Predators attract prey through ecosystem engineering in the Arctic
Bibliographic record
Abstract
Abstract Predators can affect prey through mechanisms other than predation; for example, redistributing resources could modify habitats favorably for other organisms. We examined Arctic fox ( Vulpes lagopus ) den use by lemmings, their primary prey, in winter during a year of low lemming densities. We found winter nests, which are built by lemmings under snow, on 69% of fox dens, whereas no control sites had nests. In August, dens had twice the vegetation cover and 50% greater nitrogen content in grass than controls, suggesting soil enrichment by foxes increased food quantity and quality for herbivores. Snow was ~4 times thicker in April on dens than controls, and 1.4 times thicker on dens with lemming nests than dens without, suggesting lemmings choose thicker snow for thermal insulation. Snow cover thickness was positively related to vegetation cover on dens, but not on control sites. Thus, Arctic foxes not only prey on lemmings but also engineer productive habitat that attracts lemmings. During winters with low lemming densities, when foxes often leave the denning area and predation risk is lower, fox dens may provide a refuge that could buffer the effects of deteriorating snow conditions with Arctic warming. This additional mechanism of predators interacting with their prey illustrates how ecosystem engineers potentially alter food web interactions and highlights the importance of integrating these bodies of theory in attempts to understand community dynamics.
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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 | 0.000 | 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) | 0.005 | 0.003 |
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; both teacher heads agree on what is shown here.
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".