Time vs. distance: Alternate metrics of animal resource selection provide opposing inference
Bibliographic record
Abstract
Abstract Quantifying resource use or selection by valued species on a human‐altered landscape is important for wise conservation action. Here, we contrast metrics of resource selection based on Global Positioning System ( GPS ) telemetry, which indexes time spent in various habitats, with tracking in snow, which measures distance travelled. When animals move at different speeds within different habitats, the two methods can produce different results. The study was conducted in winter on endangered mountain caribou ( Rangifer tarandus caribou ) in a landscape fragmented by forestry roads and cutblocks. From 2001 to 2009, 25 caribou were monitored with GPS collars obtaining locations every two to six hours, and compared to 63 caribou trails followed in snow in the same study area and time period. Logistic regression was used to estimate selection for both metrics, with the same definition of use and availability employed. Forest age, tree species, edge distances, and road densities were the covariates of interest. For most covariates, the two metrics produced similar results that agreed with the literature: selection for primary forest stands that represent forage patches. Both metrics indicated selection for habitat edges that potentially enhance foraging. The distance metric (snow trailing) indicated strong selection for forestry roads (42% more than available), whereas GPS locations suggested an avoidance of roads (33% less than available). The GPS analysis was in agreement with the vast majority of work published on woodland caribou, whereas the distance metric suggests some of the first evidence of selection for anthropogenic linear features. Our results highlight a potential bias against detecting selection for habitat features used for movement when using long fix intervals (>2 h) that typify most GPS studies. Avoidance of linear features should be carefully examined and designs considered to asses this bias. Finally, the selection of roads by caribou exacerbates an already desperate situation by creating a potential trap because roads are also preferred by some predators.
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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 | 0.022 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".