The effects of different management interventions on degraded rangelands in Iceland
Bibliographic record
Abstract
Abstract Loss of vegetation and soil erosion are symptoms of widespread rangeland degradation across most of the Icelandic highlands. Areas at different stages of degradation coexist as a mosaic that includes both vegetated heathlands, and exposed gravelly deserts. Revegetation efforts have included fertilizer applications and grazing exclusion to increase plant biomass and reduce bare ground, but their effectiveness is predicted to differ depending on the stage of degradation for a certain area. In this study, we used a 4‐year field experiment to test the predictions of a state‐and‐transition model for the Icelandic highlands. We measured the combined effects of grazing exclusion and factorial applications of nitrogen (N), phosphorus (P), and potassium (K) fertilizers, on plant biomass, species richness, amount of exposed bare ground and plant community composition in a dwarf‐shrub heathland and a gravelly desert habitat. After 4 years: (1) grazing exclusion alone had no effect in either habitat; (2) fertilizers increased biomass in both habitats, especially in plots treated with NP or NK; (3) the combination of fertilizers and grazing exclusion produced the greatest amount of aboveground biomass, predominantly of forb and graminoid species. In the dwarf‐shrub heath, the increase in biomass in fertilized and fenced plots also corresponded to a loss in species richness, whereas in the gravelly desert, increased biomass reduced the amount of bare ground without reducing species richness. Our results reinforce the importance in understanding the effects of different management interventions across ecological conditions to determine the most effective revegetation approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".