Emerging priorities in terrestrial herbivory research in the Arctic
Bibliographic record
Abstract
Herbivores are an integral part of Arctic terrestrial ecosystems, driving ecosystem functioning and sustaining local livelihoods. In the context of accelerated climate warming and land use changes, understanding how herbivores contribute to the resilience of Arctic socio-ecological systems is essential to guide sound decision-making and mitigation strategies. While research on Arctic herbivory has a long tradition, recent literature syntheses highlight important geographical, taxonomic, and environmental knowledge gaps on the impacts of herbivores across the region. At the same time, climate change and limited resources impose an urgent need to prioritize research and management efforts. We conducted a horizon scan within the Arctic herbivory research community to identify emerging scientific and management priorities for the next decade. From 288 responses received from 85 participants in two online surveys and an in-person workshop, we identified 8 scientific and 8 management priorities centred on (a) understanding and integrating fundamental ecological processes across multiple scales from individual herbivore–plant interactions up to regional and decadal scale vegetation and animal population effects; (b) evaluating climate change feedbacks; and (c) developing new research methods. Our analysis provides a strategic framework for broad, inclusive, interdisciplinary collaborations to optimise terrestrial herbivory research and sustainable management practices in a rapidly changing Arctic.
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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 | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".