Modelling the spatial population dynamics of arctic foxes: the effects of red foxes and microtine cycles
Bibliographic record
Abstract
The Fennoscandian arctic fox ( Vulpes lagopus (L., 1758)) population is critically endangered, possibly because of increased interference competition from red foxes ( Vulpes vulpes (L., 1758)) and fading cycles in microtine rodents, which cause food shortage. It is not known how these factors drive arctic fox population trends. To test their role in arctic fox decline, we developed a spatially explicit and individual-based model that allowed us to simulate fox interactions and food availability in a real landscape. A sensitivity analysis revealed that simulated arctic fox population size and den occupancy were strongly correlated with fecundity and mortality during the microtine crash phase, but also with red fox status. Model simulations suggested that arctic fox population trends depended on microtine cycles and that arctic fox distributions were restricted by red fox presence. We compared the model predictions with field data collected at Vindelfjällen, Sweden. The model recreated the observed arctic fox trend only with the inclusion of arctic fox avoidance of red fox home ranges. The results indicate that avoidance behaviours can affect population trends and hence that relatively small numbers of red foxes can have a strong negative impact on arctic fox population size and distribution.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".