Density‐dependent responses of moose to hunting and landscape change
Bibliographic record
Abstract
Abstract In many areas of the boreal forests and temperate mountains of Canada, resource extraction activities have created forage conditions that are favourable to the growth of moose ( Alces alces ) populations. In turn, these increased moose populations buoy the abundance of wolves ( Canis lupus ), which then have negative impacts on caribou ( Rangifer tarandus ) populations. Consequently, caribou have been declining where increased resource extraction, moose, and wolves occur. To abate unsustainable predation pressure on caribou by wolves, the moose hunting quota was expanded for 17 years to reduce and then stabilize the moose population in the Revelstoke Valley, British Columbia, Canada. However, a reduction in forestry activity paired with habitat protections slowed the early seral conditions that favour moose. Consequently, both hunter‐caused mortality and habitat loss may have been contributing to observed moose declines that occurred during this period. Within this changing regulatory and biophysical landscape, we sought to address two research objectives. First, we evaluated how increasing the moose hunting quota influenced the total yield of harvested animals. We expected that density‐dependent responses by the moose population would bolster the number of harvestable animals on the landscape. Second, we tested how different forest harvest scenarios might influence moose habitat, wolf densities, and thus caribou population growth rates into future decades. We used data from moose GPS collars (39 individuals), eight aerial population surveys, hunter harvest statistics, estimates of carrying capacity thresholds, and forest harvest records. The latter data series spanned 1961–2020 and informed the resource selection function and calculations for our first research objective as well as the predictive modelling for our second research objective. Between 2003 and 2020, we found that the habitat amounts for moose declined by 44.8%. There were 42% more moose harvested under increased moose hunting quotas than were projected to be harvested under a simulated status quo quota. As the moose population declined and stabilized, we observed higher recruitment rates (e.g. calf:cow ratios) that further contributed to the number of harvested moose. Our simulations indicated that the only forest harvesting scenario where moose carrying capacity would be low enough to stabilize caribou population growth rates by 2040 was to cease forest harvesting entirely in 2020. Practical implication: an increased observed moose harvest quota mitigated the negative effects of forestry on caribou, aided in caribou recovery, and struck a balance that also provided food security and recreational opportunities for 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".