Moose (Alces alces) behavioural and population ecology in the Revelstoke Valley, British Columbia
Bibliographic record
Abstract
We tested the hypothesis that: (1) moose reduction and stabilization harvest regimes implemented in the Revelstoke Valley, British Columbia, from 2003-2019 provided a greater cumulative number of moose harvested for people than if the status quo of mostly adult male moose harvesting regimes had persisted amidst background rates of moose habitat decline. Our first hypothesis included two more specific predictions and simulations: (1.2) changes in forest harvesting practices have led to a decrease in optimal seral habitat conditions, thereby reducing carrying capacity (K) for moose: (1.3) moose juvenile recruitment ratios increased amidst moose reduction and stabilization regimes that resulted in increases to per capita resource availability for moose, e.g., a density dependent response. To test these predictions, we first used resource selection functions (RSF) from mostly adult female moose GPS location data (46 individuals) collected between 2004-2019, environmental and anthropogenic data variables, and Generalized Additive Mixed Models (GAMMs) to understand the probability of use of cutblocks by moose as a function of years since cut. We used a previously estimated K of moose abundance in the study area and the results from our RSF GAMMs to forecast the annual K of moose from 2019-2040 under various simulated forest harvest scenarios. Next, we used provincial moose harvest statistics and results from the forecasted K calculations to forecast moose abundance and total moose harvested under simulated forestry and moose harvesting policies. Our RSF GAMM results showed that moose strongly selected middle aged (optimal) cutblocks (10-30 years since cut) and avoided both early seral (0-9 years since cut) and older cutblocks (>30 years since cut) across seasons. We found no effect of year on the availability of optimal aged cutblocks on moose home ranges — suggesting no behavioural changes occurred regarding moose habitat selection for cutblocks through high and low moose density years. We also found that had status quo moose harvest rates (~2.5% annual take, mostly adult males) continued until 2019, 33% less, or approximately 303 fewer moose, would have been harvested and consumed by moose harvesters.
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 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 | 0.001 |
| 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 | 0.000 |
| 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".