine columns: (1) the name of the project in which the data were collected (2) the name of the dataset to which the data belongs to, (3) the name of the study site in which the data were collected, (4) the unique identifier of the taxon (“Climate” for climatic events), (5) the scientific name of taxon (climatic group for climatic events), (6) the type of the event, (7) the year, (8) the date of the observation as the number of days since January 1<sup>st</sup> in the focal year, and (9) a column indicating if any quality issues were identified with the data.
	The information sources table for phenology data has three columns: (1) the name of the project, (2) the type of information sources (mostly Chronicles of Nature Books of the participating organizations) (3) the references used to extract the phenology data.