Diversity and distribution of Oxytropis DC. (Fabaceae) species in Asian Russia

Abstract Background The dataset providing information on the geographic distribution of Oxytropis species on the territory of Asian Russia is discussed. The data were extracted from different sources including prominent floras and check-lists, Red Data books, published research on congeneric species and authors’ field observations and mainly cover less-studied, remote regions of Russia. The dataset should be of value to applied, basic and theoretical plant biologists and ecologists interested in the Oxytropis species. New information The dataset includes 5172 distribution records for 143 species and 15 subspecies of genus Oxytropis DC. (Fabaceae Lindl.) in Asian Russia. The dataset fills gaps in the distribution of locoweeds in the study area and contains precise coordinates for many of rare and endemic species.


Introduction
Oxytropis DC. or Locoweeds is one of the richest genera in the flora of non-tropical Asia (Malyshev 2008). There are 378 species and subspecies in northern Eurasia (Yakovlev et al. 1996), 154 species and 24 subspecies in Siberia and the Russian Far East (Baikov 2012). However, according to the latest revision for the Asian part of Russia, the genus includes 142 species and 24 subspecies (Malyshev 2008).
Leonid Malyshev (Malyshev 2006) considered Oxytropis as a critical genus in plant biogeography because the areas with a high diversity of these species in north and central Asia differed considerably from each other by their quaternary history as well as their ecological and geographical specialisation. The highest diversity of locoweeds in Asian Russia is confined to the mountainous territories (Malyshev 2006, Malyshev 2008. The genus is characterised by a wide distribution of interspecific hybridisation and rapid adaptive radiation of many young species (Kholina et al. 2016).
The insufficient data on the distribution of Oxytropis species in Asian Russia (especially for scattered and endemic species) stimulated a surge of research interest in this genus. As a result, different studies dealing with various aspects of the ecology, distribution and taxonomic status of selected sections and model species have been published recently (Gureyeva and Bytotova 2001, Konichenko and Selyutina 2013, Kholina et al. 2018.
These studies revealed an extensive array of new localities and expanded our knowledge about many rare and endemic Oxytropis species (Selyutina et al. 2010. Detailed studies of population biology features of selected endemic Oxytropis were also recently started by several authors (Selyuitina et al. 2018, Selyutina et al. 2019, Sandanov 2019a. Finally, adding molecular biology methods to researchers' toolkits made it possible to answer some other challenging questions in taxonomy, phylogenetics and phylogeography of the genus (Artyukova et al. 2004, Shahi Shavvon et al. 2017, Kholina et al. 2018, Kholina et al. 2021. Analysing species distribution patterns and bioclimatic modelling usually requires large datasets (Araújo et al. 2019). Application of such methods helps to understand the present patterns of plant species distribution and their potential response to climate change and human impact and aids developing the conservation measures for endangered plants (Sandanov 2019b, Sandanov 2020. Thus, the presented dataset contains key information crucial for conducting different kinds of research, taking Oxytropis as a model object. 281 occurrences in Asian Russia. We have not considered this source, while it was recently published in a separate dataset (Brianskaia et al. 2021a, Brianskaia et al. 2021b The digitalisation was performed in QGIS 3.10 and QGIS 3.16 software through the georeferencing tool. We georeferenced the source raster distribution maps by snapping control points to the destination vector shapefile. We used the Natural Earth vector map at 1:10 m scale as the base map for georeferencing. Control points linked raster maps to the destination shapefile, which resulted in a transformation of the maps according to the spatial projection of destination features (WGS 1984). Subsequently, we digitised species distribution locations from each map. Coordinates of each location were calculated in an attribute table.
The list of Oxytropis species with georeferenced distribution maps. Column "Oxytropis species" contains original species name given in presented source. (Pavlova 1989). Victor Chepinoga provided coordinates for localities of Oxytropis from "Flora of Central Siberia" (Chepinoga et al. 2017) (  available in other sources. Some additional information was derived from papers by Malyshev (Malyshev 2008) and by Pyak (Pyak 2014).
A considerable number of occurrences were extracted from authors' original field data (relevés, field diaries etc.). The main contributors were Denis Sandanov (1332 occurrences), Natalia Makunina (704 occurrences) and Inessa Selyutina (408 occurrences). The dataset, presented in this paper, is the only source for the occurrence of some rare and locally distributed Oxytropis species in GBIF. For a few such species, we provide exact coordinates obtained from the field observations. The highest percentage of coordinates with high precision (more than 50%) is presented for 59 species (Table 3)  The majority of the data do not overlap because they are presented for different regions of Asian Russia. Data overlapping is observed for some arctic species with the distribution covering the north-eastern part of Asian Russia. The distribution map for Oxytropis borealis revealed overlapping of several distribution records (Fig. 1). Distribution data from "Arctic flora of USSR" provide more details and have higher resolution in comparison with species occurrences from "Vascular Plants of Soviet Far East". Digitised distribution records from floras, in some cases, are complemented by field data which have better spatial resolution and can be used for large-scale mapping (Fig. 2). For example, the distribution of Oxytropis filiformis has five overlapping field records with data from "Flora of Central Siberia", but field data do not cover the whole distribution of studied species. Therefore, combining distribution data from different sources reveals more occurrences in various habitats which are helpful for understanding ecological features of species and future species distribution modelling.

