Asynchrony in the timing of goose-vegetation interactions: implications for biogeochemical cycling in wet sedge tundra Tutakoke River, Yukon Delta NWR, Alaska, 2014-2016
Bibliographic record
Abstract
Data were collected from a three-year manipulative experiment investigating the interacting effects of changes in plant phenology and timing of goose grazing on subarctic graminoid grazing lawns in the Yukon Delta NWR, Alaska, USA. Terrestrial high-latitude ecosystems are experiencing dramatic increases in temperature, changes in precipitation, and advancement of the growing season with important implications for trophic interactions. Uncoupling of the temporal relationships between migratory animals and the phenology of the forage they rely on for energy, nutrition, and rearing of young is one of the most glaring consequences of these changes. Furthermore, herbivores in high latitudes are likely to mediate biogeochemical responses to climate change substantially altering ecosystem function. However, little is known about how changes in the synchrony of herbivore-vegetation interactions will influence biogeochemical cycles. This research was conducted in the Yukon-Kuskokwim (Y-K) Delta in western Alaska, where there is strong evidence that climate change is driving temporal decoupling of the evolved linkage between the phenology of plants and the timing of goose migration. There is an urgency to the research because: 1) some of the most rapid climate changes in the world are underway in Alaska, leaving migratory geese and the ecosystem processes they influence vulnerable to new conditions, and 2) with the rapid advancement of the growing season, migratory goose feeding ecology is lagging substantially, threatening an irreplaceable subsistence resource for Native communities. The overall research objective is to quantify how an advancing growing season and changes in the synchrony of vegetation-goose interactions alter the magnitudes and patterns of C and N cycling in the YK Delta. Our specific research questions addressed with an experiment were: 1) How does the timing of plant growth interact with goose arrival time to alter summer-long magnitudes of plant production, foliar chemistry, and N availability? and 2) How does the timing of plant growth interact with goose arrival time to alter summer-long magnitudes of net ecosystem CO2 exchange, gross ecosystem photosynthesis, and ecosystem respiration?
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".