Retrogressive thaw slumps in the Alaskan Low Arctic may influence tundra shrub growth more strongly than climate
Bibliographic record
Abstract
Abstract Thermokarst disturbance in permafrost landscapes is likely to increase across the tundra biome with climate warming, resulting in changes to topography, vegetation, and biogeochemical cycling. Tundra shrubs grow on permafrost, but shrub–thermokarst relationships are rarely studied in detail. Since the 1980s, Alaska's North Slope has experienced increased thermokarst activity, including retrogressive thaw slumps (RTSs) on hillslopes. Within decades, RTSs near Toolik Lake, Alaska, were colonized by tall (≥0.5 m) deciduous shrubs. We used dendrochronology methods on 66 shrubs (182 stem cross sections) representing dominant deciduous species: willows ( Salix pulchra and S. glauca ) and dwarf birch ( Betula nana ) at two RTS chronosequences on Alaska's North Slope comprising seven sites, to quantify thermokarst and climate effects (25 years of temperature and precipitation records) on shrub secondary growth (i.e., annual rings) in RTS‐disturbed and undisturbed moist acidic tussock (MAT) tundra. Across species, average growth ring widths were two times wider for shrubs in RTSs than in MAT, and ring widths decreased with RTS age. A 1°C June temperature increase was associated with 2% wider rings across species and sites, but shrubs showed marginal growth in warmer summers, supporting tundra‐wide shrub climate sensitivity studies. A 4.5% average ring width increase per 1 mm of previous year's September precipitation was seen in shrubs in mid‐successional RTSs, suggesting protective effects of early snowfall in RTSs versus open tundra. Retrogressive thaw slump age category explained 47% and 30% of average ring width variance of willows and dwarf birch, respectively, in linear mixed‐effects models. Climate variables explained 2% average ring width variance across species. Our results suggest that RTS exerts strong successional effects on tundra shrub growth. Climate effects appear to show weaker synoptic patterns across the study area. Retrogressive thaw slumps will likely contribute to tundra greening where RTS activity is increasing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 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".