Foxes engineer hotspots of wildlife activity on the nutrient-limited Arctic tundra
Bibliographic record
Abstract
Abstract Predators largely affect ecosystems through trophic interactions, but they also can have indirect effects by altering nutrient dynamics and acting as ecosystem engineers. Arctic foxes ( Vulpes lagopus ) are ecosystem engineers that concentrate nutrients on their dens, creating biogeochemical hotspots with lush vegetation on the nutrient-limited tundra. Red foxes ( V. vulpes ) similarly engineer subarctic environments through their denning behavior, and have recently expanded onto the tundra where they now often occupy historical Arctic fox dens. We evaluated the impact of fox denning activity on the spatial behavior of other tundra wildlife by comparing predator and herbivore visits to 12 natal dens and adjacent control sites over two years using camera traps in northeastern Manitoba, where both fox species are sympatric. Both the capture rates and species richness of wildlife were significantly greater at fox dens relative to control sites. Predators were detected almost exclusively on dens occupied by foxes, where they were observed investigating and scavenging prey remains (carrion, feathers), suggesting carcass presence or fox presence attracts predators to den sites. Caribou ( Rangifer tarandus ) also visited dens more often than control sites, likely attracted by the enhanced vegetation typically found on dens. Our results suggest fox ecosystem engineering affects the spatial distribution of herbivores by enriching vegetation at dens, and other predators by providing carrion. Understanding how predators affect other organisms via non-trophic interactions provides an enriched view of their functional roles within ecosystems.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".