Hierarchical habitat selection by North American porcupines in southern boreal forest
Bibliographic record
Abstract
In habitat-selection studies, a multi-scale approach is considered necessary to ensure that all elements of selection are depicted and that management decisions accurately reflect the needs of the species under study. We examined hierarchy in summer habitat selection in North American porcupines (Erethizon dorsatum (L., 1758)) in Eastern Canada at the scales of landscape, home range, and single tree. We used radiotelemetry to locate and observe animals visually to record their behaviour and exact location in the habitat. Den use in summer was unexpectedly high for some of our animals, which forced us to use a restricted number of locations per individual for comparison among scales. Although porcupines are generalists at the landscape level, selection patterns appear at the home-range and tree levels. Human-used land and conifer forests were least selected features of home ranges, while deciduous forests dominated by trembling aspen (Populus tremuloides Michx.) and mixed forests were most selected. At the tree scale, trembling aspen was found to be selected over other deciduous trees. However, fruit-producing trees were even more selected. This study shows the importance of a multi-scale approach that includes fine-scale selection.
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 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 | 0.000 | 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) | 0.001 | 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".