Maxim Shashkov

В работе представлены результаты сравнения глобальной пространственной модели разнообразия сообществ дождевых червей, опубликованной H.R.P. Phillipsetal.в2019 г.,с доступными для Европейской России полевыми данными. Материалы полевых исследований (271 сбор) взяты из ранее обобщенного нами массива литературных данных. На их основе показано, что всего в регионе учтено 25 видов дождевых червей, в сборах отмечено от 1 до 9 видов. Среди биотопов разных типов наибольшее число видов зафиксировано в широколиственных лесах (в среднем 5 ± 2 видов) и на урбанизированных территориях (5 ± 2 видов), наименьшее – в лиственничных лесах (2 ± 0 вида). Максимальная численность дождевых червей отмечена в широколиственных лесах (217 ±168 экз./м2), минимальная – в лиственничных лесах (5 ± 0 экз./м2). Полученные показатели значительно превышают модельные оценки. Согласно последним, среднее значение видового богатства в точках проведения полевых исследований составляет 1 ± 1 вид, численности – 2 ± 1 экз./м2. Расхождения вызваны недостаточным объемом исходных данных для моделирования, что, в свою очередь, обусловлено строгими критериями их отбора. The predictions of the global earthworm community diversity spatial model, published by H.R.P. Phillips et al. (2019), and field data for earthworm surveys available for the European part of Russia were compared. We analyzed early literature dataset comprised 271 surveys. According to the literature data, 25 earthworm species are reported in the European part of Russia, total of 1 to 9 species were recorded per survey. The greatest species richness was recorded in the broad-leaved forests (on average, 5 ± 2 species) and in the urbanized areas (5 ± 2 species), the lowest (2 ± 0 species) was in larch forests. The highest earthworm population density was observed in broad-leaved forests (217 ± 168 ind./m2 ), the lowest (5 ± 0 ind./m2 ), in larch forests. The field assesments considerably exceeded the modelling results. According to the model, species richness calculated for the sampling sites was 1 ± 1 species on average, population density, 2 ± 1 ind./m2 . These discrepancies were due to an insufficient volume of input data for modelling; the latter, in turn, was due to strict criteria for data selection.

The alpha, beta, gamma, and functional diversity of forest communities of Central Kazakhstan, which are habitats of wild fruit plants, were analysed. Forest communities were studied in seven sites represented by mountain forests, island forests, and steppe kolki. A total of 41 relevés were collected and analysed. We counted 195 species of vascular plants, including 10 species of fruit plants: Crataegus sanguinea Pall., Lonicera tatarica L., Ribes aciculare Sm., Ribes nigrum L., Ribes saxatile Pall., Rosa acicularis Lindl., Rosa laxa Retz., Rosa majalis Herrm., and Rosa spinosissima L. Results of non-metric multidimensional scaling revealed no dividing of relevés between the studied sites. Cluster analysis using Ward's method identified 4 groups of relevés. No statistically significant differences in alpha diversity scores were found between the groups. Beta-diversity assessment based on Jaccard distance showed that the groups differed well in species composition. The results of functional diversity analysis based on ecological-coenotic groups and calculation of indicator species values showed differences in the structure of the four community groups. It was shown that Rosa majalis is a significant indicator species for the mountain-forest massif of Karkaraly, and Ribes nigrum – for steppe kolki with predominance of forest species. Other fruit species were found in all analysed groups of communities and did not show specific coenotic predilection.

Earthworms are one of the most important components of temperate ecosystems and groups of soil animals globally, but data on their distribution around the world are still incomplete and uneven. Northern Eurasia is a region for which available data on earthworm distribution is extremely poor. At the same time, generations of Soviet and Russian researchers have performed extensive research and accumulated a large amount of data on the distribution of earthworms in this vast region. Most of this information was published in Russian, not digitised and, therefore, hardly accessible to researchers. Our dataset fills this gap and provides occurrence data extracted from 159 literature sources in Russian published between 1868 and 2022. The dataset contains 5,304 occurrences of 109 species and 14 subspecies. The collected data cover the territory of 27 countries. The dataset extends the data available through GBIF.org on earthworm distribution in Russia, Belarus, and Ukraine. For Transcaucasia and Central Asia countries, our resource has become the main GBIF source on earthworm diversity distribution. On a global scale, our dataset contributes to filling gaps in biodiversity, which facilitates more accurate estimates of the response of biodiversity to global climate change as well as the spreading of invasive species.