Quality control:
Final examination of the digitised species distribution maps was performed in QGIS 3.10 and QGIS 3.16. For each species, we compared the output digitised occurrences with the original maps. Following Chapman and Wieczorek (Chapman and Wieczorek 2021), we applied three types of coordinate uncertainties. The first type includes coordinate uncertainty of species occurrence from the herbarium locality description. For such data, we established approximated 5 km as the coordinate uncertainty. The second type is the coordinate uncertainty of the drawn maps. Here, we accept a symbol diameter and map scale as a value of coordinate uncertainty at geocoding. Projections for the "Arctic Flora of USSR" (Yurtzev 1986) and "Vascular Plants of Soviet Far East" (Pavlova 1989) were recognised in QGIS. The original printed maps contained non-projective distortions; therefore, the initial rasters were transformed using the "polynomial 1" or "polynomial 2" transformation method and the "nearest neighbour" interpolation method. The largest possible source of error relates to the original mapping of points in the pre-digital era. To assess the impact of these uncertainties, we compared the position of points with known coordinates and points on maps, based on these samples. The spatial resolution of digitised maps was different: markers on the maps from the

Distribution of Oxytropis borealis
Note: Map is presented in Albers Equal Area Conic projection. Size of symbols is similar to symbols on distribution maps of digitised flora.
"Vascular Plants of Soviet Far East" have diameters from 33 to 45 km (mean = 39 km), "Flora of Siberia" -from 29 to 39 km (34 km), "Arctic Flora of USSR" -from 23 to 27 km (27 km). We used these mean parameters to estimate uncertainty for each digitised source. The third type is the coordinate uncertainty of the map digitalisation in QGIS. To test the coordinate uncertainty of such maps, three experts independently performed digitalisation on their computers for each type of map. As a result, the coordinate uncertainty was less than 5 km in all cases.
We calculated the final coordinate uncertainty by summarising all three types mentioned above and, for prominent flora, it is estimated as 49 km for the "Vascular Plants of Soviet Far East", 44 km for "Flora of Siberia" and 37 km for "Arctic Flora of USSR". Coordinate's uncertainty for the grid maps of "Flora of Central Siberia" was considered as 18 km (Chepinoga et al. 2017). Occurrence data presented in the paper by Malyshev (Malyshev 2008) has lower precision and recognised uncertainty for them is equal to 30 km. The uncertainty of georeferenced data for Red Data Books, based on large-scale maps, was estimated as 5 km. The same accuracy was used for occurrences of Oxytropis sobolevskajae (Pyak 2014).
Step description:

1.
Digitising species distribution maps of Oxytropis species from prominent flora and check-lists of Asian Russia 2.
Summarising field data with occurrences of locoweeds 3.
Merging all data to a single dataset.

Distribution of Oxytropis filiformis
Note: Map is presented in Albers Equal Area Conic projection. Size of symbols is similar to symbols on distribution maps of digitised flora.

