Tundra wildfire triggers sustained lateral nutrient loss in Alaskan Arctic
Bibliographic record
Abstract
Abstract Climate change is creating widespread ecosystem disturbance across the permafrost zone, including a rapid increase in the extent and severity of tundra wildfire. The expansion of this previously rare disturbance has unknown consequences for lateral nutrient flux from terrestrial to aquatic environments. Lateral loss of nutrients could reduce carbon uptake and slow recovery of already nutrient‐limited tundra ecosystems. To investigate the effects of tundra wildfire on lateral nutrient export, we analyzed water chemistry in and around the 10‐year‐old Anaktuvuk River fire scar in northern Alaska. We collected water samples from 21 burned and 21 unburned watersheds during snowmelt, at peak growing season, and after plant senescence in 2017 and 2018. After a decade of ecosystem recovery, aboveground biomass had recovered in burned watersheds, but overall carbon and nitrogen remained ~20% lower, and the active layer remained ~10% deeper. Despite lower organic matter stocks, dissolved organic nutrients were substantially elevated in burned watersheds, with higher flow‐weighted concentrations of organic carbon (25% higher), organic nitrogen (59% higher), organic phosphorus (65% higher), and organic sulfur (47% higher). Geochemical proxies indicated greater interaction with mineral soils in watersheds with surface subsidence, but optical analysis and isotopes suggested that recent plant growth, not mineral soil, was the main source of organic nutrients in burned watersheds. Burned and unburned watersheds had similar δ 15 N‐NO 3 − , indicating that exported nitrogen was of preburn origin (i.e., not recently fixed). Lateral nitrogen flux from burned watersheds was 2‐ to 10‐fold higher than rates of background nitrogen fixation and atmospheric deposition estimated in this area. These findings indicate that wildfire in Arctic tundra can destabilize nitrogen, phosphorus, and sulfur previously stored in permafrost via plant uptake and leaching. This plant‐mediated nutrient loss could exacerbate terrestrial nutrient limitation after disturbance or serve as an important nutrient release mechanism during succession.
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 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 | 0.000 |
| 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 | 0.000 | 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".