Data availability for certain groups of organisms (ecosystem engineers, invasive or protected species, etc.) is important for monitoring and making predictions in changing environments. One of the most promising directions for research on the impact of changes is species distribution modelling. Such technologies are highly dependent on occurrence data of high quality (Van Eupen et al. 2021). Earthworms (order Crassiclitellata) are a key group of organisms (Lavelle 2014), but their distribution around the globe is underrepresented in digital resources. Dozens of earthworm species, both widespread and endemic, inhabit the territory of Northern Eurasia (Perel 1979), but extremely poor data on them is available through global biodiversity repositories (Cameron 2018). There are two main obstacles to data mobilisation. Firstly, studies of the diversity of earthworms in Northen Eurasia have a long history (since the end of the nineteenth century) and were conducted by several generations of Soviet and Russian researchers. Most of the collected data have been published in "grey literature", now stored only in a few libraries. Until recently, most of these remained largely undigitised, and some are probably irretrievably lost. The second problem is the difference in the taxonomic checklists used by Soviet and European researchers. Not all species and synonyms are included in the GBIF (Global Biodiversity Information Facility) Backbone Taxonomy. As a result, existing earthworm species distribution models (Phillips 2019) potentially miss a significant amount of data and may underestimate biodiversity, and predict distributions inaccurately. To fill this gap, we collected occurrence data from the Russian language literature (published by Soviet and Russian researchers) and digitised species checklists, keeping the original scientific names. To find relevant literature, we conducted a keyword search for "earthworms" and "Lumbricidae" through the Russian national scientific online library eLibrary and screened reference lists from the monographs of leading Soviet and Russian soil zoologist Tamara Perel (Vsevolodova-Perel 1997, Perel 1979). As a result, about 1,000 references were collected, of which 330 papers had titles indicating the potential to contain data on earthworm occurrences. Among these, 219 were found as PDF files or printed papers. For dataset compilation, 159 papers were used; the others had no exact location data or duplicated data contained in other papers. Most of the sources were peer-reviewed articles (Table 1). A reference list is available through Zenodo (Ivanova et al. 2023). The earliest publication we could find dates back to 1899, by Wilhelm Michaelsen. The most recent publication is 2023. About a third of the sources were written by systematists Iosif Malevich and Tamara Perel. Occurrence data were extracted and structured according to the Darwin Core standard (Wieczorek et al. 2012). During the data digitisation process, we tried to include as much primary information as possible. Only one tenth of the literature occurrences contained the geographic coordinates of locations provided by the authors. The remaining occurrences were manually georeferenced using the point-radius method (Wieczorek et al. 2010). The resulting occurrence dataset Earthworm occurrences from Russian-language literature (Shashkov et al. 2023) was published through the Global Biodiversity Information Facility portal. It contains 5304 occurrences of 117 species from 27 countries (Fig. 1). To improve the GBIF Backbone Taxonomy, we digitised two catalogues of earthworm species published for the USSR (Perel 1979) and Russian Federation (Vsevolodova-Perel 1997) by Tamara Perel. Based on these monographs, three checklist datasets were published through GBIF (Shashkov 2023b, 124 records; Shashkov 2023c, 87 records; Shashkov 2023a, 95 records). Now we work towards including these names in the GBIF Backbone so that all species names can be matched and recorded exactly as mentioned in papers published by Soviet and Russian researchers.

