Data from: Microhabitat selection by boreal woodland caribou improves access to food
Bibliographic record
Abstract
Abstract Bio-logging sensors attached to radiotelemetry receivers have great potential to transform our understanding of the ecological, physiological, and energetic constraints that shape patterns of wildlife movement under field conditions. We used video camera collars to assess microhabitat selectivity by woodland caribou (Rangifer tarandus) in boreal forests of Ontario, Canada. Our study areas provided a range of fine-grain microhabitats within forest stands (>10 ha), drawn from an unlogged forest landscape and a partially commercially-logged landscape. We classified ground-truthed samples within each stand into their sub-stand vegetation types at a scale of <10 m radius. We used a resource selection function to evaluate microhabitat selection by caribou, contrasting their selection of vegetation type by season, study area, calf presence, and behaviour, as identified from 17,384 videos from 19 caribou. Most caribou observations were in upland sites in all seasons and caribou showed seasonal selection among the vegetation types. All used sites were dominated either by black spruce (Picea mariana) or jack pine (Pinus banksiana) and all other vegetation types were seldomly used. The only favoured lowland vegetation type was poor lowland (low stocking, wet deep organic soils) and wetland types selected in summer. Caribou with calves did not select vegetation types differently from females without calves, nor did they avoid other caribou. Caribou selected for vegetation types with the greatest amount of lichen and other commonly used food types. While habitat selection at landscape scales is important to avoid predation, the videos enabled an understanding of within stand selection among closely-related forest types, indicating habitat selection occurred at a finer spatial scale for different reasons. This new knowledge is useful for identifying silvicultural practices needed to restore plant communities most strongly selected by caribou across a managed landscape, which would be expected to improve their energetic balance. The data file 'caribou_finegrain_data_RSF.csv' contains the data required to repeat the analysis on microhabitat selection by woodland caribou (Rangifer taranadus) in northern Ontario described in Thompson et al. (2026) by the same authors listed as creators. The 'animalID' column shows the unique animal identifier for each adult female caribou. The 'used' column indicates whether the point was used (1), or available (0). Used points were the vegetation categories identified from video collar recordings and available points were inferred from a combination of remotely sensed and ground collected data using random points within 90% minimum convex polygon seasonal home ranges. Vegetation type was determined by trained observers viewing the video files from video collars and were combinations of categories from a forest ecosystems classification system (Sims et al. 1989). Behaviour represents the primary behaviour displayed within videos for used points, and was randomly assigned to available points. Seasons were defined as winter and non-winter, primarily depending on snow cover, with a more specific definition provided in Thompson et al. (2026). Study areas are show as 'NK' for Nakina, Ontario, and 'PL' for Pickle Lake, Ontario, depending on where each animal was originally captured and fitted with a GPS video collar. References: Sims, R., W. D. Towill, K. A. Baldwin, and G. M Wickware. 1989. Field guide to the forest ecosystem classification for Northwestern Ontario. Forestry Canada and Ontario Ministry of Natural Resources, Forest Resource Development Agreement. Thunder Bay, Ontario. Thompson, I.D., P. A. Wiebe, A. R. Rodgers, D. Reid, J. A. Baker, B. R. Patterson, E. P. McNeill, R. Dejeante, and J. M. Fryxell. (in press). Microhabitat selection by boreal woodland caribou improves access to food. Wildlife Biology
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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 | 0.001 |
| 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) | 0.003 | 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".