The tundra food web of Bylot Island in a changing climate and the role of exchanges between ecosystems
Bibliographic record
Abstract
It is increasingly recognized that ecosystems are not closed systems and that exchanges of resources across ecosystem boundaries can have repercussions on food webs, especially in low productivity systems such as the terrestrial Arctic. However, because these exchanges can take multiple forms, assessing their significance in the functioning of the tundra food web is difficult. In this paper, we first review some important concepts related to resource exchanges between ecosystems and examine their relevance to the study of trophic interactions in the arctic tundra. An analysis of the Bylot Island food web in the Canadian Arctic using a mass-balance trophic model suggests that predators are the dominant force controlling this food web. However, an important feature of this ecosystem is that several top predators benefit from allochthonous inputs, either through the presence of migratory birds during the summer or the use of the marine environment as a foraging ground in winter. We also show that migratory birds may act as autochthonous resource exporters for lower trophic levels, for instance by removing nitrogen from the nutrient pool when young produced locally migrate south and die away from the system. Although these resource exchanges may be a general feature of several arctic terrestrial ecosystems, their importance in the functioning of the tundra food web remains to be determined. Through long-term monitoring, we found that primary production in wetlands of Bylot Island increased by 85% over a 20-y period, likely a consequence of the warming trend observed in the area. However, we have not detected any changes at higher trophic levels, which is consistent with a top-down control of this food web. Given the importance of resource exchanges between ecosystems in the dynamics of the tundra food web, a full investigation of the effects of climate change will require a broader cross-ecosystem perspective.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".