Filling Gaps in Earthworm Digital Diversity in Northern Eurasia from Russian-language Literature

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

Single-Walled Carbon Nanotube Network Field Effect Transistor as a Humidity Sensor

Single-walled carbon nanotube network field effect transistors were fabricated and studied as humidity sensors. Sensing responses were altered by changing the gate voltage. At the open channel state (negative gate voltage), humidity pulse resulted in the decrease of the source-drain current, and, vice versa, the increase in the source-drain current was observed at the positive gate voltage. This effect was explained by the electron-donating nature of water molecules. The operation speed and signal intensity was found to be dependent on the gate voltage polarity. The positive or negative change in current with humidity pulse at zero-gate voltage was found to depend on the previous state of the gate electrode (positive or negative voltage, respectively). Those characteristics were explained by the charge traps in the gate dielectric altering the effective gate voltage, which influenced the operation of field effect transistor.Peer reviewe

Morphological differences between genetic lineages of the peregrine earthworm Aporrectodea caliginosa (Savigny, 1826)

Aporrectodea caliginosa is a universally distributed and highly abundant peregrine earthworm that is the object of many ecological and ecotoxicological studies. Molecular phylogenetic analysis suggested that A. caliginosa consists of three highly diverged genetic lineages. In this study, we investigated morphological diversity within a sample of these three lineages from Belarus. We detected a variety of forms with different degrees of pigmentation and a shift in the clitellum position. The three genetic lineages of A. caliginosa demonstrated different propensity to particular morphological variants, including size, colour, and the clitellum position, yet no character could be used to distinguish among the lineages with sufficient accuracy. Thus, our results suggest that identification of the genetic lineage should be recommended for ecological studies involving A. caliginosa to account for possible differences between them

Highly passable propulsive device for UGVs on rugged terrain

One of the priority functional tasks of both industrial and mobile robotics is to perform operations for moving payloads in space. Typically, researchers pay attention to control the movement of the robot on different soils. It is necessary to underline the specificity of the movements of mobile robots, the main functional purpose of which is the movement of different objects. Unlike other robot applications there is the fact that transported cargo may have different mass-dimensional characteristics. The payload should be comparable to the mass of the mobile robot. This article addresses the issue of passability on rough terrain for a mobile robot performing the transport task and proposed a technical solution in the field of mechanics of propulsion to improve propelling of the traction wheel of the mobile robot with the ground

A Comparative Study of Machine Learning Methods for Predicting Live Weight of Duroc, Landrace, and Yorkshire Pigs

Simple Summary Live weight is an important indicator of livestock productivity and serves as an informative measure for the health, feeding, breeding, and selection of livestock. In this paper, the live weight of pig was estimated using six morphometric measurements, breed, weight at birth, weight at weaning, and age at weaning. In the present paper, we propose a comparative analysis of various machine learning methods using outlier detection, normalisation, hyperparameter optimisation, and stack generalisation to increase the accuracy of the predictions of the live weight of pigs. The StackingRegressor algorithm yielded a prediction quality of the live weight of Duroc, Landrace, and Yorkshire pigs that was higher than that of the state-of-the art algorithms. Live weight is an important indicator of livestock productivity and serves as an informative measure for the health, feeding, breeding, and selection of livestock. In this paper, the live weight of pig was estimated using six morphometric measurements, weight at birth, weight at weaning, and age at weaning. This study utilised a dataset including 340 pigs of the Duroc, Landrace, and Yorkshire breeds. In the present paper, we propose a comparative analysis of various machine learning methods using outlier detection, normalisation, hyperparameter optimisation, and stack generalisation to increase the accuracy of the predictions of the live weight of pigs. The performance of live weight prediction was assessed based on the evaluation criteria: the coefficient of determination, the root-mean-squared error, the mean absolute error, and the mean absolute percentage error. The performance measures in our experiments were also validated through 10-fold cross-validation to provide a robust model for predicting the pig live weight. The StackingRegressor model was found to provide the best results with an MAE of 4.331 and a MAPE of 4.296 on the test dataset

Highly passable propulsive device for UGVs on rugged terrain

One of the priority functional tasks of both industrial and mobile robotics is to perform operations for moving payloads in space. Typically, researchers pay attention to control the movement of the robot on different soils. It is necessary to underline the specificity of the movements of mobile robots, the main functional purpose of which is the movement of different objects. Unlike other robot applications there is the fact that transported cargo may have different mass-dimensional characteristics. The payload should be comparable to the mass of the mobile robot. This article addresses the issue of passability on rough terrain for a mobile robot performing the transport task and proposed a technical solution in the field of mechanics of propulsion to improve propelling of the traction wheel of the mobile robot with the ground