Hidden engineers of the earth: Investigating the geomorphic impacts of small fossorial rodents in the Vaud Pre-Alps, Switzerland
Bibliographic record
Abstract
• Montane water voles significantly impact high-altitude grasslands. • Large-scale, multiannual population cycles shape landscapes over time. • Vole geomorphic activity drives sediment fluxes of 100 t ha −1 yr −1. • Integrating geophysical and ecological processes is crucial for understanding ecosystems. The role of animals in geomorphology remains largely understudied, even though animal energy remains an important factor in exogenic geomorphology. To better understand the coupling of geophysical and ecological processes, annual montane water vole ( Arvicola scherman ) burrowing impacts were monitored in a high-altitude grassland ecosystem in the Swiss Pre-Alps. Considered an engineer species, the montane water vole generated significant geomorphic impacts for 2021–2022. With an average density of 14.4 burrows ha −1 , the sediments excavated in the form of earth tumuli erode easily under the effect of gravity, with a slope of over 20°. The sediment volumes calculated correspond to an average excavation rate of 100 t/ha yr −1 . According to the literature, apart from marmots, pocket gophers and other vole species, few burrowing mammals have such a high sediment potential displacement. The data collected show the importance of monitoring water vole populations, given the multiannual large-amplitude fluctuations in populations, which appear at least, partially related to agricultural land use and landscape patterns. Finally, the study of cascading environmental effects linked to animal activity can provide information on the geo-ecosystem from a functional point of view, particularly in the light of ongoing climate change, biodiversity loss and anthropogenic transformations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".