Geographic coverage
Description: All Oxytropis species distribution records are located in Asian Russia. This is a vast area, stretching from the Ural Mountains in the west to the Pacific Ocean in the east; from the Arctic Ocean in the north to borders with Kazakhstan, Mongolia, China and northern Korea in the south. Analysis of diversity for all species presented in the study region has been done with adding the data from "Flora of Siberia" (Artemov and Egorova 2021). Very sparse distribution data were observed from the south-western part of Asian Russia, including Omsk Oblast, Novosibirsk Oblast, Tomsk Oblast, Tumen Oblast, Kurgan Oblast and Khanty-Mansi Autonomous Okrug (Fig. 3). A small part of occurrences was presented for the south part of Yamal-Nenets Autonomous Okrug, mid-latitudes of Krasnoyarsk Krai, the central and southern regions of Yakutia, Amur Oblast, Khabarovsk Krai and Primorsky Krai. Notably, the territory of the Sverdlovsk Oblast and Chelyabinsk Oblast (Central and Southern Ural Region) has not been included in the "Flora of Siberia" (Xue et al. 2020). That is the reason why the distribution data for the Ural Mts. are missing in this comprehensive compendium. Distribution maps for all analysed flora and check-lists are based on herbaria specimen. Thus, the published dataset on the distribution of Oxytropis species in Asian Russia reflects the absence of locoweeds and, in some cases, data from the region. The author's field observations provided additional data from southern Siberia and northeast Asia. The main lack of information falls in the mid-latitudes of Asian Russia; the pattern is specific for vascular plants of northern Asia in general (Sandanov 2020). Recent activities in the "Flora of Russia" project, implemented on the iNaturalist platform (Seregin et al. 2020), involved many fine-scale distribution data for plant species, which can improve our general understanding of plant distribution and Oxytropis species, in particular.
The distribution of locoweeds from different taxonomic sections showed a few interesting geographic patterns (Fig. 4). For example, section Xerobia is mainly distributed in central Asia, while sections Arctobia and Gloeocephala are most abundant in Asian Arctic. Some species from section Gloeocephala (e.g. O. adenophylla, O. jurtzevii) are distributed in the mountains of southern Siberia.
The distribution maps were transferred into gridded distributions with equal area projection (Albers cubic equal area projection) at a spatial resolution 100 × 100 km in QGIS to eliminate the potential influence of area on the estimation of species richness. The richness of Oxytropis species peaked in the mountainous regions of southern Siberia (Altai and Sayan Mts.) and north-eastern Asian Russia (Fig. 5). The topography of the latter region is complicated and includes a number of mountain ridges divided by plateaus and depressions. Higher diversity of Oxytropis in the territory of Baikal Siberia match with highabundant floristic regions presented by Malyshev (Malyshev 2000

Data resources
Data package title: Occurrences of Oxytropis species on the territory of Asian Russia.  Description: The dataset providing information on the geographic distribution of Oxytropis species on the territory of Asian Russia is discussed. Different sources (prominent flora and compendia, modern papers, Red Data Books and field data) have been used to describe diversity and distribution of the one of the richest genera in northern and central Asia. Presented species distribution data cover purely studied regions of Russia and reveal different geographic patterns on species and supraspecific levels of organisation. The presented dataset will be helpful in understanding ecological features and main determinants limiting distribution of Oxytropis pecies. kingdom The full scientific name of the kingdom in which the taxon is classified. phylum The full scientific name of the phylum or division in which the taxon is classified.

class
The full scientific name of the class in which the taxon is classified. order The full scientific name of the order in which the taxon is classified. family The full scientific name of the family in which the taxon is classified. taxonRank The taxonomic rank of the most specific name in the scientificName. decimalLatitude The geographic latitude (in decimal degrees, using the spatial reference system given in geodeticDatum) of the geographic centre of a Location. decimalLongitude The geographic longitude (in decimal degrees, using the spatial reference system given in geodeticDatum) of the geographic centre of a Location. geodeticDatum The ellipsoid, geodetic datum or spatial reference system (SRS) upon which the geographic coordinates given in decimalLatitude and decimalLongitude are based.
coordinateUncertaintyInMetres The horizontal distance (in metres) from the given decimalLatitude and decimalLongitude describing the smallest circle containing the whole of the