Range Selection by Semi-Domesticated Reindeer (<i>Rangifer tarandus tarandus</i>) in Relation to Infrastructure and Human Activity in the Boreal Forest Environment, Northern Finland
Bibliographic record
Abstract
During past decades, the amounts of infrastructure and human activity have increased in northern latitudes. Although the effects of human development on wild reindeer and caribou have been widely examined, its effects on semidomesticated reindeer and the reindeer herding environment are still poorly understood. We studied how seven different human activities (population centres, buildings, main roads, forest roads, snowmobile tracks, skiing trails, and gold digging areas) affect the range selection by semi-domesticated reindeer in northern Finnish Lapland using GPS tracking data on 29 female reindeer. Data were analyzed using compositional analysis on two spatial scales (home range selection and within-home-range selection) and in three seasonal periods (early winter, late winter, and summer-autumn). Results showed that during winter, reindeer strongly avoided almost all studied human activities when selecting home range areas (for forest roads, the direction of the effect was unclear), but in summer and autumn, only some of those activities were important. Within the selected home range areas, pasture use by reindeer appears to be less sensitive to infrastructure and human activity, probably because reindeer were able to avoid these anthropogenic disturbances at the upper level of habitat selection. The size of the potential cumulative area affected by infrastructure varied seasonally between 27.5% and 39.0% of the study area when calculated on the basis of home range selection, and between 7.2% and 20.3% when calculated from within-home-range selection. The strongest avoidance of infrastructure was found in late winter on both scales of range selection, but weakest avoidance was in early winter for home range selection and in summer for within-home-range selection. Cumulative impacts of different human activities on the usability value of reindeer ranges should be taken into account when planning new land-use operations in the areas important for the reindeer herding.
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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 | 0.000 |
| 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".