Ecosystem engineering of tundra heath by Arctic fox (Vulpes lagopus) is driven by nutrient additions
Bibliographic record
Abstract
Abstract Background Soil nutrient availability is a limiting factor for tundra productivity. Therefore, consumer-driven alteration of nutrient availability can have a large effect on tundra communities. Previous work has demonstrated that Arctic foxes (Vulpes lagopus) act as ecosystem engineers in tundra heath communities by altering plant composition and increasing plant biomass on their dens, which then increases snow depth. To test the ability of increased nutrients and deeper snow to cause the ecosystem effects observed on fox dens, we set up a nutrient addition and snow fencing experiment on tundra heath in Wapusk National Park, Canada. Results Changes in experimental plots were mainly driven by fertilizer application, not snow depth. After 2 years, the fertilizer plots were invaded by a dune grass (Elymus mollis), which increased to 12% cover by the end of the experiment, which is typical of fox dens. After 4 years, total plant cover was 26% higher on the fertilizer plots than on the control plots. After 7 years of treatments, the plots receiving both fertilizer and snow fencing had the greatest shift in plant species composition, although they still lacked the tall willow shrubs typical of fox dens. Fertilized plots and dens had five times more arthropods than control plots. Most wildlife, except caribou (Rangifer tarandus), spent more time on fertilized plots in years when they were abundant, with Canada geese (Branta canadensis) being present 20 times longer in fertilizer plots. Collared lemmings (Dicrostonyx richardsoni) also preferred fertilized plots in the summer, but winter use was more pronounced on snow fenced and fertilized plots, where they produced 20 latrines per plot in a peak population year. Conclusions These results demonstrate that the nutrient limitation in tundra vegetation makes tundra ecosystems vulnerable to changes in nutrient availability, with changes in plant abundance and composition leading to increased animal activity, that has the potential to create a positive feedback in ecosystem productivity.
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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 | 0.000 | 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".