Unveiling trade‐offs in resource selection of migratory caribou using a mechanistic movement model of availability
Bibliographic record
Abstract
Habitat selection is a multi‐level, hierarchical process that should be a key component in the balance between food acquisition and predation risk avoidance (food–predation trade‐off). However, to date, studies have not fully elucidated how fine‐ and broad‐scale habitat decisions by individual prey can help balance food versus risk. We studied broad‐scale habitat selection by Newfoundland caribou Rangifer tarandus , focusing on trade‐offs between predation risk versus access to forage during the calving and post‐calving period. We improved traditional measures of habitat availability by incorporating fine‐scale movement patterns of caribou into the availability kernel, thus enabling separation of broad and fine scales of selection. Remote sensing and field surveys served to create a spatio‐temporal model of forage availability, whereas GPS telemetry locations from 66 black bears Ursus americanus and 59 coyotes Canis latrans provided models of predation risk. We then used GPS telemetry locations from 114 female caribou to assess food–predation trade‐offs through the prism of our refined model of caribou habitat availability. We noted that migratory movements of caribou were oriented mainly towards habitats with abundant forage and lower risk of bear and (to a lesser extent) coyote encounter. These findings were generally consistent across caribou herds and would not have been evident had we used traditional methods instead of our refined model when estimating habitat availability. We interpret these findings in the context of stereotypical migratory behaviour observed in Newfoundland caribou, which occurs despite the extirpation of wolves Canis lupus nearly a century ago. We submit that caribou are able to balance food acquisition against predation risk using a complex set of factors involving both finer and broader scale selection. Accordingly, our study provides a strong argument for using refined habitat availability estimates when assessing food–predation trade‐offs.
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 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".