Fertilisation, cattle grazing and voles: collapse of meadow vole populations in young forests?
Bibliographic record
Abstract
Context A puzzling aspect of microtine population fluctuations is the damping out or collapsing of cycles over the last three decades, particularly in northern Europe. Occasional population fluctuations of Microtus may also have been damped out in temperate and boreal forests of the Pacific North-west of North America. One cause might be the presence of cattle (Bos taurus) grazing in forest habitats that offer summer forage. Aims We tested hypotheses (H) that abundance, population fluctuations, and demographic parameters of reproduction, recruitment and survival, of Microtus pennsylvanicus would be driven by understory plant productivity. Two predictions follow from this hypothesis: (H1) enhanced abundance and demography in fertilised stands, and (H2) reduced abundance and demography in stands with cattle grazing. Methods Study areas were located in ‘grazed’ and ‘ungrazed’ young forests in south-central British Columbia, Canada. Each study area had four replicate units of unfertilised and repeatedly fertilised stands. Herbaceous vegetation and meadow vole (M. pennsylvanicus) populations were sampled from 1993 to 2002. Key results Mean abundance of total herbs, grasses, and fireweed (Epilobium angustifolium) increased dramatically (8 to 34 times higher) with fertilisation in the ungrazed stands. Mean abundance of meadow voles was 3.1 to 8.5 times higher in the nutrient-enriched herbaceous vegetation in the ungrazed fertilised than grazed fertilised stands. Demographic variables also followed this pattern of abundance. Except for two years, mean abundance of meadow voles was similar between fertilised and unfertilised stands at the grazed area, with some degree of fluctuations generated in grazed stands. Thus, H1 and H2 were at least partly supported for M. pennsylvanicus in fertilised stands. Conclusions In high-quality habitats where cover and other attributes of vegetation are substantial enough to generate population increases and fluctuations of Microtus, grazing of vegetation by cattle or other livestock may indeed lead to potential collapse of fluctuations. Degree of grazing pressure would be crucial, but considering the widespread nature of grazing in the continuum of post-harvest forested sites in the Pacific North-west of North America, moderate to heavy grazing pressure is common. Implications Reductions in populations of microtines have serious consequences for predator communities and other ecological functions.
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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 | 0.001 |
| 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 | 0.001 |
| 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".