Effect of forest understorey stand density on woodland caribou (<i>Rangifer tarandus caribou</i>) habitat selection
Bibliographic record
Abstract
Woodland caribou ( Rangifer tarandus caribou (Gmelin, 1788)) use older forests that provide abundant terrestrial lichen forage and refuge from predators. However, forest structural characteristics vary widely, differing in forage availability but also, perhaps, in the ability of caribou to move freely to access forage or to escape predation. We conducted a multivariate analysis of habitat in two geographically and biophysically distinct regions to identify the independent effects of various attributes, including forest understorey stand density, defined as standing and downed biomass, on caribou habitat selection. We developed Bayesian network models to predict the probability of habitat selection based on a set of remotely sensed habitat inputs. Caribou in the Bistcho range (northwestern Alberta) selected non-forest/sparsely forested areas, while caribou in the Trout Lake region (northwestern Ontario) selected primarily forested habitats, nevertheless consistent with selection for reduced predation risk in both cases. Caribou also selected forest stands with lower understorey stand density in both regions, consistent with selection for stands that would allow greater ease of movement. The high-resolution satellite data resolved habitat characteristics more consistently and in greater detail than standard forest cover datasets that are most often used for these analyses, and led us to conclude that habitat management may require different treatments in different parts of the species’ range to address what are nevertheless common pathways to decline.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".