Changing northern vegetation conditions are influencing barren ground caribou (<i>Rangifer tarandus groenlandicus</i>) post‐calving movement rates
Bibliographic record
Abstract
Abstract Aim To quantify changes in vegetation productivity over the past three decades across five barren ground caribou ( Rangifer tarandus groenlandicus ) herd ranges and assess how these changes are influencing caribou movement rates. Location Northwest Territories and Nunavut, Canada. Methods As an indicator of vegetation productivity, the enhanced vegetation index ( EVI ) was calculated on newly developed cloud‐free, gap‐free, Landsat surface reflectance image composites representing 1984–2012. Changes in EVI were assessed on a pixel basis using Theil‐Sen's nonparametric regression and compared across herd ranges and land cover types using generalized least squares regression. Animal movement velocity was calculated from caribou telemetry data and generalized additive mixed models were used to link movement rates with vegetation productivity during the post‐calving phase of the year (July and August). Results Vegetation productivity increased across the five caribou herd ranges examined. The largest productivity increase occurred over the ranges of the most western herds, with the largest observed changes in grassland or shrub habitats. Caribou tended to move more slowly through tundra habitats with elevated levels of productivity to a point, while grasslands movement rates decreased linearly with increasing productivity. Movement velocities peaked at intermediate productivity levels in shrub habitats. Main conclusions Over the three decades of collected data, barren ground caribou habitats have become more productive, which is consistent with other studies that have documented increases in Arctic vegetation productivity. The more western herds, whose ranges are also closest to the Arctic Ocean, experienced the largest increases in productivity. Finally, we demonstrate that barren ground caribou movement patterns will likely change as a result of changing vegetation productivity in complex manners depending on herd, habitat type and the magnitude of change in vegetation productivity.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 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".