The sheep in wolf's clothing? <scp>R</scp>ecognizing threats for land degradation in Iceland using state‐and‐transition models
Bibliographic record
Abstract
Abstract Land degradation and extensive soil erosion are serious environmental concerns in Iceland. Natural processes associated with a harsh climate and frequent volcanic activity have shaped Icelandic landscapes. However, following human settlement and the introduction of livestock in the ninth century, the extent of soil erosion rapidly escalated. Despite increased restoration and afforestation efforts and a considerable reduction in sheep numbers during the late 20th century, many Icelandic rangelands remain in poor condition. A deeper understanding of the ecology of these dynamic landscapes is needed, and state‐and‐transition models (STMs) can provide a useful conceptual framework. STMs have been developed for ecosystems worldwide to guide research, monitoring, and management but have been used at relatively small spatial scales and have not been extensively applied to high‐latitude rangelands. Integrating the best available knowledge, we develop STMs for rangelands in Iceland, where sheep grazing is often regarded as a main driver of degradation. We use STMs at a countrywide scale for 3 time periods with different historical human influence, from presettlement to present days. We also apply our general STM to a case‐study in the central highlands of Iceland to illustrate the potential application of these models at scales relevant to management. Our STMs identify the set of possible states, transitions and thresholds in these ecosystems, and their changes over time and suggest increasing complexity in recent times. This approach can help identify important knowledge gaps and inform management efforts and monitoring programmes, by identifying realistic and achievable conservation and restoration goals.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".