Calving rate, calf survival rate, and habitat selection of forest‐dwelling caribou in a highly managed landscape
Bibliographic record
Abstract
Abstract Logging negatively affects the threatened forest‐dwelling caribou (Rangifer tarandus caribou) through its positive effects on large predator populations. As recruitment is a key component of caribou population growth rate, we assessed calving rates of females and calf survival rates during the most critical period for calf survival, the calving period. We also identified causes of calf mortality and investigated the influence of predation risk, food availability, and human disturbance on habitat selection of females during the calving period at both the home‐range and forest stand scales. We hypothesized that caribou should display habitat selection patterns to reduce predation risk at both scales. Using telemetry, we followed 22 females and their calves from 2004 to 2007 in a highly managed study area in Québec, Canada. Most females (78.5 ± 0.05 [SE]) gave birth each year, but only 46.3 ± 8.0% of the calves survived during the first 50 days following birth, and 57.3 ± 14.9% of them died from black bear (Ursus americanus) predation. At the home‐range scale, caribou selected calving areas located at upper slope positions and avoided high road density areas. Surprisingly, they also selected the forested habitat type having the lowest lateral cover (mixed and deciduous stands) while avoiding the highest cover (regenerating conifer stands). At the forest stand scale, caribou selected areas located at relatively high elevations and with a lower basal area of black spruce trees. The selection of upper slope positions likely favored spatial segregation between calving females and wolves (Canis lupus) but not black bear. Our results suggest that calving females used areas from which they could visually detect approaching predators. While wolf avoidance appeared to be effective in a highly managed landscape, caribou did not appear to have adjusted their predator avoidance strategy to the recent increase in black bear abundance, who have benefited from increased food abundance. This situation requires focused attention from wildlife managers as logging activities are progressing towards the north within the core of forest‐dwelling caribou range. © 2011 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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".