MOOSE POPULATION DYNAMICS DURING 20 YEARS OF DECLINING HARVEST IN BRITISH COLUMBIA
Bibliographic record
Abstract
Licenced harvest of moose (Alces alces) in British Columbia, Canada declined by approximately half over the 20-year period from 1996–2015. To better understand changes in moose populations coinciding with this period of declining harvest, we modelled population dynamics within 31 Game Management Zones (GMZs). We used aerial survey data (180 density and 159 composition surveys) combined with licensed harvest to develop 4 competing statistical models to assess population dynamics based on constant parameters and temporal trends in calf:cow ratios at 6 months, juvenile survival from 6–18 months, or cow survival. The models indicated that moose populations declined (λ < 1) in 7 GMZs (23%) from 1996–2005 and in 22 GMZs (71%) from 2006–2015. Over the 20-year period, the best model was fit with declining trends in calf:cow ratios in 8 GMZs, declining juvenile survival in 6 GMZs, and declining cow survival in 8 GMZs. Population growth rate was slightly reduced in those GMZs where licenced antlerless (cow and calf) hunting occurred but was not considered the primary factor causing population decline. Total licenced bull harvest influenced bull:cow ratios that were significantly lower in 2006–2015 (mean = 37:100) than 1995–2005 (mean = 48:100); significant predictive relationships existed between harvest rates and bull:cow ratios. Provincial moose numbers and harvest were highly correlated (r = 0.81) suggesting that declining harvest was a reaction to declining population trends. We found that the provincial moose population increased 6% from 1996–2005, subsequently declined 32% from 2006–2015, and declined 29% overall during the 20-year study period.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".