Foraging ecology of woodland caribou in boreal and montane ecosystems of northeastern British Columbia
Bibliographic record
Abstract
Woodland caribou (Rangifer tarandus caribou) are an iconic species of boreal and montane ecosystems, but many populations are declining due to habitat alteration and associated changes in predator-prey dynamics. Summer forage, however, influences lactation, juvenile growth, pregnancy, and survival, thereby affecting individuals and populations. I used tame caribou (of three nutritional classes — lactating, non-lactating, yearling) as a habitat assessment tool, at 135 sites across northeastern British Columbia, to determine: food habits and selection; dry matter intake rates; diet quality; and daily nutrient intakes. My goal was to assess the suitability of nutritional resources in boreal and montane plant communities to support energy and protein requirements of caribou during summer. Caribou were highly selective foragers. Deciduous shrubs were the primary summer forage of caribou; forbs, lichens, and mushrooms were secondary dietary items. Intake rates by caribou increased with increasing bite masses and quantities of accepted forage biomass (vegetation species used proportionately more than or equal to availability). Caribou achieved highest intakes at sites with an abundance of selected deciduous shrubs (e.g., willow-alpine sites, young forests) that afforded large bite masses, whereas lowest intakes occurred where mean bite masses were small (e.g., dry alpine, nutrient-poor forests). Dietary digestible energy (DE) and protein (DP) content, intake rates, and foraging time varied across plant communities and among nutritional classes. Caribou increased foraging time, but could not compensate for low intake rates and some plant communities failed to provide caribou with adequate nutrient intakes to support nutritional demands for lactation and maintenance of body mass. Although highest nutrient intakes were associated with productive sites, predation risk and disturbance may constrain the nutritional benefits caribou can acquire from these sites. In a pilot study, I mapped foodscapes of DE and DP intakes for a herd of free-ranging boreal caribou. Caribou did not select for nutrient intakes, but other factors including food quantity, predation risk, and accuracy of spatial data layers, may have confounded my ability to isolate the role of nutrition in habitat selection. Insights from this study into the nutritional ecology of caribou during summer can better inform caribou conservation and management.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".