Moose response to high‐elevation forestry: Implications for apparent competition with endangered caribou
Bibliographic record
Abstract
ABSTRACT Habitat disturbance threatens woodland caribou (Rangifer tarandus caribou) by altering inter‐tropic relationships, which causes predation rates to increase. Of particular concern is the increase in early seral vegetation in high‐elevation caribou summer habitat created by the recent expansion of logging into these forests. Deep snow confines the relatively abundant moose (Alces alces) population to valleys during winter, but in summer they can move up slope, where their spatial overlap with caribou increases. Wolves (Canis lupus) follow moose, their primary prey, up slope and occasionally encounter and kill caribou. We tested the hypothesis that early seral vegetation in high‐elevation cutblocks (i.e., logged areas) attracts moose into mountain caribou summer habitat, and thereby increases the spatial overlap between caribou, moose, and wolves. To test our hypothesis, we examined how moose selection for early seral vegetation changed with elevation, how moose used undisturbed habitat, and how the proportion of early seral vegetation at high elevations in a moose home range was related to the amount of time moose spent at high elevations. Moose selection for cutblocks increased with elevation; however, when moose were at high elevations they spent the majority of their time in old‐growth forest where they were likely browsing on understory shrubs, and the area of high‐elevation cutblocks in moose home ranges did not affect the amount of time moose spent at high elevations. When we further explored the relationship between the amount of early seral vegetation at high elevations and the amount of time moose spent at high elevations, we found moose spent more time at high elevations when total early seral vegetation (from natural sources and cutblocks) increased, but there was little evidence that either type, on their own, influenced moose to use higher elevations. We conclude that although moose select cutblocks, the influence of high‐elevation cutblocks on moose was minor in our study. Our results and those of other studies suggests low‐elevation logging in moose winter ranges has led to an increased number of moose, and likely has a greater effect on moose distribution than logging at higher elevations. These insights can help guide management of apparent competition between moose and caribou. © 2017 The Wildlife Society.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".