Quantifying The Predator-Prey Relationship: Lessons Learned From A Multiple-Prey, Wolf-Hybrid Zone In Algonquin Park, Ontario, Canada
Bibliographic record
Abstract
We studied winter kill rates and prey selection in an eastern wolf/moose/white-tailed deer system in Algonquin Park, Ontario Canada. Eastern wolves (Canis lycaon) are a distinct species, known to hybridize with both gray wolves and eastern coyotes, resulting in genetic variation within the study area. Deer in Algonquin are seasonally migratory, and accessibility of deer shifts significantly over winter. Some wolf packs migrate off territory to forage on deer, while others remain on territory, relying on moose. Our objectives were to 1) identify factors influencing variation in prey use, and 2) compare methodologies for quantifying prey use in a multiple prey system. We used fine scale GPS collar data to identify kill sites, and calculated relative use of moose and deer for each pack using several measures, including prey biomass/wolf/day, days/kill/pack and a newly developed method of time spent at kill sites from GPS data. We also conducted stable isotope analysis to compare with field collected prey-use data. Variation in prey use among wolf packs was most influenced by accessibility to deer, vulnerability of moose, and genetic admixture, and mediated by winter progression. Methodological comparisons showed that prey biomass/wolf/day tended to overestimate large prey items, while days/kill/pack overestimated the importance of small prey. Stable isotope results were inconsistent, revealing some possible weaknesses of this approach. We found wide variation in kill rates and relative prey use with winter progression, and spatial variation in age-specific predation associated with differences in hunter harvest pressure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".