Macrophyte diversity in rivers and streams of the Vologda Region and several other regions of Russia

Abstract

The data paper contains the authors’ materials on the diversity of macrophytes, macroscopic plants regardless of their taxonomic position, in rivers and streams of East European Russia and Western Siberia. These data, collected on 247 rivers and 32 streams in 13 administrative regions of the Russian Federation, were provided as an occurrence dataset presented in the form of GBIF-mediated data. The main portion of the data was obtained in water objects of the Vologda Region (5201 occurrences). In addition, occurrences from the Arkhangelsk Region (347 occurrences), Khanty-Mansi Autonomous Okrug (159), Yaroslavl Region (132), Novgorod Region (97), Kostroma Region (41), Republic of Karelia (31), Sverdlovsk Region (29), Komi Republic (28), Orenburg Region (26), Chelyabinsk Region (22), Voronezh Region (22) and Tyumen Region (18) were given. The studies were carried out mainly in the southern and middle taiga and, to a lesser extent, in the northern taiga and the forest-steppe. The analysed watercourses belong to five drainage basins: the Azov Sea, the Baltic Sea, the White Sea, the Caspian Sea and the Kara Sea. The dataset contains materials on the diversity of Plantae (6094 occurrences) and Chromista (59 occurrences). This paper, in a standardised form, summarises mostly unpublished materials on the biodiversity of lotic ecosystems. The paper summarises the data obtained in long-term studies of phytodiversity in a range of rivers and streams of East European Russia and, fragmentarily, Western Siberia. A total of 6153 occurrences were included in the dataset. According to the GBIF taxonomic backbone, the dataset comprises 292 taxa, including 280 lower-rank taxa (species, subspecies, varieties) and 12 taxa identified to the genus level. All the occurrences are published openly through the Global Biodiversity Information Facility (GBIF) for the first time. Most of the data were stored in field diaries and, thus, by adding the data in GBIF, we believe that other researchers could benefit from it.