Interlinking hare and lynx dynamics using a century's worth of annual data
Bibliographic record
Abstract
Abstract The classic fur trade records on Canadian lynx (Lynx canadensis) have rarely been analysed in direct conjunction with data on its principal prey, the snowshoe hare (Lepus americanus). Comparable long‐term data for hare exist only for a region south of Hudson Bay. We fitted a bivariate log‐linear time‐series model to this hare and lynx data to disentangle the within‐ and between‐population interactions of these species. To reduce problems with fur returns being non‐normal and non‐linearly related to abundance, we transformed the fur returns to a normal distribution based on sample quantiles. The estimated effect on next year's lynx abundance of a 1% increase in current hare abundance was a 0.23% (SE = 0.05) increase in lynx. Conversely, a 1% increase in current lynx abundance corresponded to a 0.46% (SE = 0.12) decrease in next year's hare abundance. This contrasts with some earlier studies. However, these studies mixed hare data from south of Hudson Bay with lynx totals for all of Canada. Despite this asymmetry of interaction strengths, coefficients of determination were similar for hare versus lynx and lynx versus hare, because hare abundance varies more than lynx. Both species showed clear intraspecific density‐dependence of about equal strength. A 1% increase in current abundance increased next year's abundance by about 0.75%.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".