The Effects of Temperature, Flooding, and Goose Feces Addition on Greenhouse Gas Emissions and Ammonification in Four High Latitude Soils. Yukon Delta National Wildlife Refuge, Alaska, 2022.
Bibliographic record
Abstract
The large carbon (C) stock of wetlands is vulnerable to climate change, especially in high latitudes that are warming at a disproportional rate. Likewise, low-lying Arctic areas will experience increased coastal flooding under climate change and sea-level rise, which may alter goose herbivory and fecal deposition patterns if geese are pushed inland. While temperature, flooding, and feces impact soil C emissions, their interactive effects have been rarely studied. Here, we explore the impact of these interactions on carbon dioxide (CO2) and methane (CH4) emissions and nitrogen (N) mineralization (ammonification) in soils collected from four plant communities in the Yukon-Kuskokwim (Y-K) Delta, a high latitude coastal wetland in western Alaska. Communities included a Grazing Lawn, which is intensely grazed and susceptible to flooding, a Lowland Wetland and an Upland Wetland that experience moderate grazing and frequent (Lowland) and less frequent (Upland) flooding, and a rarely grazed and flooded Tundra community, located at the highest elevation. Soils were incubated for 16 weeks at 8 degrees Celsius (°C) or 18°C in microcosms and subjected to flooding and feces addition treatments with no-flood and no-feces controls. We quantified C emissions weekly and ammonification over the course of the experiment. While warming increased ammonification and C demand in the Lowland Wetland and always increased CO2 and CH4 emissions, interactions with flooding complicated warming impacts on C emissions in the Grazing Lawn and Tundra. In the Grazing Lawn, flooding increased CH4 emissions at 8°C and 18 °C, but in the Tundra, flooding suppressed CH4 emissions at 18°C. Flooding alone reduced CO2 emissions in the Upland Wetland. Feces addition increased CO2 emissions in all communities, but feces impacts on CH4 emissions and ammonification were minimal. When feces and flooding occurred together in the Lowland Wetland, CH4 emissions decreased compared to when feces was added without concomitant flood. Feces decreased the immobilization of ammonium and N demand in the Tundra only. Our results suggest that flooding could partially offset C loss from warming in less frequently flooded, higher elevation communities, but this offset could be negligible if flooding and warming drastically increase C loss in more flooded lowland areas.
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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 | 0.000 | 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) | 0.000 | 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".