Goose herbivory effects on early-stage litter decomposition in coastal Alaskan wetlands
Bibliographic record
Abstract
Abstract Aims Herbivores create large differences in litter decomposition rates, but identifying how they do this can be difficult because they simultaneously influence both biotic and abiotic factors. In the Yukon-Kuskokwim (Y-K) River Delta in western Alaska, geese are dominant herbivores in wet-sedge meadows, where they create ‘grazing lawns’ that have nutrient-rich litter and an open habitat structure. To understand how geese affect decomposition, we tested the effects of litter quality and habitat type on litter decomposition over one year. Methods We performed a litter bag study in which we collected two litter types representing grazed and ungrazed vegetation conditions (high quality litter similar to grazed litter, and lower quality senesced, ungrazed litter), then incubated them in ‘grazing lawn’ and ungrazed meadows. Litter mass loss, carbon, nitrogen, cellulose and lignin content were measured after 3, 6, 9, and 52 weeks. We also monitored abiotic conditions (i.e., soil temperature, UV radiation, throughfall, and soil moisture content) in each habitat type. Results High-quality litter (lower lignin:N ratios) lost more mass than low-quality ungrazed litter over the whole study. However, at different times during the decomposition process, lower quality litter decomposed faster in grazed habitat, whereas higher quality litter decomposed faster in ungrazed habitat. This occurred despite abiotic conditions in grazed habitat that generally promote faster decomposition. Conclusion Results suggest that herbivore-induced increases in litter quality increase decomposition rates, and that the accumulation of the low-quality litter in ungrazed habitats is partly due to slow decomposition rates. While herbivores influence habitat conditions, the effects of habitat on decomposition differed across litter qualities, which suggests that other variables, such as differing microbial communities, play a role in decomposition processes. Graphical abstract
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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) | 0.001 | 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".