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Record W4403193947 · doi:10.17223/19988591/66/5

Distribution, habitats and abundance of the Eurasian Least Shrew Sorex minutissimus (Eulipotyphla, Soricidae) in the North-East of the European part of Russia

2024· article· en· W4403193947 on OpenAlexaboutno aff
Anatoly Bobretsov, Anatoly N. Petrov, Н. М. Быховец

Bibliographic record

VenueVestnik Tomskogo gosudarstvennogo universiteta Biologiya · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsShrewSorexGeographyDistribution (mathematics)Abundance (ecology)HabitatInsectivoraZoologyEcologyBiology

Abstract

fetched live from OpenAlex

Eurasian least Shrew, Sorex minutissimus, occupies large area in Northern Eurasia from Norway, Sweden, and Belarus to the Pacific Ocean shore, and Sakhalin and Hokkaido islands. It is marked in Mongolia and China, in Northern America (Alaska) and in western part of Canada. Sorex minutissimus can be met in different zones from forest tundra in the North through boreal spruce forests to mixed forests and forest steppe in the South. The Eurasian least Shrew is rare species all over this area. In addition, it is very difficult to catch shrews with usual catching methods. Thus, the distribution and ecology of this species have not been studied enough until now. The northern boundary of this area, within North-East of European part of Russia, is almost unknown. Until recently, only five records of Sorex minutissimus were registered on the region territory. This work aims to analyze all materials of the last decades on Eurasian least Shrew distribution in the North-East of European Russia and summarize the data on biotopic distribution of this species and its number. To catch and count shrews, we used standard 50m length trench with five catching cones embedded in the bottom. We considered the number of caught animals per 100 cone-days as count index (an. per 100 c-d.). Mean indexes of species abundance were indicated based on long term counts in eight locations. Three of them (Yany-Pupunyor, Garevka, and Kybla-Kyrta) are located in the Ural Mountains, one in extremely northern taiga subzone (Karpushevka), one in plain northern taiga subzone (Kamenny), and the last two in medium taiga subzone (Dan’ and Yaksha). Over the last 30 years, we could discover 14 new habitats of Eurasian least Shrew. It undoubtedly inhabits wider area, but catching trenches, which can catch this species, were used in very little number of habitats. For example, this only can explain the absence of the Eurasian least Shrew species in the Subpolar Urals. All records of Eurasian least Shrew are located in taiga zone of the region. This species was not found in European plain tundra and forest-tundra. The northern boundary of this area from Pinega State Reserve to the East crosses of the Mezen’ River in its lower course. On the territory of the Republic of Komi, the most northern habitats where the species was found are the middle stream of the Tobysh River (66º00′15′′N, 51º08′29′′E) and the outskirts of Karpushevka village (See Fig. 1, See Table 1) in extremely northern (thin) taiga subzone. From here, the northern distribution boundary of the Eurasian least Shrew in the region goes to the central Polar Urals, where it was caught on the eastern slopes of Rai-Iz Mountain. In the Northern Urals on the territory of Pechora-Ilych State Reserve, it was found both in the South (Yany-Pupunyor Mountain Ridge) and in the North (Saran-Iz Mountain Ridge) of the mountain area. The Eurasian least Shrew is considered as the one of most eurytopic species of shrews. In taiga zone of the North-East of European part of Russia it inhabits different biotopes: spruce and pine forests, cleared spaces, meadows, upland bogs, and ecotones (boundaries of biotopes). The use of index of fidelity to biotope made it possible to find preference of Eurasian least Shrew to communities of taiga type (See Fig. 2). This connection occurred in all studied habitats. Shrubby green-moss spruce forests are the optimal stations. In these biotopes, were caught 90.9% of all animals in Kamenny locality and 85% in Yaksha, respectively. In Dan’ habitat, more than 50% of all shrews were caught in two biotopes – sphagnum-moss spruce forest and shrubby green-moss spruce forest. Similar biotopic preferences were registered in other areas on European North as well [12]. In some localities, cleared places of different type and green-moss pine forests were also among the preferred biotopes. A specific feature of Eurasian least Shrew is omnipresent low abundance. This is also typical for territory of the North-East of European part of Russia, where mean abundance indexes in different locations vary from 0.2 to 1.2 an. per 100 c-d. (See Table 2). The Sorex minutissimus occupies one of the last positions in the population of shrews with its share ranging from 0.1 to 1.8% in different regions. Some possible causes of habitat versatility and low abundance of least shrew may be due to energetic features of this species. Very small body size requires significant energy loss for survival of this species. Therefore S. minutissimis is exigent to local environmental factors (humidity, micro-climate, and food resources). Micro-stations, which occupy small territory in different habitats, are the optimal environment. Small (< 4-5 mm), abundant and accessible arthropods with high occurrence are prevalent in feeding of least shrew; thus, this species (if relevant environmental factors are present) occupies many biotopes, including poor habitats. However, the abundance of least shrew is restricted by fragmentarity of its population and low ecological capacity of micro-stations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.195
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2024
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