Behaviour as an indicator of cyclic trends in abundance of Canada lynx (Lynx canadensis) and snowshoe hare (Lepus americanus)
Bibliographic record
Abstract
• Behaviour of individual animals is influenced by population density. • Measured behaviours and abundance of Canada lynx in relation to cyclic hare abundance. • Strong co-occurring indicators of the cyclic population dynamics of Canada lynx. • Insights into the mechanisms influencing population distribution and abundance. • Improve assessments of trends where behaviour and abundance can be monitored simultaneously. Changes in reproduction may be closely linked to population density and often manifest in various behaviours. Thus, variation in behaviours associated with reproduction can provide insights into the mechanisms influencing the distribution and abundance of populations as well as broader community dynamics. We used a combination of occurrence and behavioural data from camera traps to measure variation in the abundance of Canada lynx during two time periods with contrasting abundance of their primary prey, snowshoe hare. Our first objective was to determine if N -mixture models, camera occurrence rates, and behaviours could be used to monitor trends in cyclic abundance. Our second objective was to investigate the underlying environmental and ecological factors influencing lynx reproductive behaviours (cheek-rubbing, scent-marking, and grouping). We found that lynx behaviours and relative abundance were correlated among years and that those relationships varied with the abundance of snowshoe hare. Consistent with our predictions, years with greater abundance of lynx and hare were characterized by increases in cheek-rubbing, scent-marking, and grouping behaviours. Variation in behaviour and estimates of abundance proved to be strong co-occurring indicators of the cyclic population dynamics of Canada lynx. Behaviour can provide valuable insights into the mechanisms (i.e., prey availability, breeding activity) that influence population distributions and abundance.
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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 | 0.001 |
| 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) | 0.001 | 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".