Работа выполнена в малонарушенном ельнике липняковом заповедника «Кологривский лес» (Костромская обл.) на основе данных аэрофотосъемки квадрокоптером. Алгоритмы автоматического детектирования позволили выделить большинство деревьев в пологе леса. Детектирование деревьев по ортофотоплану с использованием нейросетевого алгоритма Deep Forest оказалось более качественным, чем детектирование по модели высот при помощи алгоритма, основанного на методе скользящего окна. Как правило, оба метода демонстрировали более качественные результаты для хвойных деревьев по сравнению с лиственными. Сравнение средних высот деревьев, оцененных по дистанционным данным и измеренных наземными методами, не выявило значимых различий. Показана необходимость дополнительных наземных обследований для оценки качества детектирования подроста. The work was performed in the old-growth linden-spruce forest of the Kologrivsky Forest Nature Reserve (Kostroma Oblast, Russia) based on aerial photography with a quadcopter. Automatic detection algorithms made it possible to detect most of the trees in the forest canopy. Tree detection by orthomosaic using neural network algorithm ‘DeepForest’ turned out to be of better quality than detection based on the canopy height model using an algorithm based on the sliding window method. As a rule, both methods showed better results for conifers compared to deciduous trees. Comparison of the average heights of trees estimated from remote data and measured by ground survey did not reveal significant differences. Additional ground surveys to assess the quality of undergrowth detection are needed.

Ground beetles (Carabidae, Coleoptera) are one of the most species-rich and well-studied insect families. However, the number of published datasets is disproportionately low against the biodiversity of this group. According to GBIF, only a fifth of the percentage of all published data covers ground beetles. This article describes a sampling-event dataset providing primary data on ground beetles collected in urban and suburban habitats in Kaluga, a typical central Russian city. We surveyed habitats of different land-use types and the extent and intensity of anthropogenic influence: yards, gardens, quarries, small urban woodlands, grasslands and riparian habitats. Carabids were collected by pitfall traps during most of the vegetative season (mostly from late April - early May to at least early October) for 13 seasons between 1994 and 2015. In total, the dataset contains 189 carabid species and 79,091 specimens. The dataset provides information about species composition and abundance, habitat distribution, seasonal and long-term dynamics of carabid beetles in environments of different degrees of urbanisation. This dataset is the first sampling-event dataset about carabids in various urban habitats published through GBIF.

The ground-based surveys of areas affected by storms might be difficult or even impossible because of the limited ability to move within the damaged area. Therefore, this work was aimed to estimate storm damage based on aerial photography and open biodiversity data available via the Internet. The study was carried out in the old-growth hemiboreal forests of the Kologrivsky Forest State Nature Reserve (Kostroma Region, Russia), which was affected by a catastrophic windthrow caused by a storm on 15.05.2021. The sampling area was 100 000 m2. We used our previous ground-survey studies and open-access biodiversity data available through the Global Biodiversity Information Facility for describing the forest stands composition before the catastrophic event. The aerial photography data were used for estimating tree stands damages after the windthrow. For remote data collecting, we used an unmanned aerial vehicle – quadrocopter DJI Phantom 4. Agisoft Metashape software was used for aerial photographs processing. The obtained photogrammetric digital elevation model (DEM) and orthophoto-mosaic were processed with QGIS software. Damaged areas were detected automatically based on the DEM. Individual fallen trees were visually detected using the orthophoto-mosaic. We found before the windthrow the study area was covered by old-growth stands developed naturally over a long time. The stand structure was multi-layered and uneven-aged. The ontogenetic spectra of late-successional tree species Picea abies (hereinafter – spruce) and Tilia cordata (hereinafter – linden) were normal. The old-growth stands were heterogeneous before the windthrow: the canopy closed multi-layered and uneven-aged stands, decaying spruce stands and areas where spruce completely fell out and the tree stand was absent. In addition, old-growth linden stands were present. According to the obtained results, the stand structure was critically changed caused by the windthrow. The DEM-processing results showed the windthrow strongly damaged 33.1% stands in the study area. Using the orthophoto-mosaic, we visually detected 759 fallen trees. Among them, 82.9% were associated with strongly-damaged areas. According to the DEM classification, the rest of the visually detected fallen trees were in non-damaged areas and canopy gaps established before the windthrow. The analysis showed that these were less damaged areas with survived stands or groups of trees after the storm. Thus, our results showed that it is necessary to use both the DEM and the orthophoto-mosaic for more accurate estimates. Our exploratory analysis of different tree stand damages found that apparently, spruce stands were more affected by the storm than linden stands. It is explained by the different wind resistance of spruce and linden and differences in regrowth density and species composition in these stands.

