Evidence of migratory coupling between grey wolves and migratory caribou
Bibliographic record
Abstract
Large‐scale animal migrations influence population and community dynamics along with ecosystem functioning. The migratory coupling concept posits that movement of migrant prey can lead to large‐scale movements of predators. In northern ecosystems, spatial patterns and behavioral responses of grey wolf to spatio‐temporal changes in its primary prey distribution, the migratory caribou, remain poorly documented. We used a long‐term GPS dataset (2011–2021) of 59 wolves and 431 migratory caribou from the declining Rivière‐aux‐Feuilles herd (QC, Canada) to investigate movement patterns and space use of wolves related to caribou seasonal distribution. Wolves home ranges overlapped with areas used by caribou year‐round, especially in May and winter. Wolves exhibited three annual tactics: sedentary (17%), long‐distance migration (> 700 km) between wintering areas and the tundra (36%), and a medium‐distance migration, stopping their northward movement near the treeline (47%). Migratory wolves started spring migration northward earlier than caribou, intercepting their prey on their way to calving grounds, but departed southward for fall migration later than caribou, tracking them on their way back to wintering areas. Wolves near or overlapping areas used by caribou exhibited lower monthly movement rates compared to wolves located further away. Overlap of home range among wolves was higher during migrations and winter but decreased in summer when wolves rear pups and caribou are dispersed on summer grounds. We provide evidence of migratory coupling between grey wolves and migratory caribou, with most wolves adjusting their space use patterns to match their primary prey distribution. Although predation pressure may affect the dynamics of declining caribou herds, the global decline of that prey may in turn impact predators on the long‐term, potentially enhancing intraspecific competition for new resources. Highlighting this migratory coupling is a key step to develop appropriate conservation and management measures for both guilds in the context of large‐scale migratory prey decline.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".