Glacial stream ecosystems and epilithic algal communities under a warming climate
Bibliographic record
Abstract
Climate change is accelerating the global loss of glaciers with potentially striking consequences for downstream ecosystems. However, there exists limited evidence of the ecological impacts of glacier loss in meltwater streams, particularly in those outside of North America and Europe. We provide a review of the abiotic conditions in glacial streams that are potential factors of their ecosystem function and biodiversity with an emphasis on their key primary producers, namely rock-attached algae or “epilithon”. Here, shrinking glaciers discharge over time less turbid melt waters, resulting in slower moving and more transparent stream conditions that are also warmer and more chemically dilute. We hypothesize that these environmental changes will stimulate epilithic algal growth while also shifting its community structure towards larger and less nutritious taxa. Although such an increase in algal growth may benefit the productive harvestable fish capacity of certain mountain streams, a potential negative trade-off involves the proliferation of nuisance algae (e.g., Didymosphenia geminata), which thrives under clear, nutrient-poor mountain conditions. We advocate the use of long-term ecological monitoring programs and experiments coordinated across global mountain ranges to better predict and understand the ecological consequences of loss of glaciers on mountain stream 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".