Experimental tests of water chemistry response to ornithological eutrophication: biological implications in Arctic freshwaters
Bibliographic record
Abstract
Many populations of Arctic-breeding geese have increased in abundance in recent decades, and in the Canadian Arctic, snow geese ( Chen caerulescens ) and Ross's geese ( Chen rossii ) are formally considered overabundant by wildlife managers. The impacts of these overabundant geese on terrestrial habitats are well documented, and, more recently, studies have suggested impacts on freshwater ecosystems as well. The direct contribution of nutrients from goose faeces to water chemistry could have cascading effects on biological functioning, through changes in phytoplankton biovolumes and community composition. We demonstrated previously that goose faeces can enrich ponds with nutrients at a landscape scale. Here, we show experimentally that goose droppings rapidly released nitrogen and phosphorus when submerged in freshwater, increasing the dissolved nitrogen and phosphorus in the water. This resulted in both a decrease in the nitrogen:phosphorus ratio and an increase in cyanobacteria in the goose dropping treatment. In contrast, this pattern was not found when we submerged cut sedge ( Carex sp.) leaves. These results demonstrate that geese act as bio-vectors, causing terrestrial nutrients to be bioavailable in freshwater systems. Collectively, the results demonstrate the direct ecological consequences of ornithological nutrient loading from hyper-abundant geese in Arctic freshwater ecosystems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".