Nutrient deposition on Arctic fox dens creates atypical tundra plant assemblages at the edge of the Arctic
Bibliographic record
Abstract
Abstract Questions In most ecosystems, some organisms can be considered ecosystem engineers because they modify their physical environment in a way that can affect many other organisms. Nutrient deposition may be extremely important as an ecosystem engineering activity in nutrient‐limited environments, but this mechanism remains understudied. In low‐Arctic tundra, a region characterized by continuous permafrost, low‐nutrient soils, and slow nutrient turnover, Arctic foxes ( Vulpes lagopus ) concentrate nutrients on their dens through faecal deposition and feeding their young. This nutrient concentration enhances productivity in patches on the landscape, likely creating a unique habitat for a variety of plants, and could have cascading effects on the distribution and diversity of vegetation on the tundra. Location Low‐Arctic tundra in Wapusk National Park, Manitoba, Canada. Methods We quantified differences in vegetation composition between 20 fox dens and adjacent control sites. Results Plant growth form differed greatly between dens, which were dominated by deciduous grasses near the coast and erect shrubs farther from the coast, and control sites, which were dominated by evergreen prostrate shrubs. Dens also had more forb cover and less cover of lichens, mosses, and sedges. Species composition also varied greatly between control and den areas, with 17 of the 20 species found in at least 10% of the sampled sites being indicator species for dens or control sites. Conclusions By providing habitat for plants reliant on higher nutrient availability not typical of tundra heath, Arctic foxes enhance the biodiversity of the region. These erect plants may also help create new habitat by retaining snow on normally windswept beach ridges. Overall, this study illustrates the broader impacts of predators on diversity and community composition through mechanisms other than predation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".