Importance of diel behaviour when studying habitat selection: examples from female Scandinavian brown bears (Ursus arctos)
Bibliographic record
Abstract
The goal of habitat selection studies is to identify important habitats for a particular species. However, most studies using radiotelemetry have focused on habitat-selection patterns using daytime positions only. We used 24 h data from six female brown bears ( Ursus arctos L., 1758) equipped with GPS–GSM collars and activity loggers to analyse variations in habitat selection related to diel variations in activity (foraging and resting). We found that the bears rested mainly during the daylight hours and foraged mainly during the crepuscular and nocturnal hours. The bears selected habitats differently when they were resting than when they were foraging. We found no selection for tall coniferous forest using all data, but this habitat was selected by resting bears and avoided by foraging bears. Thus, for studies of habitat selection, our results show the importance of obtaining data from all 24 h and dividing these data into relevant categories based on the diel activity pattern of the studied species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".