Ecological recovery in an Arctic delta following widespread saline incursion
Bibliographic record
Abstract
Arctic ecosystems are vulnerable to the combined effects of climate change and a range of other anthropogenic perturbations. Predicting the cumulative impact of these stressors requires an improved understanding of the factors affecting ecological resilience. In September of 1999, a severe storm surge in the Mackenzie Delta flooded alluvial surfaces up to 30 km inland from the coast with saline waters, driving environmental impacts unprecedented in the last millennium. In this study we combined field monitoring of permanent sampling plots with an analysis of the Landsat archive (1986-2011) to explore the factors affecting the recovery of ecosystems to this disturbance. Soil salinization following the 1999 storm caused the abrupt dieback of more than 30,000 ha of tundra vegetation. Vegetation cover and soil chemistry show that recovery is occurring, but the rate and spatial extent are strongly dependent on vegetation type, with graminoid- and upright shrub-dominated areas showing recovery after a decade, but dwarf shrub tundra exhibiting little to no recovery over this period. Our analyses suggest that recovery from salinization has been strongly influenced by vegetation type and the frequency of freshwater flooding following the storm. With increased ocean storm activity, rising sea levels, and reduced sea ice cover, Arctic coastal ecosystems will be more likely to experience similar disturbances in the future, highlighting the importance of combining field sampling with regional-scale remote sensing in efforts to detect, understand, and anticipate environmental change.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".