Physiographic Controls and Wildfire Effects on Aquatic Biogeochemistry in Tundra of the Yukon‐Kuskokwim Delta, Alaska
Bibliographic record
Abstract
Abstract Northern high‐latitude deltas are hotspots of biogeochemical processing, terrestrial‐aquatic connectivity, and, in Alaska's Yukon‐Kuskokwim Delta (YKD), tundra wildfire. Yet, wildfire effects on aquatic biogeochemistry remain understudied in northern delta regions, thus limiting a more comprehensive understanding of high latitude biogeochemical cycles. In this study, we assess wildfire impacts on summertime aquatic biogeochemistry in YKD tundra using a multi‐year (2015–2019) data set of water chemistry measurements (n = 406) from five aquatic environments: peat plateau ponds, fen ponds, fen channels, lakes, and streams. We aimed to (a) characterize variation in hydrochemistry among aquatic environments; (b) determine wildfire effects on hydrochemistry; and (c) assess post‐fire multi‐year patterns in hydrochemistry in lakes (lower terrestrial‐freshwater connectivity) and fen ponds (higher connectivity). Variation in hydrochemistry among environments was more strongly associated with watershed characteristics (e.g., terrestrial‐aquatic connectivity) than wildfire. However, certain hydrochemical constituents showed consistent wildfire effects. Decreases in dissolved organic carbon (DOC) and CO2, and increases in pH, specific conductance, NH4+, and NO3− indicate that, by combusting soil organic matter, wildfire reduces organics available for hydrologic transport and microbial respiration, and mobilizes nitrogen into freshwaters. Multi‐year post‐fire variation in specific conductance, DOC, and CO2 in lakes and fen ponds suggest that watershed characteristics underlie ecosystem response and recovery to wildfire in the YKD. Together, these results indicate that increasing tundra wildfire occurrence at northern high latitudes could drive multi‐year shifts toward stronger aquatic inorganic nutrient cycling, and that variation in terrain characteristics is likely to underlie wildfire effects on aquatic ecosystems across broader scales.
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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 | 0.000 |
| 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 | 0.000 |
| 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".