The article provides primary data on repeated tree measurements collected during two censuses on a permanent sampling plot (440 m × 200 m) established in the old-growth polydominant broad-leaved forest in the Kaluzhskie Zaseki State Nature Reserve (centre of European Russia). The time span between the inventories was 30 years, and a total of 11 578 individuals of ten tree, one shrub species, and several undefined tree species of three known genera were registered. During the surveys, tree identity, stem diameter at breast height (DBH) of 1.3 m, and life status (alive or dead) were recorded for every tree individual with DBH ≥ 5 cm. Additional attributes were determined for some individuals. Field data were digitised and compiled into the PostgreSQL database. An accurate data quality assessment, validation, and cleaning (with documentation of changes) have been performed before data standardisation according to the Darwin Core standard. Standardised data were published through the GBIF repository. From 1986 to 1988, 9811 individuals were recorded within the initial census, including 3920 Corylus avellana individual shrubs. Corylus avellana shrubs were recorded without measuring DBH. From 2016 to 2018, 7658 stems were recorded in the recensus, including 3090 living trees marked during the initial census, and 1641 other living trees reaching the DBH of at least 5 cm. Corylus avellana was not included in the recensus. Thus, over 30 years, about 65% of living tree individuals have survived, but the total number of living trees has not changed considerably. The mean diameter of shade-intolerant tree species (Quercus robur, Fraxinus excelsior, Populus tremula, and Betula spp.) has increased the most remarkably during 30 years. For these species, the increase in average diameter, along with the decrease in numbers, is associated with the death of young trees, presumably due to low illumination under the canopy. Contrastingly, shade-tolerant tree species (Ulmus glabra, Tilia cordata, Acer platanoides) increased in number, while their mean diameter increased slightly or even decreased, that evidences the successful regeneration of these species under the canopy. These data are relevant for investigating forest ecology questions at spatiotemporal scales as a model of natural succession.

This sampling-event dataset provides primary data about species diversity, age structure, abundance (in terms of biomass and density) and seasonal activity of earthworms (Lumbricidae). The study was carried out in old-growth broad-leaved and young forests of two protected areas ("Kaluzhskiye Zaseki" Nature Reserve and Ugra National Park) of Kaluga Oblast (Russia).The published dataset provides new data about earthworm communities in European Russia. We propose a new schema according to Darwin Core for the standardisation of the soil invertebrates survey data.

Earthworms are an important soil taxon as ecosystem engineers, providing a variety of crucial ecosystem functions and services. Little is known about their diversity and distribution at large spatial scales, despite the availability of considerable amounts of local-scale data. Earthworm diversity data, obtained from the primary literature or provided directly by authors, were collated with information on site locations, including coordinates, habitat cover, and soil properties. Datasets were required, at a minimum, to include abundance or biomass of earthworms at a site. Where possible, site-level species lists were included, as well as the abundance and biomass of individual species and ecological groups. This global dataset contains 10,840 sites, with 184 species, from 60 countries and all continents except Antarctica. The data were obtained from 182 published articles, published between 1973 and 2017, and 17 unpublished datasets. Amalgamating data into a single global database will assist researchers in investigating and answering a wide variety of pressing questions, for example, jointly assessing aboveground and belowground biodiversity distributions and drivers of biodiversity change.