Population fluctuations of long-tailed voles (<i>Microtus longicaudus</i>) in managed forests: site-specific disturbances or a long-term pattern?
Bibliographic record
Abstract
Abstract We investigated population responses of Microtus longicaudus to cumulative clear-cutting of coniferous forests and to enhanced understory vegetation in young, fertilized pine stands near Summerland, British Columbia, Canada. We explored if there was a threshold level of habitat quality arising from a given forest disturbance for M. longicaudus to increase to high population levels and potentially fluctuate in abundance over time. Secondly, we asked if these outbreaks were site-specific or part of a long-term pattern. We tested three hypotheses (H) that populations of M. longicaudus would increase in abundance and potentially fluctuate owing to (H1) the availability of early seral postharvest habitats associated with cumulative clear-cut harvesting; (H2) woody debris piles on clear-cuts; and (H3) have higher mean abundance, reproduction, and survival in fertilized forest sites with enhanced understory vegetation. Mean annual and peak abundances of M. longicaudus were significantly different across the four Periods of cumulative forest harvesting with numbers being highest in the first two Periods. Thus, H1, that long-tailed voles would increase in abundance on new clear-cuts, was partially supported for the first two Periods but not in the later Periods. Constant cattle (Bos taurus) grazing during summer periods over the four decades may have reduced vegetative productivity for voles and damped out population responses on these clear-cut sites, at least in the last two Periods. Woody debris piles on clear-cuts may have increased abundance and generated a population fluctuation, thereby supporting H2. Enhanced abundance of understory vegetation had no effect on mean abundance, reproduction, or survival of M. longicaudus, and hence did not support H3. We conclude that it is site-specific disturbances, particularly in forest management, that generate occasional outbreaks of M. longicaudus, and there does not appear to be any long-term pattern to these discordant fluctuations.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".