IMPACTS OF ENHANCED TEMPERATURE AND SNOW DEPOSITION ON SENESCENCE DATE, VEGETATION COVER, AND CO2 EXCHANGE IN A CANADIAN HIGH ARCTIC MESIC ECOSYSTEM
Bibliographic record
Abstract
Arctic regions are expected to experience an increase in both temperature and precipitation over the coming decades, which is likely to impact vegetation dynamics and greenhouse gas exchange. To test this response, an experiment was installed at the Cape Bounty Arctic Watershed Observatory, on Melville Island, NU, in 2008 as part of the International Tundra Experiment (ITEX). Snow fences and open top chambers (OTCs) were used to manipulate snow depth and air temperature, respectively. Unlike most ITEX sites to date, enhanced temperature and snowfall were combined here in a factorial design with eight replicates. As an added control, four plots were established well outside the enhanced snow area. Senescence date was recorded at the end of the season, and at the peak of the growing season a vegetation survey was conducted within each plot in order to determine the total percent cover of each plot, as well as the percent cover of individual species. Carbon dioxide (CO2) exchange was also measured within each plot throughout the growing season. \nThe date of senescence occurred significantly earlier in plots which had not been manipulated in any way, compared to all other treatments for all species. Salix arctica showed the greatest increase in cover over time at the species level. Lichen cover increased significantly in the deepened snow plots, and in general there were significant increases in percent cover in some functional groups over time. \nDuring June and into July the net CO2 flux was to the atmosphere. It was not until July 27 that these ecosystems became net carbon sinks. However, warming alone resulted in the ecosystem acting as a significant net carbon sink for the entire growing season. Plots exposed to warming alone were estimated to have removed approximately 19.94 g C m-2 from the atmosphere, whereas all other treatments were very similar to one another and estimated to have added approximately 3.12 g C m-2 to the atmosphere. Active layer depth and soil temperatures suggest that plots within the ambient snow zone may be receiving some additional snow due to their proximity to the fences. CO2 fluxes measured within the outer control plots suggest that the effect of warming alone could lead to this ecosystem being an even stronger net C sink under truly ambient snow conditions.
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 | 0.000 | 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 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".