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Record W2899531456 · doi:10.1139/cjz-2018-0201

Dynamics of small-mammal communities along an elevational gradient

2018· article· en· W2899531456 on OpenAlexvenueno aff
Ana-Maria Benedek, Ioan Sîrbu

Bibliographic record

VenueCanadian Journal of Zoology · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyEcologyApodemusMammalBank voleHabitatRodentAbundance (ecology)Elevation (ballistics)VolePopulation

Abstract

fetched live from OpenAlex

Elevation is one of the most important natural gradients that is strongly shaping communities across relatively small areas. However, few studies have followed the temporal dynamics of elevational patterns, even in organisms for which population and community fluctuations have been extensively studied, such as rodents. Here we report the multiannual dynamics of small-mammal communities along an elevational gradient in the Southern Carpathians. During a 5-year survey, we conducted live-trapping in forested and shrubby habitats, at elevations between 820 and 2040 m. We used partial constrained multivariate analysis and mixed-effects models to test the effect of elevation, year, and their interaction. Community metrics differed significantly between even and odd years and temporal changes had stronger effect on small-mammal communities than elevation. The 2-year pattern of dynamics was especially marked in the yellow-necked field mouse (Apodemus flavicollis (Melchior, 1834)). Species abundance was predicted not only by year and elevation, but also by their interaction. The dominant rodent species, the bank vole (Myodes glareolus (Schreber, 1780)) and A. flavicollis, showed opposite annual patterns in relation to elevation, possibly as a strategy to avoid competition. Failure to consider the fluctuations in montane small-mammal communities may lead to wrong assessment of species’ state and distribution.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.856
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.234
Teacher spread0.210 · 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 teacher head, not a consensus.

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".

Quick stats

Citations10
Published2018
Admission routes1
Has abstractyes

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