Impact of climate change on the small mammal community of the Yukon boreal forest
Bibliographic record
Abstract
Long-term monitoring is critical to determine the stability and sustainability of wildlife populations, and if change has occurred, why. We have followed population density changes in the small mammal community in the boreal forest of the southern Yukon for 46 years with density estimates by live trapping on 3-5 unmanipulated grids in spring and autumn. This community consists of 10 species and was responsible for 9% of the energy flow in the herbivore component of this ecosystem from 1986 to 1996, but this increased to 38% from 2003 to 2014. Small mammals, although small in size, are large in the transfer of energy from plants to predators and decomposers. Four species form the bulk of the biomass. There was a shift in the dominant species from the 1970s to the 2000s, with Myodes rutilus increasing in relative abundance by 22% and Peromyscus maniculatus decreasing by 22%. From 2007 to 2018, Myodes comprised 63% of the catch, Peromyscus 20%, and Microtus species 17%. Possible causes of these changes involve climate change, which is increasing primary production in this boreal forest, and an associated increase in the abundance of 3 rodent predators, marten (Martes americana), ermine (Mustela ermine) and coyotes (Canis latrans). Following and understanding these and potential future changes will require long-term monitoring studies on a large scale to measure metapopulation dynamics. The small mammal community in northern Canada is being affected by climate change and cannot remain stable. Changes will be critically dependent on food-web interactions that are species-specific.
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 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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".