Reindeer carcasses modulate vegetation composition and greenness in High-Arctic tundra
Bibliographic record
Abstract
Vertebrate carrion is an integral part of foodwebs in ecosystems and can impact biodiversity at the local as well as the landscape scale. However, very little knowledge currently exists about the ecological role of carrion in the Arctic ecosystems. We conducted a ground survey on the cover of five plant functional groups at paired reindeer carcass and control sites and analysed the relationship between cover and carcass presence in the Arctic tundra of Svalbard. Vegetation indices from Red–Green–Blue (RGB) imagery captured by drones complemented this, assessing plant productivity in terms of “spectral greening” and modelling the relationship between vegetation index values and carcass distance. We show that graminoids capitalised most from carcass presence, whereas bryophytes and lichen showed decreases in cover. Woody plant and forb covers were not significantly impacted by carcass presence. The Red Green Blue Vegetation Index decreased locally at fresh carcasses (i.e., <1 year old) but showed an increase at more established carcass sites (i.e., >1 year). We show that carcasses have differential impacts on the plant functional groups of Svalbard's tundra and induce a local “green-up” through secondary succession within 2 m of the carcass. Given their non-random distribution, carcasses may contribute to vegetation heterogeneity at landscape scales. This is relevant for understanding how climate change-induced reindeer mortalities will impact tundra plant community composition in the future.
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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 | 0.000 | 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) | 0.000 | 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".